Basis Function Model Research Articles

Mapping vertical distribution of SOC and TN in reclaimed mine soils using point and imaging spectroscopy

Soil organic carbon (SOC) and total nitrogen (TN) contents in different soil horizons are essential for vegetation growth and crucial indicators to evaluate soil quality in reclaimed mining areas. Compared with conventional wet chemistry methods, soil spectroscopy, including imaging spectroscopy, can be used as a cost and time-efficient soil analysis technique. However, there is a great challenge in combining laboratory point spectra and laboratory hyperspectral imagery for mapping vertical distribution of SOC and TN (0–100 cm) in reclaimed soils. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The main objective of this study is to provide a generic workflow to efficiently evaluate and map reclaimed mine soils in different horizons using imaging spectroscopy and machine learning approaches. A total of 65 soil samples (0–100 cm) were collected from three reclaimed mining lands and one natural site in northern China. Both point soil spectral information and hyperspectral images (350–2500 nm) were obtained under laboratory condition. In order to enhance the relationship between soil quality indicators and spectral features, the stacked feature selection algorithms and three-bands spectral indices were proposed for further modelling. Three machine learning methods (partial least squares regression; PLSR, random forest; RF, and radial basis function model; RBF) based on the point spectra were applied to calibrate and map continuous vertical distribution of SOC and TN. According to the results, thirty spectral bands were identified as important spectral features for SOC and eighteen bands for TN. With feature spectral bands and optimized three-bands spectral indices, the RF model yielded the best predictions for both SOC (R2 = 0.97, RMSE = 7.5 g kg−1) and TN (R2 = 0.78, RMSE = 0.33 g kg−1). It was concluded that imaging spectroscopy can be used to quantify and map soil quality indicators for better monitoring ecological restoration process in reclaimed soil of mining site.

A two-stage model updating method for the linear parts of structures with local nonlinearities

Finite element model updating provides an important supplement for finite element modelling. However, some studies have shown that if the tested structure involves local nonlinearities due to damages, material properties and large deformation et al., it is difficult to achieve an accurate modified model using conventional model updating methods that are based on the assumption of linear structures. To address this issue, a two-stage model updating method separating the effects of local nonlinearities is proposed in this paper. Firstly, the underlying linear frequency response function is obtained by using the conditioned reverse path method. Then, combined with the Sherman-Morrison-Woodbury formula and the model updating objective function established by the frequency response function similarity metric, then structural model updating and damage detection are carried out as the second stage. Three numerical examples are given to illustrate the effectiveness of the proposed method. This method can not only accurately identify the location and quantify the extent of structural damages, but also has the advantages of not based on sensitivity, not depending on the selection of frequency points, not repeatedly calling the initial model et al. The proposed method has high computational efficiency and avoids the numerical problems often encountered by conventional frequency response function-based model updating methods.

Multiple-input multiple-output Radial Basis Function Neural Network modeling and model predictive control of a biomass boiler

This study presents a model predictive control of a 4 × 3 Multiple-Input Multiple-Output (MIMO) biomass control system that uses radial basis function (RBF) as an activation function to enhance the control performance. The biomass boiler model is developed from the system identification box in MATLAB by collecting measured input and output data from the 31.5 MW Wonji sugar mill and analyzing the data using black box modeling approach. The major goal of this study is to improve the MIMO control performance of a biomass boiler by utilizing Radial Basis Function Neural Network (RBFNN) to increase model accuracy which ultimately enhances the overall control system. Biomass boilers have four inputs and three outputs. The inputs are airflow 1, airflow 2, water flow and stocker speed (the speed of the motor which feeds bagasse or sugarcane pulp) and outputs are temperature, pressure and drum level. The RBFNN model was programmed using MATLAB R2021a software with system identification toolbox. Shorter settling times of 0.286 s, 1.873 s, and 0.637 s, as well as tolerable overshoots of 0.505%, 1.5%, and 15.698% for temperature, pressure, and level respectively, were achieved with the suggested model predictive controller using RBFNN model. However, when utilizing the identical controller and state space model, the settling times are 2.318 s, 5.461 s, and 6.147 s, with overshoots of 4.737%, 8.152%, and 38.194% for the three variables respectively. The suggested method was compared to the model predictive control (MPC) biomass boiler state space model, which can be considered as a model without RBFNN. The simulation results indicate that the RBFNN-based MPC performed better than the state space model-based MPC. This is because, the state space model is always constant, in contrast to the neural network's active monitoring of boiler dynamics.

