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This paper addresses the high‐precision control problem for nonlinear strict feedback systems with external time‐varying disturbances and proposes a novel composite learning control algorithm. Unlike previous research that only uses tracking errors for neural network updates, this paper prioritizes the accuracy of neural network learning. The article uses a selective memory recursive least squares algorithm to construct system information prediction errors, which are combined with tracking errors to update the neural network weights. A new composite learning control algorithm is developed to design dynamic surface control and neural network disturbance observers, which achieves high‐precision control of nonlinear strict feedback systems under external time‐varying disturbance conditions. Lyapunov's method demonstrates the stability of the closed‐loop system and the boundedness of errors. The simulation results show that the proposed control algorithm can effectively estimate system nonlinearity and suppress the impact of disturbances. [ABSTRACT FROM AUTHOR]

Radial basis function (RBF) neural network training presents a challenging optimization task, necessitating the utilization of advanced algorithms that can fully train the network so as to produce accurate and computationally efficient models. To achieve this goal, this work introduces a new framework where the original RBF training problem is divided into two simpler subproblems; the linear parameters, namely the network weights, are projected out of the problem using variable projection (VP), thus leaving a reduced functional, which depends only on nonlinear parameters, i.e., the RBF centers. The centers are updated using the Levenberg–Marquardt (LM) algorithm, while the optimal values of the synaptic weights are calculated in each iteration of the LM algorithm using linear regression. The proposed VP-LM scheme is coupled with the fuzzy means (FM) algorithm, which helps to select the number of RBF centers and enhances the overall search procedure, thus resulting to a framework that produces parsimonious models with enhanced accuracy in shorter training times. The proposed training scheme is evaluated on 12 both real-world and synthetic benchmark datasets and tested against various RBF training algorithms, as well as different neural network architectures. The experimental results underscore the effectiveness of the VP-FM algorithm in producing neural network models that outperform those generated by alternative methods in many aspects; to be more specific, the proposed approach achieves very competitive model accuracy, while resulting to smaller network sizes and thus lower complexity, which leads to shorter training times. [ABSTRACT FROM AUTHOR]

This paper presents a high-resolution gridded dataset of daily minimum (TN), mean (TG) and maximum (TX) near-surface air temperatures over Poland, covering the period from 1951 to 2020, with a spatial resolution of 1 km2. PL1GD-T dataset was developed using radial basis functions (RBFs) applied to quality-controlled observations from ground weather stations from the Institute of Meteorology and Water Management – National Research Institute. Cross-validation methods evaluated the gridding procedure on a monthly basis. The linear RBF was employed by hold-out cross-validation (HO-CV) as the most suitable for the gridding procedure among other RBFs. The leave-one-out cross-validation (LOO-CV) was performed to ensure the ability to reproduce the original characteristics variability. The values of the scores averaged over all stations for individual months are in range 0.3–0.2, 0.3–0.2, 0.1–0.2 for the bias, in range 1,23–1,46, 0.69–0.92, 0.84–0.99 the root-mean-squared difference (RMSD) and in range 0.91–0.97, 0.98–0.99, 0.98–0.99 for the correlation for TN, TG and TX, respectively. The RSMD is clearly altitude dependent, increasing from low-land to mountainous regions. The dataset's scope and resolution allowed robust estimation of local climate variability characteristics and observed trends. The availability of high-resolution datasets in both spatial and temporal contexts is essential for climate change impact analysis on a smaller scale. This new dataset provides a quality-validated, high-resolution, and open-access dataset that could be utilised by society, administrative bodies, or research institutions for further analysis. The dataset is publicly available from the repository of the Institute of Meteorology and Water Management – National Research Institute under https://doi.org/10.26491/imgw%5frepo/PL1GD-T (Jaczewski et al., 2024). [ABSTRACT FROM AUTHOR]

