Interpolation Method Research Articles

Study of a Numerical Integral Interpolation Method for Electromagnetic Transient Simulations

In the fixed time-step electromagnetic transient (EMT)-type program, an interpolation process is applied to deal with switching events. The interpolation method frequently reduces the algorithm’s accuracy when dealing with power electronics. In this study, we use the Butcher tableau to analyze the defects of linear interpolation. Then, based on the theories of Runge–Kutta integration, we propose two three-stage diagonally implicit Runge–Kutta (3S-DIRK) algorithms combined with the trapezoidal rule (TR) and backward Euler (BE), respectively, with TR-3S-DIRK and BE2-3S-DIRK for the interpolation and synchronization processes. The proposed numerical integral interpolation scheme has second-order accuracy and does not produce spurious oscillations due to the size change in the time step. The proposed method is compared with the critical damping adjustment method (CDA) and the trapezoidal method, showing that it does not produce spurious numerical oscillations or first-order errors.

Buckling analysis of functionally graded sandwich thin plates using a meshfree Hermite Radial Point Interpolation Method

Buckling analysis of functionally graded sandwich thin plates using a meshfree Hermite Radial Point Interpolation Method

Three-dimensional slope stability analysis based on irregular ellipsoid sliding surface

The determination of the critical sliding surface is a crucial aspect of slope stability analysis. In reality, the environment in which slopes are located is often very complex, and the sliding surfaces have different shapes. Therefore, a three-dimensional stability study is proposed, using irregular ellipsoidal shapes as the sliding surface. By constructing the equation of the irregular ellipsoid and applying spatial geometric transformations, an irregular ellipsoid controlled by six parameters, (semi-axis length (a), width (b), height (c), rotation angle (θ), and spatial displacements (tx and tz)), is obtained. The interpolation method is employed to apply the irregular ellipsoid to 3D slopes, thereby generating irregular ellipsoidal sliding surfaces. The slope stability coefficient is calculated by using the residual thrust method and genetic algorithm to determine the critical sliding surface. A comparative stability analysis is conducted between the standard ellipsoidal sliding surface and the irregular ellipsoidal sliding surface through a classic case study. The results show that the Type II irregular ellipsoidal sliding surface aligns well with the mining area. Finally, the research findings are applied to the 3D slope stability analysis of the Shengli West No.2 Open-Pit Coal Mine in China, validating the feasibility of the proposed method.

Topology Optimization Via Spatially-Varying TPMS.

Structural design with multi-family triply periodic minimal surfaces (TPMS) is a meaningful work that can combine the advantages of different types of TPMS. However, very few methods consider the influence of the blending of different TPMS on structural performance, and the manufacturability of final structure. Therefore, this work proposes a method to design manufacturable microstructures with topology optimization (TO) based on spatially-varying TPMS. In our method, different types of TPMS are simultaneously considered in the optimization to maximize the performance of designed microstructure. The geometric and mechanical properties of the unit cells generated with TPMS, that is minimal surface lattice cell (MSLC), are analyzed to obtain the performance of different types of TPMS. In the designed microstructure, MSLCs of different types are smoothly blended with an interpolation method. To analyze the influence of deformed MSLCs on the performance of the final structure, the blending blocks are introduced to describe the connection cases between different types of MSLCs. The mechanical properties of deformed MSLCs are analyzed and applied in TO process to reduce the influence of deformed MSLCs on the performance of final structure. The infill resolution of MSLC within a given design domain is determined according to the minimal printable wall thickness of MSLC and structural stiffness. Both numerical and physical experimental results demonstrate the effectiveness of the proposed method.

Prediction and analysis of relative error in electric vehicle charging stations based on an improved ConvFormer model

In order to address the issue of metering inaccuracies in charging stations that directly affect the development of electric vehicles, a prediction method for the relative error of charging stations based on the ConvFormer model is proposed. The model combines Convolutional Neural Networks (CNN) with Transformer models in parallel, significantly improving the prediction accuracy. First, charging station data is preprocessed using forward interpolation and normalization methods, and the dataset is transformed into a dataset of input relative errors. Then, a neural network with an improved unidirectional convolutional and attention combination for time-series forecasting is constructed, and common regression performance evaluation metrics, MAE (Mean Absolute Error) and MSE (Mean Squared Error), are selected for evaluation. Finally, based on seven days of charging station data, the relative error of charging stations for the next 24 h is predicted, and compared to traditional Transformer and LSTM (Long Short-Term Memory) time-series models. The results show that the improved model yields the lowest values for both MAE and MSE, with a 47.30 % reduction in MAE compared to the Transformer model and a 38.06 % reduction compared to LSTM, and a 66.94 % reduction in MSE compared to the Transformer model and approximately 62.32 % reduction compared to LSTM.