A multi-surrogate multi-tasking genetic algorithm with an adaptive training sample selection strategy for expensive optimization problems

Multi-surrogate assisted evolutionary algorithms, utilizing inexpensive global and local surrogate models to assist evolutionary search, have demonstrated a remarkable ability to solve expensive optimization problems (EOPs). The selected training samples of the local surrogate model used in the current algorithms may not be updated continuously, which could bring about a waste of computing resources. This paper proposes a multi-surrogate multi-tasking genetic algorithm with an adaptive training sample selection strategy (MS-MTGAwA), in which the local surrogate model is updated adaptively based on information from established local models to enhance the capability in exploiting the optimal solution. The adaptive training sample selection strategy (ATS) applies optimal points found in historical local models as training samples. In addition, MS-MTGAwA inherits the optimization framework of a multi-tasking genetic algorithm. The radial basis function model is chosen as the modeling basis of the global and local surrogate models. A set of common benchmark functions whose dimensions vary from 10 to 100 and the tension/compression spring design problem are adopted to validate the performance of the proposed algorithm. Based on the average best fitness value over thirty independent runs on the benchmark functions, MS-MTGAwA ranks first in Friedman’s test compared to five state-of-the-art algorithms including S-JADE, GORS-SSLPSO, SAHO, SAMSO, and MS-MTO. The proposed ATS can be expected to be applied in any algorithms with local surrogate to make full use of historical information of the local model to improve the prediction accuracy of the next local model.If this paper is accepted, MATLAB codes associated with this paper will be uploaded tohttps://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE.

Eligibility of village fund direct cash assistance recipients using artificial neural network

<p>Bantuan Langsung Tunai Dana Desa (BLT-DD), or known as Village Fund Direct Cash Assistance is assistance from the Indonesian government which causes problems and conflicts in the community when the assistance is not on target. The classification algorithm is proven to use in determining BLT-DD recipients. In this study, the radial basis function (RBF) and elman recurrent neural network (ERNN) models compare to classify the eligibility of BLTDD recipients. In the experiment, the optimal performance of the RBF and ERNN compare in determining the eligibility of BLT-DD recipients. Also, it’s compared with the classification algorithm that implements the same data, namely BLT-DD data for Kubu Raya District. The experimental results show the effectiveness of the RBF model in recognizing test data, while the ERNN model is effective in identifying test data. The RBF and ERNN models can achieve the same total accuracy of 98.10%.</p>

Structural optimization design of BIW using NSGA-III and entropy weighted TOPSIS methods

Multi-objective optimization is always a difficult and hot problem for the static and dynamic performance and lightweight optimization design of a body-in-white (BIW). In this regard, a hybrid method coupled with NSGA-III and entropy weighted TOPSIS methods was put forward here. Effects of each design variable on the BIW performance was studied by means of the contribution method so as to optimize various design variables. Further, an experimental design was carried out by means of a radial basis function model to approximate each performance index of the automotive body. On the base of that, a comprehensive method where the Pareto frontier is searched by means of NSGA-III while the optimal solution is selected by means of the entropy weighted TOPSIS method was proposed based on lightweight, static and dynamic performances to analyze BIW performances to determine the contradictory optimal solution among various BIW factors. Afterward, our numerical verifications demonstrate that the BIW performances are improved significantly so that it can be feasible to optimize static and dynamic BIW performances and the lightweight performance of the BIW can be guaranteed accordingly.

Approximating robust Pareto fronts by the MEOF-based multiobjective evolutionary algorithm with two-level surrogate models

The multiobjective optimization problems (MOPs) under uncertain environments are very challenging to be solved due to the sensitivities of some robust decision variables. To find the robust Pareto fronts (PFs) of these MOPs, the mean effective objective function (MEOF) is often used for evaluating the qualities of solutions in the existing evolutionary multiobjective optimization (EMO) algorithms. In the MEOF evaluation, the objective function values of multiple solutions in the neighborhood of a certain solution should be averaged. As a result, the MEOF-based EMO algorithms consume a large number of function evaluations to find robust PFs with high qualities. To overcome this weakness, we propose a new MEOF-based EMO framework with two-level surrogate models, denoted by EMO-MEOF/TS, which utilizes radial basis function and Gaussian process model to predict high-quality robust solutions at the levels of global search and local search. Some experiments are conducted to evaluate the performance of the proposed framework on some modified MOPs with robust decision variables. Our experimental results demonstrate that EMO-MEOF/TS is advantageous against several robust MOEAs in approximating the PFs of MOPs with robust characteristics.