Introduction: The robot-assistive technique has been widely developed in the field of neurorehabilitation for enhancement of neuroplasticity, muscle activity, and training positivity. To improve the reliability and feasibility in this patient–robot interactive context, motion constraint methods and adaptive assistance strategies have been developed to guarantee the movement safety and promote the training effectiveness based on the user's movement information. Unfortunately, few works focus on customizing quantitative and appropriate workspace for each subject in passive/active training mode, and how to provide the precise assistance by considering movement constraints to improve human active participation should be further delved as well. Methods: This study proposes an integrated framework for robot-assisted upper-limb training. A human kinematic upper-limb model is built to achieve a quantitative human–robot interactive workspace, and an iterative learning-based repulsive force field is developed to balance the compliant degrees of movement freedom and constraint. On this basis, a radial basis function neural network (RBFNN)-based control structure is further explored to obtain appropriate robotic assistance. The proposed strategy was preliminarily validated for bilateral upper-limb training with an end-effector-based robotic system. Results: Experiments on healthy subjects are enrolled to validate the safety and feasibility of the proposed framework. The results show that the framework is capable of providing personalized movement workspace to guarantee safe and natural motion, and the RBFNN-based control structure can rapidly converge to the appropriate robotic assistance for individuals to efficiently complete various training tasks. Discussion: The integrated framework has the potential to improve outcomes in personalized movement constraint and optimized robotic assistance. Future studies are necessary to involve clinical application with a larger sample size of patients. [ABSTRACT FROM AUTHOR]

ABSTRACT The problem of air feed system control with guaranteed prescribed performance and fixed time control is investigated for proton exchange membrane fuel cell (PEMFC). First of all, the original model of PEMFC air feed system is converted to fully actuated system using higher order fully actuated system (HOFAS) approach. A fixed time extended state observer (ESO) is employed to approximate the cathode pressure. The parameter uncertainty is compensated by an adaptive radial basis function neural network (RBFNN). Then a prescribed performance sliding mode controller is designed with fixed convergence time. The proposed control law could guarantee the prescribed transient response for oxygen excess ratio (OER) error under stack current changing and bounded uncertainty. Lyapunov approach is utilized to proof fixed‐time stability of proposed controller. Simulation results of the study tests illustrate that the presented controller is efficient under constant OER tracking control, constant OER tracking control with parameter perturbation, and variable OER tracking control, respectively. [ABSTRACT FROM AUTHOR]

Background/Objectives: Spinal conditions, such as fractures and herniated intervertebral discs (HIVDs), are often challenging to diagnose due to overlapping clinical symptoms and the difficulty in assessing their functional impact. Accurate differentiation between these conditions is crucial for effective treatment, particularly in the context of preoperative anesthesia evaluation, where understanding the underlying condition can influence anesthesia planning and pain management. Methods and Materials: This study presents a Support Vector Machine (SVM) model designed to distinguish between spinal fractures and HIVDs using key clinical predictors, including age, gender, preoperative Visual Analog Scale (VAS) pain scores, and the number of spinal fractures. A retrospective analysis was conducted on a dataset of 199 patients diagnosed with these conditions. The SVM model, using a radial basis function (RBF) kernel, classified the conditions based on the selected predictors. Model performance was evaluated using precision, recall, accuracy, and the Kappa index, with Leave-One-Out (LOO) cross-validation applied to ensure robust results. Results: The SVM model achieved a precision of 92.1% for fracture cases and 91.2% for HIVDs, with recall rates of 98.1% for fractures and 70.5% for HIVDs. The overall accuracy was 92%, and the Kappa index was 0.76, indicating substantial agreement. The analysis revealed that age and VAS pain scores were the most critical predictors for accurately diagnosing these conditions. Conclusions: These results highlight the potential of the SVM model with an RBF kernel to reliably differentiate between spinal fractures and HIVDs using routine clinical data. Future work could enhance model performance by incorporating additional clinical parameters relevant to preoperative anesthesia evaluation. [ABSTRACT FROM AUTHOR]