DAT-Net: Filling of missing temperature values of meteorological stations by data augmentation attention neural network

Abstract For a long time, filling in the missing temperature data from meteorological stations has been crucial for researchers in analyzing climate variation cases. In previous studies, people have attempted to solve this problem by using interpolation and deep learning methods. Through extensive case studies, it is observed that the data utilization rate of convolutional neural networks based on PConv is low at a high missing rate, which will result in the poor filling performance of each model at a high missing rate. To solve these problems, a Data Augmentation Attention Neural Network (DAT-Net) is presented. DAT Net uses encoder and decoder structures, which include a data augmentation training mechanism (DAM) to enhance model training. In addition, a time encoder (TED) has been developed to assist the model in learning the temporal dependencies of the data. To evaluate DAT-Net, 75% and 85% of experiments were performed, while comparisons were made with Linear, NLinear, DLinear, PatchTST, and GSTA-Net. The results showed that when the missing rate was 75%, DAT-Net decreased by 55.22%, 55.05%, 55.18%, 28.73%, and 12.35% on MAE and 54.08%, 53.88%, 54.08%, 35.48% and, 14.51% on RMSE, R 2 increased by 3.80%, 3,75%, 3.68%, 0.55%, and 0.27%, respectively.

GPS and GIS Based Soil Mapping of Rajkot City

Abstract Traditional (Classical) soil mapping is inefficient in illustrating continuous changes in soil types or forms. Before designing and constructing any construction, geotechnical engineers should be aware of changes in the index qualities of the soil. Sand, Gravel, Silt and Clay, Plastic Limit, and other soil index values may vary from location to location. This work aims to develop a system for mapping important soil index features using GPS and GIS in conjunction with previously published soil exploration data. This database will allow geotechnical engineers to run more searches on various combinations of soil properties. During this study, data from 112 boreholes in Rajkot was compiled into soil investigation reports by various geotechnical specialists. All 112 boreholes are included in a soil database. GPS has been used to figure out each location’s longitude, latitude & altitude for georeferencing. The ordinary kriging geospatial interpolation method was used for thematic map preparation. In this 10% of boreholes, data has been used for validation. Map accuracy is measured using the Mean Average Error (MAE) and the Root Mean Squared Error (RMSE). Where the difference in values were not more than 10% in all 12 cases. The value is only crossed in the plastic index case, where it is more than 10% in the 1-2-meter depth case.

Centrifugal failure mechanism analysis for ferrofluid seals based on a bidirectional coupled multi-physics model

In this paper, we propose a bidirectional coupled multi-physics (BCM) model to study the centrifugal failure mechanism of ferrofluid seals, employing a hybrid finite volume–finite element method. In this model, the evolution of the flow field is described by the two-phase incompressible Navier-Stokes (NS) equations with Kelvin force. The volume of fluid (VOF) method is used to capture the gas-ferrofluid interface. Magnetic scalar potential is introduced to solve for the magnetostatic field. We conducted secondary development on the OpenFOAM and Elmer software packages, which are based on the finite volume method (FVM) and finite element method (FEM), respectively, to calculate the flow field and magnetic field in ferrofluid seals. By developing interpolation methods between FEM and FVM grids based on the parallel bidirectional coupler EOF-Library[1], field information exchange between the flow field and magnetic field is achieved. The BCM model executes iterative computations on the flow and magnetic fields in an alternating way, continuously synchronizing the information of each, thus facilitating bidirectional coupling between the FVM and FEM frameworks. We employ the BCM model to investigate the failure mechanisms of ferrofluid seals under different conditions, including various load pressure and rotational velocity. The results indicate that the ferrofluid will leave the surface of the shaft along the pole teeth under the action of centrifugal forces, with the highest radial velocity occurring at the boundary of the liquid film. Under varying conditions of rotational velocity and pressure drop, the centrifugal failure modes of ferrofluid seals exhibit distinct differences. Moreover, two dimensionless numbers are proposed to quantitatively evaluate the effects of load pressure and centrifugal force, thereby augmenting the efficacy of seal design.