The multi-objective optimisation design of outlet guide vanes of diagonal flow fan based on sobol sensitivity analysis

Diagonal flow fans offer substantial energy-saving potential and find broad application across various sectors. Their performance relies heavily on factors like outlet guide vanes and spacing relative to moving blades. However, research into enhancing fan performance through optimized guide vanes and spacing remains limited. In this study, we focus on improving the accuracy of predicting the internal flow field of diagonal flow fans. This paper incorporate rotation and curvature effects using the Large Eddy Simulation (LES) model and introduce stress terms with helicity constraints to create a non-linear subgrid-scale model. This refined model enables more precise numerical simulations. By employing accurate simulations, we optimize the outlet guide vane configuration and conduct sensitivity analysis. We utilize a Radial Basis Function (RBF) model coupled with the Sobol method for this purpose. The optimized guide vane design exhibits enhanced resistance to airflow separation compared to the original, resulting in notable reductions in flow losses within the grille channel. Experimental tests are performed on the diagonal flow fan both before and after optimization. At the specified operating point, the second guide vane optimization leads to a 1.28 m3/min increase in fan flow, a 4.33% rise in total pressure efficiency, and a 2.2 dB noise reduction. These findings underscore the accuracy of the helicity correction model in predicting diagonal flow fan behavior. The multi-objective optimization approach, combining the RBF proxy model with the Sobol method, proves highly reliable. It offers valuable design insights for similar fans and establishes a credible design methodology.

The study and analysis related to the evolution of the knowledge tracing research

In the field of education, the level of awareness of students' knowledge status largely affects students' learning efficiency. Knowledge Tracing, as a method of modeling students' learning status, obtaining their knowledge status to predict their future learning can provide more targeted learning solutions, which is extremely useful for improving students' learning efficiency and implementing tailor-made education. This paper introduces the related research methods and their core ideas in the order of probability-based models, logistic function-based models, and deep learning-based models by investigating the development of this field since its introduction. Among the probability-based models, the mainstream methods include BKT and DBKT, which are based on Hidden Markov Models with certain assumptions and are not ideal in practical application scenarios. In contrast, logistic-based models consider a variety of factors in the student learning process and have achieved good results. After that, deep learning-based model has achieved excellent assessment results through its powerful feature extraction ability, and many variants have been derived. This paper introduces DKT in detail and briefly introduces its variant models. At last, this paper summarizes some of the mainstream evaluation criteria in this field and gives the widely used datasets and their categories. Finally, this paper proposes some suggestions for the future development of the field in view of the shortcomings of the current research status for reference.

Optimization of engineering parameters of deflagration fracturing in shale reservoirs based on hybrid proxy model

Engineering parameter optimization of deflagration fracturing in shale reservoirs is operated with single factor analysis and orthogonal experimental design analysis. There are problems of the difficulty to search the global optimal solution, time-consuming optimization and low computation efficiency. To resolve these problems, a method of optimizing engineering parameters of deflagration fracturing based on the hybrid proxy model was proposed in this paper. The parameters with significant effects on deflagration fracturing were selected as variables, and the models for prediction of reservoir burst degree and stimulated area were established based on Radial Basis Function model, Kriging interpolation model and eXtreme Gradient Boosting regression model respectively. A hybrid proxy model based on the roulette method was established by integrating the priorities of these three models. Then, a multi-objective optimization method of deflagration fracturing based on the hybrid proxy model and was established by integrating the Nondominated Sorting Genetic algorithm II with the engineering parameters as variables and the maximum stimulated area and minimum burst degree as objectives. Optimal design of the deflagration fracturing scheme was achieved. The results show that the hybrid proxy model can accurately predict the evaluation index of the fracturing results in vertical and horizontal wells, and the prediction accuracy is maintained at about 0.9. At the same time, compared with numerical model, the proposed method greatly saves the calculation time. The method proposed by this paper provides the solution to the problem of multi-objective optimization of deflagration fracturing engineering parameters, weighs the reservoir failure degree and the stimulation range, and provides guidance for field operation.