The loss of energy by heat is a common problem in almost all areas of industry, and heat pipes are essential to increase efficiency and reduce energy waste. However, in many cases, they have complex theoretical equations with high percentages of error, limiting their development and causing dependence on empirical methods that generate a waste of time and material, resulting in significant expenses and reducing the viability of their use. Thus, Artificial Neural Networks (ANNs) can be an excellent option to facilitate the construction and development of heat pipes without knowledge of the complex theory behind the problem. This investigation uses experimental data from previous studies to evaluate the ability of three different ANNs to predict the thermal performance of heat pipes with different capillary structures, each of them in various configurations of the slope, filling ratio, and heat load. The goal is to investigate results in as many different scenarios as possible to clearly understand the networks' capacity for modeling heat pipes and their operating parameters. We chose two classic ANNs (the most used, Multilayer Perceptron (MLP) network, and the Radial Basis Function (RBF) network) and the Extreme Learning Machine (ELM), which has not yet been applied to heat pipes studies. The ELM is an Unorganized Machine with a fast training process and a simple codification. The ANN results were very close to the experimental ones, showing that ANNs can successfully simulate the thermal performance of heat pipes. Based on the RMSE (error metric being reduced during the training step), the ELM presented the best results (RMSE = 0.384), followed by MLP (RMSE = 0.409), proving their capacity to generalize the problem. These results show the importance of applying different ANNs to evaluate the system deeply. Using ANNs in developing heat pipes is an excellent option for accelerating and improving the project phase, reducing material loss, time, and other resources. [ABSTRACT FROM AUTHOR]

Detecting and characterizing continuous changes on Earth's surface has become critical for planning and development. Since 2016, Planet Labs has launched hundreds of nanosatellites, known as Doves. Despite the advantages of their high spatial and temporal resolution, these nanosatellites' images still present inconsistencies in radiometric resolution, limiting their broader usability. To address this issue, a model for radiometric normalization of PlanetScope (PS) images was developed using Multispectral Instrument/Sentinel-2 (MSI/S2) sensor images as a reference. An extensive database was compiled, including images from all available versions of the PS sensor (e.g., PS2, PSB.SD, and PS2.SD) from 2017 to 2022, along with data from various weather stations. The sampling process was carried out for each band using two methods: Conditioned Latin Hypercube Sampling (cLHS) and statistical visualization. Five machine learning algorithms were then applied, incorporating both linear and nonlinear models based on rules and decision trees: Multiple Linear Regression (MLR), Model Averaged Neural Network (avNNet), Random Forest (RF), k-Nearest Neighbors (KKNN), and Support Vector Machine with Radial Basis Function (SVM-RBF). A rigorous covariate selection process was performed for model application, and the models' performance was evaluated using the following statistical indices: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Lin's Concordance Correlation Coefficient (CCC), and Coefficient of Determination (R2). Additionally, Kruskal–Wallis and Dunn tests were applied during model selection to identify the best-performing model. The results indicated that the RF model provided the best fit across all PS sensor bands, with more accurate results in the longer wavelength bands (Band 3 and Band 4). The models achieved RMSE reflectance values of approximately 0.02 and 0.03 in these bands, with R2 and CCC ranging from 0.77 to 0.90 and 0.87 to 0.94, respectively. In summary, this study makes a significant contribution to optimizing the use of PS sensor images for various applications by offering a detailed and robust approach to radiometric normalization. These findings have important implications for the efficient monitoring of surface changes on Earth, potentially enhancing the practical and scientific use of these datasets. [ABSTRACT FROM AUTHOR]

An overview of the status of the research in analytical figures of merit is provided, including all calibration scenarios from univariate to multivariate and multiway analytical protocols. Both linear and nonlinear multivariate models are considered. Starting with the simplest multivariate model, inverse least‐squares regression, the basic concepts of sensitivity, sample leverage, and limit of detection are introduced. The extension to other multivariate models is discussed, as well as to nonlinear models based on radial basis functions, kernel partial least‐squares, and multilayer feed‐forward artificial neural networks. Finally, multiway calibration models are discussed, including multilinear decomposition models such as parallel factor analysis (PARAFAC) and multivariate curve resolution–alternating least‐squares (MCR‐ALS). In the latter case, recent developments concerning the pervasive phenomenon of rotational ambiguity are discussed. Unfinished works and areas where further research efforts are needed to develop closed‐form expressions and to fully understand their meaning are included. [ABSTRACT FROM AUTHOR]

This work addresses issues that appear from wind turbines equipped with frequency converters and presents a new fixed-speed wind turbine (FSWT) concept based on a split power transmission, along with a Mechatronic Control Model. The wind turbine rotor drives the first transmission shaft, while a servomotor with variable speed powers the second input. The differential gear transmission output is linked to the electric grid through an asynchronous generator. To optimize the power extracted from the wind energy while minimizing excessive dynamic loads on the wind turbine, a mechatronic control model of a 750 kW-FSWT is applied using a proportional and integral controller (PI). The paper suggests employing neural networks and evolutionary algorithms for determining appropriate PI gains. For collective servomotor control of a 750 kW-FSWT, a radial basis function (RBF) neural networks based on PI controller is proposed. To acquire an optimum dataset for RBF training, the particle swarm optimization (PSO) evolutionary algorithm is harnessed. The robustness and effectiveness of the proposed model and controller are verified and confirmed through simulation results. Hands-on experience is conducted using the 20-sim software package. [ABSTRACT FROM AUTHOR]