Comparative analysis of parametric B-spline and Hermite cubic spline based methods for accurate ECG signal modeling

Analyzing Electrocardiogram (ECG) signals is imperative for diagnosing cardiovascular diseases. However, evaluating ECG analysis techniques faces challenges due to noise and artifacts in actual signals. Machine learning for automatic diagnosis encounters data acquisition hurdles due to medical data privacy constraints. Addressing these issues, ECG modeling assumes a crucial role in biomedical and parametric spline-based methods have garnered significant attention for their ability to accurately represent the complex temporal dynamics of ECG signals. This study conducts a comparative analysis of two parametric spline-based methods—B-spline and Hermite cubic spline—for ECG modeling, aiming to identify the most effective approach for accurate and reliable ECG representation. The Hermite cubic spline serves as one of the most effective interpolation methods, while B-spline is an approximation method. The comparative analysis includes both qualitative and quantitative evaluations. Qualitative assessment involves visually inspecting the generated spline-based models, comparing their resemblance to the original ECG signals, and employing power spectrum analysis. Quantitative analysis incorporates metrics such as root mean square error (RMSE), Percentage Root Mean Square Difference (PRD) and cross correlation, offering a more objective measure of the model's performance. Preliminary results indicate promising capabilities for both spline-based methods in representing ECG signals. However, the analysis unveils specific strengths and weaknesses for each method. The B-spline method offers greater flexibility and smoothness, while the cubic spline method demonstrates superior waveform capturing abilities with the preservation of control points, a critical aspect in the medical field. Presented research provides valuable insights for researchers and practitioners in selecting the most appropriate method for their specific ECG modeling requirements. Adjustments to control points and parameterization enable the generation of diverse ECG waveforms, enhancing the versatility of this modeling technique. This approach has the potential to extend its utility to other medical signals, presenting a promising avenue for advancing biomedical research.

A gap filling method for daily evapotranspiration of global flux data sets based on deep learning

In response to irregular data gaps in evapotranspiration (ET) data obtained using eddy covariance (EC) methods, this study seeks to explore a high-precision interpolation method that is not constrained by spatiotemporal limitations. Leveraging daily observations from 241 global flux towers (FLUXNET ET) as a reference, data gaps are artificially introduced at five different proportions: 5 %, 10 %, 20 %, 35 %, and 50 %, this process is repeated 30 times (Take the average of the results). Four ET products, along with environmental factors (ERA5 meteorological data and NOAA Normalized Difference Vegetation Index), are employed as input to assess the performance of the target model, Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM), as well as five comparative models: CNN, LSTM, Random Forest (RF), Support Vector Machine (SVM), and Extreme Learning Machine (ELM), in filling the data gaps. These results are then compared with the filling outcomes obtained without ET products as input, aiming to investigate whether ET products contributes to improving the accuracy and stability of ET gap filling. The results indicate that, firstly, the filling performance of the target model CNN-LSTM surpasses that of other models, with minimal impact from gap proportions. Moreover, incorporating ET products as input alongside environmental factors significantly enhances the filling accuracy and stability of the model. Compared to scenarios without ET products as input, the CNN-LSTM model shows an average increase in R2 of 5 % to 15 % and a decrease in root mean square error (RMSE) of 15 % to 35 %. Specifically, as the gap proportion increases from 5 % to 50 %, the average Pearson correlation coefficient (R2) decreases from 0.952 to 0.846 (a decline of 11 %), the average RMSE increases from 0.491 mm/d to 0.620 mm/d (a rise of 26 %), and reached the filling limit at the gap ratio of 93 % (R2 below 0.4). Conversely, without ET products as input, as the gap proportion increases, the average R2 decreases from 0.916 to 0.784 (a decline of 14 %), the RMSE increases from 0.552 mm/d to 0.952 mm/d (a rise of 72 %), and reached the filling limit at the gap ratio of 80 %. Secondly, the contribution of ET products to the model estimation far exceeds that of ERA5 meteorological factors and NDVI, with the contribution of ET products increasing with higher gap proportions from 58.0 % to 73.6 %. Finally, different latitudes, land cover types, and climate types influence the model filling performance, with better performance observed in high-latitude regions, Grassland (GRA), and Dry-Cold climates. In summary, incorporating ET products as input for CNN-LSTM yields high-precision gap-filling results, surpassing existing typical methods for gap filling and its application is not constrained by spatiotemporal limitations.