Predicting quality of the whole egg powder during storage: Based on Arrhenius and radial basis function model

The quality of whole egg powder (WEP) deteriorates during long-term storage due to oxidation, which causes caking, discoloration and reduction of functional properties. The purpose of this study is to investigate the changes in lipid oxidation, protein oxidation, and functional characteristics of WEP during accelerated storage and to establish an Arrhenius model and radial basis function neural network (RBFNN) model to predict the oxidative changes in WEP. During storage, the peroxide value (PV) and thiobarbituric acid reactive substances (TBARS) of lipid oxidation significantly increased, and the Fourier transform infrared spectroscopy results showed an increasing trend in the characteristic absorption peaks of lipid hydroperoxides, free fatty acids, and cis fatty acids. The carbonyl groups, surface hydrophobicity, and fluorescence intensity significantly decreased, and the functional characteristics showed a decreasing trend in the solubility, foaming, and emulsifying properties of WEP. Most importantly, compared to the Arrhenius model, the RBFNN model could predict and assess the lipid and protein quality of WEP more accurately, with a small mean squared error (MSE) (0.00048) for storage at 20–60 °C.

Prediction of Blade Tip Timing Sensor Waveforms Based on Radial Basis Function Neural Network

As the existing Blade Tip Timing (BTT) vibration measurement methods have serious under-sampling problems, where the blade resonance frequency is usually higher than the sampling frequency of the data acquisition system of the BTT method, resulting in large errors in the identification of blade vibration parameters, new solutions are needed to extend the capability of BTT to nonlinear and multimodal vibration analysis. Therefore, it is the current research direction to pursue new and more accurate measurement and signal processing methods. By analyzing the waveform data from the BTT sensor and using it for vibration analysis, it significantly extends the BTT database. To avoid the current problems of under-sampling and low recognition accuracy, this paper conducts a study on the recognition of rotating blade vibration parameters based on the Radial Basis Function (RBF) model by establishing a RBF neural network prediction model to analyze the static calibration experimental data and predict the waveform of the BTT sensor, and comparing the prediction curves of various models. As the results show, for the RBF model, the prediction accuracy is closely related to the source data of the sampling point data, when the source data predicted by the RBF model is close to the center of the samples, the prediction accuracy is high, meanwhile, the prediction accuracy decreases as it is far away from the center of these data. At the same time, the number of samples is too small to affect the prediction ability of the RBF model. By using this method, more waveforms under the Blade Tip Clearance (BTC) can be predicted with the available sample point data, and the errors in the experimental measurement process can be corrected.

Response surface methodology and desirability approach to investigate and optimize the performance of a CO2 geothermal thermosyphon

Geothermal thermosyphons (GTs) are increasingly used in many heating and cooling geothermal applications, owing to their high heat transfer performance. In this paper a combined response surface methodology and multi-objective desirability method is proposed for the investigation and optimization of the thermal performance of a geothermal thermosyphon. The filling ratio, temperature, and flow rate of the heat transfer fluid are selected as the designing parameters, and heat transfer rate and effectiveness are adopted as response parameters (objective functions). First, a dedicated experimental GT test bench filled with carbon dioxide (CO2) was built and subjected to different test conditions defined by RSM. A multi-level design of experiment-based response surface methodology (RSM) was used to establish corresponding models between the input parameters and responses. Various diagnostic tests were used to evaluate the quality and validity of the best-fit models, which explain respectively 98.9 and 99.2% of the output result's variability. Further, a surface response analysis of the effect of each input parameter on the responses was performed and a desirability function-based model was employed to determine the optimal combination of factors that would maximize the desired responses. The results revealed that the parameter-setting obtained using the proposed procedure satisfies the requirements of each response, with a desirability of 0.98.