The related work of the diamond burnishing processes focused on improvements in surface quality. The study aims to optimize burnishing factors, including the spraying distance of the nozzle (S), the inlet pressure of the cold air (I), and the quantity of the liquid CO2 (L) of the cool and cryogenic-assisted diamond burnishing operation for minimizing energy consumed (EC) and arithmetical mean surface height roughness (Sa). Burnishing responses are modelled based on the radial basis function network and full factorial data. The entropy method, improved grey wolf optimizer, non-dominated sorting genetic algorithm II, and technique for order of preference by similarity to the ideal solution were implemented to calculate the weights, produce solutions, and select the best outcome. As a result, the optimal data of the S, I, and L were 15.0 mm, 3.0 bar, and 11.0 L/min, respectively. The Sa and EC were reduced by 20.4% and 3.8%, respectively, at the optimality. The optimized outcomes could be employed to improve energy efficiency and machining quality for the internal diamond burnishing process. The optimizing technique could be used to solve complicated issues for different burnishing operations. The cool and cryogenic-assisted diamond burnishing process could be utilized for machining different internal holes. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for solving the first-passage time probability problem of nonlinear stochastic dynamic systems. The safe domain boundary is exactly imposed into the radial basis function neural network (RBF-NN) architecture such that the solution is an admissible function of the boundary-value problem. In this way, the neural network solution can automatically satisfy the safe domain boundaries and no longer requires adding the corresponding loss terms, thus efficiently handling structure failure problems defined by various safe domain boundaries. The effectiveness of the proposed method is demonstrated through three nonlinear stochastic examples defined by different safe domains, and the results are validated against the extensive Monte Carlo simulations (MCSs). [ABSTRACT FROM AUTHOR]

When calculating the seismic response of structures, conventional methods often neglect uncertainty and suffer from excessive computation. Although robustness metrics have been effective in evaluating structural safety, they are not suitable for isolated continuous girder bridges (ICGB). To address this issue, this paper introduces an uncertain seismic response and robustness assessment method tailored to ICGB: Firstly, the method considers uncertainty in material parameters and ground motion by constructing a data set using Monte Carlo simulation. Secondly, a genetic algorithm for parallel optimisation of radial basis function neural network (GAPO-RBFNN) is trained to predict the seismic responses of the bridges. Thirdly, a robustness metric specifically designed for bridges is derived and applied to bridge. Finally, the proposed robustness metric is validated on lead rubber bearing (LRB) and shape memory alloy-lead rubber bearing (SMA-LRB) bridges. Results indicate that the GAPO-RBFNN model accurately approximates the mapping relationship between structural parameters and seismic response, with an average error of only 0.32% and a 70% reduction in computation time compared with traditional methods. Moreover, the new robustness metric overcomes the limitations of the dismantled member method and is applicable to bridge. The robustness of the SMA-LRB-ICGB, strengthened by SMA, is improved, providing evidence of the effectiveness of the proposed robustness metric. [ABSTRACT FROM AUTHOR]

Traditional control charts depend on the process parameters that are used to monitor the shifts in the process. The adaptive control charts are used to adapt a process parameter during the online monitoring. This research introduces a support vector regression (SVR) based adaptive exponentially weighted moving average control chat to enhance the monitoring of the mean in industrial processes. The study systematically investigates the comparative efficiency of linear, radial basis function (RBF), and polynomial functions within the SVR framework. The proposed SVR‐based AEWMA control chart leverages the strengths of the RBF kernel, providing a robust mechanism for detecting shifts in the process mean by adapting the smoothing constant according to the size of the shift. To validate the efficacy of the proposed methodology, a practical application is presented by using real‐life data. The application showcases the adaptability and reliability of the SVR‐based adaptive EWMA control chart in effectively monitoring location shifts. [ABSTRACT FROM AUTHOR]