Theoretical investigation into potential thermoelectric material Monolayer InI with direct bandgap and ultralow lattice thermal conductivity

Monolayer metal iodides are garnering widespread attention due to their ultralow lattice thermal conductivity and their potential applications in the field of thermoelectrics. Here, we utilized first-principles calculations and Boltzmann transport theory to thoroughly investigate the thermoelectric behavior of monolayer InI. Results indicate that this material functions as a direct bandgap semiconductor and features an exceptionally low lattice thermal conductivity of 0.27 W m−1K−1 at 300 K, a result of its low group velocities and short phonon lifetimes. Furthermore, transport coefficients were calculated with enhanced precision through the Wannier interpolation method, incorporating the HSE06 hybrid functional and spin-orbit coupling (SOC) effects. Remarkably, the maximum thermoelectric figure of merit (ZT) values for monolayer InI demonstrate substantial growth with increased temperatures, surging from 0.31 at 300 K to 1.20 at 450 K for p-type, and escalating from 0.26 at 300 K to 0.72 at 450 K for n-type. These promising low-temperature thermoelectric properties exhibited by monolayer InI suggest its suitability for integration into the production of thermoelectric devices.

Improving urban waste management: A comprehensive study on household waste generation and spatial patterns in the Grand Guayaquil Metropolitan Area.

This study tackles the growing global concern about municipal waste management, particularly in cities like the Grand Guayaquil Metropolitan Area (GGA). Through realistic field studies on in situ household waste generation and geographic information system (GIS) tools, this work offers a framework to predict the quantities and types of recyclable household waste for any metropolitan area in Latin America. Over 4 weeks, students collected, sorted and weighed recyclable waste types, including plastic, paper, metal, glass and fabric, from 776 sampled household of the GGA. ArcGIS survey tool identified household locations and allowed to survey different socio-demographic features. With the help of ArcGIS interpolation method, the total household waste generation for GGA was predicted, and the classification of the different types of recyclable waste was also spatially distributed for the study area. The report identified notable trends in plastic waste, specifically polyethylene terephthalate waste's steady prevalence and 42% growth rate, emphasizing the importance of enhanced recycling techniques. Spatial density maps showed a heterogeneous waste distribution across the GGA, emphasizing locations with higher waste output. This study demonstrates that improving recyclable waste collection can be accomplished with a moderately cheap expenditure by collaborating with academia to overcome knowledge gaps. This strategy provides opportunities to mitigate the environmental impacts of poor waste management.

Evaluation of the Spatial Pattern of Fitness and Leisure Venues at the City Scale

Reasonable layout of fitness and leisure places is an important part of the implementation of the national fitness strategy. The analysis of the spatial pattern and influence mechanism of fitness and leisure places from the perspective of city has certain guiding significance for optimizing urban planning and construction and rationally allocating sports resources. This paper uses kernel density, buffer zone, inverse distance interpolation, Kriging interpolation, geographical detector and other geographical spatial analysis methods to analyze the spatial distribution pattern and distribution causes of five types of fitness and leisure places in counties (cities, districts) under the jurisdiction of Jinhua City. It is found that the overall spatial distribution characteristics of fitness and leisure places in Jinhua City are ' core-periphery ', and different forms of fitness and leisure places show different distribution characteristics. The spatial pattern of fitness and leisure places in Jinhua City has the characteristics of relying on traffic arteries and high-density population areas, and has a positive correlation with the spatial layout of housing prices. Jinhua fitness and leisure places generally have good traffic accessibility.

Nonlinear Dynamic Analysis of High-Strength Concrete Bridges under Post-Fire Earthquakes Considering Hydrodynamic Effects

This study employed the linear interpolation method to ascertain the curve relationship between the elastic modulus and stress of high-strength concrete C60 with temperature, and the nonlinear dynamic analysis of high-strength concrete bridge structures subjected to post-fire earthquake action at varying water levels was subsequently evaluated. It was established that both the hydrodynamic effects and the temperature effects have a considerable impact on the structural dynamic response of bridges. The presence of water has been observed to increase the dynamic response of pier structures. At water levels of 0 m and 10 m, the temperature effect results in a reduction in the fundamental frequencies of acceleration and displacement responses by 73.68% and a decrease in the fundamental frequency of stress responses by 83.33%. At a water level of 20 m, the fundamental frequencies of the acceleration, displacement, and stress responses decrease by 53.49%. In consideration of the acceleration and displacement at the pier top and stress at the pier base at a water depth of 10 m, the superposition of temperature effects and hydrodynamic effects results in an increase of 59.06% in acceleration, 25.93% in displacement, and 49.53% in stress than combination effects, respectively. At a water depth of 20 m, the superposition of temperature and hydrodynamic effects results in an increase of 92.82%, 100%, and 127.85% in acceleration, displacement, and stress, respectively. The combined effects of hydrodynamic and temperature effects cannot be described merely as a linear superposition of the two single actions. The research findings provide a significant theoretical basis for understanding the impact of multiple disasters, such as fires and earthquakes, on bridge structures.