Comparison study of a landslide-event hazard mapping using a multi-approach of fuzzy logic, TRIGRS model, and support vector machine in a data-scarce Andes Mountain region

Landslides are a significant global hazard, especially prevalent in regions with high rainfall, active tectonic processes, and rugged topography, such as the Colombian Andean region. Therefore, it is crucial to identify areas prone to landslides in order to protect human lives and mitigate the adverse impacts on national economies, especially in developing countries situated in tropical and mountainous regions. Assessing landslide hazard and susceptibility is a fundamental step in comprehending the fundamental characteristics of slopes susceptible to failure, particularly under extreme rainfall conditions. Various researchers have devised methods and techniques to assess and map landslides, employing heuristic, statistical, and deterministic approaches. This study carried out a geographic information system-based approach for shallow landslides, with the objective to compare different methods for a landslide-event hazard mapping using the landslide records on May 18, 2015, triggered by a rainstorm in the La Liboriana basin (Colombia). In the first place, a fuzzy logic gamma model was applied using landslide conditioning factors. Then, the deterministic model TRIGRS was applied to assess shallow landslides. Finally, a support vector machine (SVM) model was used to obtain an intermediate scale solution. All models consider the rainfall that triggered the aforementioned landslide event. The results indicated that the SVM (radial basis function) model permits to obtain a better performance (AUC = 0.95) in landslide hazard zonation rather than quantitative heuristic fuzzy gamma model (AUC = 0.86) and the deterministic TRIGRS model (AUC = 0.60), obtaining best accurate at predicting the landslide hazard in the study area.

Identifying Anomalous Behavior in Aircraft Landing Trajectory Using a Bayesian Autoencoder

Anomalous behavior during the aircraft landing phase can significantly increase the probability of adverse events. Automated anomaly detection during the landing phase can help aviation safety-related organizations to efficiently detect anomalous behavior and consider mitigation strategies. This paper develops a Bayesian autoencoder neural network model to identify anomalous behavior in landing trajectories by reconstructing the flight data because the reconstruction error is larger for anomalous flights. Different loss functions, such as Huber loss, mean squared error loss, and least trimmed squares are investigated to construct the Bayesian autoencoder model; and their performances are compared using different measures: the mean of the reconstruction error, the standard deviation of the reconstruction error, and both the mean and standard deviation of the reconstruction error. Different loss function-based models show differences in performance, depending on which measure is used for anomaly detection; among all the options considered, one of the Huber loss options appears to give the best performance, as indicated by the F1 score. Furthermore, the mean and standard deviation of the reconstruction error for a single flight are used to identify the time of occurrence and the flight parameters related to anomalous behavior.

The Use of Forest Biomass for Energy Purposes in Selected European Countries

The utilization of primary and secondary woody biomass resources, despite controversies, is being promoted to reduce dependence on fossil fuels and due to the need to diversify energy sources and ensure energy security in European Union countries. Forest biomass is one of the renewable and sustainable energy sources that can be used for electricity, heat, and biofuel production. In the context of the ongoing energy crisis in Europe, an attempt was made to analyze the production and consumption of woody biomass for energy purposes (fuel wood, chips, and pellets). Specifically, an analysis of similarities between European countries in terms of biomass utilization was conducted. The analysis was complemented by a forecast of primary biomass production in selected European countries. The similarity analysis was conducted using the Ward method. Artificial neural networks (ANNs), including multi-layer feedforward perceptron (MLP) and radial basis function (RBF) models, were used to predict fuelwood extraction. The study showed that woody biomass remains an important source of bioenergy in Europe, and its significance as a strategic resource guaranteeing energy security is likely to increase. Fuel wood harvesting in Europe generally shows an upward trend, particularly in the Czech Republic, Germany, Estonia, Denmark, and the UK. A decreasing trend was observed in France, Spain, Greece, and Cyprus. The analysis revealed differences between countries in terms of woody biomass consumption. The ANN-based forecasts of fuelwood supply generally showed an increase in primary biomass harvesting.