GIS-based Visualization of Elemental Distribution in Neoboletus Luridiformis Fruiting Body.

The fruiting body of Neoboletus luridiformis (Scarletina bolete) mushroom was used to determine the level of bioconcentration and subsequent distribution of seventeen elements (Ag, Al, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Se, Sr, and Zn). A two-centimeter-thick vertical section of the entire fruit body was divided into 101 partial sub-samples where the contents of the studied elements were determined using ICP OES. The actual distribution of the elements in the fruiting body profile was visualized using a GIS interpolation method resulting in distribution maps. The study provides valuable insights into the distribution patterns of 17 elements within the fruiting body of N. luridiformis. Based on the visualization of the elemental content, the determined elements can be divided into three categories. Elements accumulated primarily (i) in the cap (Al, Ag, Ca, Cd, Cu, Fe, K, Mg, Ni, and Zn), (ii) in the stipe (Ba, Mn, Na, Pb, and Se), and (iii) elements with non-specific distribution (Cr and Sr). Since such detailed information supported by graphical visualization has not been published to date, the information in this study will help to better understand the accumulation and distribution of elements within the fruiting bodies of wild as well as cultivated mushroom species.

Evaluation of a decomposition-based interpolation method for fourth-order fiber-orientation tensors: An eigensystem approach

We propose and assess a new decomposition-based interpolation method on fourth-order fiber-orientation tensors. This method can be used to change the resolution of discretized fields of fiber-orientation tensors, e.g., obtained from flow simulations or computer tomography, which are common in the context of short- and long-fiber–reinforced composites. The proposed interpolation method separates information on structure and orientation using a parametrization which is based on tensor components and a unique eigensystem. To identify this unique eigensystem of a given fourth-order fiber-orientation tensor in the absence of material symmetry, we propose a sign convention on tensor coefficients. We explicitly discuss challenges associated with material symmetries, e.g., non-distinct eigenvalues of the second-order fiber-orientation tensor and propose algorithms to obtain a unique set of parameters combined with a minimal number of eigensystems of a given fourth-order fiber-orientation tensor. As a side product, we specify for the first time, parametrizations and admissible parameter ranges of cubic, tetragonal, and trigonal fiber-orientation tensors. Visualizations in terms of truncated Fourier series, quartic plots, and tensor glyphs are compared.

Research on Molten Iron Quality Prediction Based on Machine Learning

The quality of molten iron not only has a significant impact on the strength, toughness, smelting cost and service life of cast iron but also directly affects the satisfaction of users. The establishment of timely and accurate blast furnace molten iron quality prediction models is of great significance for the improvement of the production efficiency of blast furnace. In this paper, Si, S and P content in molten iron is taken as the important index to measure the quality of molten iron, and the 989 sets of production data from a No.1 blast furnace from August to October 2020 are selected as the experimental data source, predicting the quality of molten iron by the I-GWO-CNN-BiLSTM model. First of all, on the basis of the traditional data processing method, the missing data values are classified into correlation data, temporal data, periodic data and manual input data, and random forest, the Lagrangian interpolation method, the KNN algorithm and the SVD algorithm are used to complete them, so as to obtain a more practical data set. Secondly, CNN and BiLSTM models are integrated and I-GWO optimized hyperparameters are used to form the I-GWO-CNN-BiLSTM model, which is used to predict Si, S and P content in molten iron. Then, it is concluded that using the I-GWO-CNN-BiLSTM model to predict the molten iron quality can obtain high prediction accuracy, which can provide data support for the regulation of blast furnace parameters. Finally, the MCMC algorithm is used to analyze the influence of the input variables on the Si, S and P content in molten iron, which helps the steel staff control the quality of molten iron in a timely manner, which is conducive to the smooth running of blast furnace production.

The cell-centered positivity-preserving finite volume scheme for 3D convection–diffusion equation on distorted meshes

PurposeThis paper aims to construct positivity-preserving finite volume schemes for the three-dimensional convection–diffusion equation that are applicable to arbitrary polyhedral grids.Design/methodology/approachThe cell vertices are used to define the auxiliary unknowns, and the primary unknowns are defined at cell centers. The diffusion flux is discretized by the classical nonlinear two-point flux approximation. To ensure the fully discrete scheme has positivity-preserving property, an improved discretization method for the convection flux was presented. Besides, a new positivity-preserving vertex interpolation method is derived from the linear reconstruction in the discretization of convection flux. Moreover, the Picard iteration method may have slow convergence in solving the nonlinear system. Thus, the Anderson acceleration of Picard iteration method is used to solve the nonlinear system. A condition number monitor of matrix is employed in the Anderson acceleration method to achieve better robustness.FindingsThe new scheme is applicable to arbitrary polyhedral grids and has a second-order accuracy. The results of numerical experiments also confirm the positivity-preserving of the discretization scheme.Originality/value1. This article presents a new positivity-preserving finite volume scheme for the 3D convection–diffusion equation. 2. The new discretization scheme of convection flux is constructed. 3. A new second-order interpolation algorithm is given to eliminate the auxiliary unknowns in flux expressions. 4. An improved Anderson acceleration method is applied to accelerate the convergence of Picard iterations. 5. This scheme can solve the convection–diffusion equation on the distorted meshes with second-order accuracy.

Exploration of the behavior and evolution features of beam-column joints with T-stub using the structural strain energy method

Based on the structural strain energy method, the work behavior of four T-stub connection joints under cyclic loading is analyzed to visualize their working behavior and failure evolution. The experimental strain data are modeled as generalized strain energy density (GSED) to characterize the structural stress state and the corresponding characteristic parameters, and the Mann-Kendall (M-K) criterion is formulated to determine the performance characteristic points (P and Q) of the joint Mj-Ej curves and to derive the starting point of structural continuity damage. Then, by analyzing the parameters of the joint working state (strain, deformation, joint energy dissipation, connection stiffness, bolt force and pry force), the trends of the joint stressing state curves before and after the critical loading are reflected. Finally, the feature point data interpolation (EDI) method is introduced to effectively expand the joint test data, visualize the change process of the strain/stress field, and deeply reveal the mechanical response mechanism and evolutionary degradation law behind it. The contribution of the above work is to quantitatively identify the P and Q characteristic points in the changing trend of the joint working state, to reveal the mechanical mechanism and the failure degradation law from the perspective of energy evolution, and to visualize the mutation of the field data before and after the P/Q using the EDI method, which is a generalized method that can be applied to the other progressive failure structures.

Accelerating multipool CEST MRI of Parkinson's disease using deep learning-based Z-spectral compressed sensing.

To develop a deep learning-based approach to reduce the scan time of multipool CEST MRI for Parkinson's disease (PD) while maintaining sufficient prediction accuracy. A deep learning approach based on a modified one-dimensional U-Net, termed Z-spectral compressed sensing (CS), was proposed to recover dense Z-spectra from sparse ones. The neural network was trained using simulated Z-spectra generated by the Bloch equation with various parameter settings. Its feasibility and effectiveness were validated through numerical simulations and in vivo rat brain experiments, compared with commonly used linear, pchip, and Lorentzian interpolation methods. The proposed method was applied to detect metabolism-related changes in the 6-hydroxydopamine PD model with multipool CEST MRI, including APT, CEST@2 ppm, nuclear Overhauser enhancement, direct saturation, and magnetization transfer, and the prediction performance was evaluated by area under the curve. The numerical simulations and in vivo rat-brain experiments demonstrated that the proposed method could yield superior fidelity in retrieving dense Z-spectra compared with existing methods. Significant differences were observed in APT, CEST@2 ppm, nuclear Overhauser enhancement, and direct saturation between the striatum regions of wild-type and PD models, whereas magnetization transfer exhibited no significant difference. Receiver operating characteristic analysis demonstrated that multipool CEST achieved better predictive performance compared with individual pools. Combined with Z-spectral CS, the scan time of multipool CEST MRI can be reduced to 33% without distinctly compromising prediction accuracy. The integration of Z-spectral CS with multipool CEST MRI can enhance the prediction accuracy of PD and maintain the scan time within a reasonable range.