Developing Machine Learning Models for Catalysts in Oxidative Dehydrogenation of n‐butane

Abstract1,3‐butadiene is widely used as a raw material in the synthesis of rubber and automobile tires. In this study, machine learning models were utilized to predict the performance of catalysts in the oxidative dehydrogenation of n‐butane to 1,3‐butadiene., The datasets consisted of 304 data points generated from experiments conducted in a fixed‐bed reactor in the temperature range of 400– 500 °C and O2/C4 molar feed ratio of 1–4 mol/mol, using metal oxides of Nickel (Ni), Iron (Fe), Cobalt (Co), Bismuth (Bi), Molybdenum (Mo), Manganese (Mn) and Tungsten (W) supported on gamma alumina catalysts. Repeated 10‐fold cross‐validation was used, with 70 % of the datasets randomly selected for training and optimizing the models, whereas the remaining 30 % were used for testing the models. Among the various models examined, support vector machine with radial basis function (SVMR) model had the best coefficient of determination (R2) performance for the n‐butane conversion (training set: 98.3 % vs. test set: 88.3 %) and 1,3‐butadiene selectivity (training set: 94.0 % vs. test set: 88.6 %). In addition, the models had the lowest training and test mean absolute errors (MAE) and root mean square errors (RMSE). This study highlights the notable role of machine learning algorithms in enhancing the prediction of n‐butane oxidative dehydrogenation catalyst performance, thus promoting the development of more effective catalyst designs that exhibit high activity and selectivity, contributing positively to the field of heterogeneous catalysis. Notably, a user‐friendly application incorporating the developed machine learning models was developed, providing an invaluable tool for the real‐time on‐site prediction of catalyst performance in the oxidative dehydrogenation of n‐butane to 1,3‐butadiene. This application is a significant advancement, facilitating swift and accurate catalyst performance prediction, fostering more efficient catalyst design, streamlining, and revolutionizing the rubber and automobile tire synthesis industries.

Research on surrogate model of dam numerical simulation with multiple outputs based on adaptive sampling.

Dam numerical simulation is an important method to research the dam structural behavior, but it often takes a lot of time for calculation when facing problems that require many simulations, such as structural parameter back analysis. The surrogate model is widely used as a technology to reduce computational cost. Although various methods have been widely investigated, there are still problems in designing the surrogate model's optimal Design of Experiments (DoE). In addition, most of the current DoE focuses on establishing a single-output problem. Designing a reasonable DoE for high-dimensional outputs is also a problem that needs to be solved. Based on the above issues, this research proposes a sequential surrogate model based on the radial basis function model (RBFM) with multi-outputs adaptive sampling. The benchmark function demonstrates the applicability of the proposed method to single-input & multi-outputs and multi-inputs & multi-outputs problems. Then, this method is applied to establishing a surrogate model for dam numerical simulation with multi-outputs. The result demonstrates that the proposed technique can be sampled adaptively and samples can be targeted based on the function form of the surrogate model, which significantly reduces the required sampling and calculation cost.

Multi-objective optimization design of shaftless rim-driven thruster

This paper proposes a multi-objective optimization design method for shaftless rim-driven thruster (RDT) based on the ISIGHT platform. The pitch ratio, the blade area ratio and the advance coefficient of RDT were considered as the design optimization variables, the thrust and efficiency of the RDT were the optimization objectives. The multi-objective optimization design method was based on the surrogate module and the optimization module in the ISIGHT platform. Two analytical methods (response surface methodology (RSM) and radial basis function Model (RBF)) were used to build the surrogate model. The Muti-Island GA optimization algorithm was adopted in the optimization module. The paper indicates that both RSM model and RBF model are feasible to build the surrogate model, the RBF model has better accuracy and reliability than RSM model. The blade obtained by RBF method has larger thrust and smaller torque than that of the blade obtained by RSM method. For the large advance coefficient ( J > 0.6), the efficiency of the blade obtained by RBF method is slightly higher than that of the blade obtained by RSM method.

Multidisciplinary design optimization of dynamic positioning system for semi-submersible platform

The dynamic positioning system (DPS) is a complex mechatronic system consisting of multiple sub-disciplines. For such highly coupled sub-disciplines and sub-systems within the DPS, the conventional sequential design strategy cannot manage this interaction adequately and explicitly. Achieving the global optimal solution becomes challenging due to the discoordination between the structure and dynamic performance parameters. To address this challenge, a co-design strategy is explored by combining the structure and dynamic performance parameters based on a simultaneous dynamic optimization approach. To minimize the operating cost of the DPS, the radial basis function (RBF) model is extended to the constraint condition. The alpine skiing optimization (ASO) algorithm is applied to solve the multidisciplinary design optimization problem. The results indicate that the co-design strategy not only reduce the operating cost of the DPS but also achieve a smaller rolling response and yaw response.