In this paper, a learning-based nearly optimal control framework with fault-tolerant capability is designed to tackle the tracking control problem of a flexible-link manipulator in the presence of actuator fault and model uncertainties. Initially, the optimal control law is obtained by adopting the dynamic programming and a critic structure as the solution of Hamilton–Jacobi–Bellman equation for the nominal model. Then, by implementing an integral sliding mode control, the robustness against actuator fault and model uncertainty is guaranteed. The adaptive laws are constructed based on radial basis functions neural networks to estimate the upper bound of uncertainty and the actuator bias fault, satisfying both optimal performance and chattering reduction of the sliding surface. Furthermore, the actuator effectiveness loss is handled. The stability of the closed-loop system is analytically proven, and the performance of the proposed framework is investigated against several practical operating conditions. This incorporates the fidelity assessment of tracking precision and trackability of control signal using performance indices such as the integral absolute error and root-mean-square error. The results of extensive simulation studies confirm the effectiveness and robustness of the proposed control framework. [ABSTRACT FROM AUTHOR]

For boundary value problem of an elliptic equation with variable coefficients describing the physical field distribution in inhomogeneous media, the parametrix can represent the solution in terms of volume and surface potentials, with the drawback that the volume potential involving in the solution expression requires heavy computational costs as well as the solvability of the integral equations with respect to the density pair. We introduce an modified integral expression for the solution to an elliptic equation in divergence form under the parametrix framework. The well‐posedness of the linear integral system with respect to the density functions to be determined is rigorously proved. Based on the singularity decomposition for the parametrix, we propose two schemes to deal with the volume integrals so that the density functions can be solved efficiently. One method is an adaptive discretization scheme for computing the integrals with continuous integrands, leading to the uniform accuracy of the integrals in the whole domain, and consequently the efficient computations for the density functions. The other method is the dual reciprocity method which is a meshless approach converting the volume integrals into boundary integrals equivalently by expressing the volume density as the combination of the radial basis functions determined by the interior grids. The proposed schemes are justified numerically to be of satisfactory computation costs. Numerical examples in 2‐dimensional and 3‐dimensional cases are presented to show the validity of the proposed schemes. [ABSTRACT FROM AUTHOR]

Pumped storage is an important green, low-carbon, and clean flexible regulating power source in the power system, which can provide regulation services for the power system, promote the construction of a new type of power system, and facilitate the green transformation of energy. To improve the efficiency and stability of the centrifugal pump turbine under multiple operating conditions, a surrogate model combining radial basis functions with a high-dimensional model is used for performance optimization. Taking the active guide vane of the centrifugal pump turbine as the research object, the airfoil profile is parameterized, and the surrogate model's independent variables and training range are determined. Combining programming and numerical simulation software, an efficiency prediction model for the centrifugal pump and water turbine based on guide vane airfoil control variables is constructed. The particle swarm algorithm is used to globally optimize the constructed model to obtain the optimal efficiency point and corresponding airfoil-related parameters. Finally, numerical simulation and experimental research methods are used to validate the predicted data. The results show that under the premise of ensuring grid performance and operational stability, the numerical simulation efficiency of the pump turbine under the optimization scheme is increased by 1.6 and 0.32%, respectively, compared to the numerical efficiency of the prototype guide vane. In the experimental case, the efficiency of the water turbine and pump is increased by 0.76 and 0.14%, respectively, compared to the prototype guide vane. [ABSTRACT FROM AUTHOR]

In this study, we address the trajectory tracking control problem for stratospheric airships, characterized by parameter uncertainties and external disturbances. Initially, a mathematical model of the airship is constructed, from which a trajectory tracking error model is derived. Subsequently, a novel sliding mode control law is formulated to enhance control accuracy and accelerate the convergence rate of the sliding mode. Furthermore, radial basis function neural networks are utilized to estimate and compensate for uncertain system parameters and external disturbances, significantly improving the system's robustness. Lastly, the application of Lyapunov's theory verifies the fixed-time stability of the closed-loop system and ensures the convergence of trajectory tracking error to a near-zero vicinity within fixed time. Simulation results strongly support the efficacy of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The advancement of machine learning offers a promising solution for diagnosing both the extent of leakage and the precise location of natural gas transmission pipeline leaks. While pipeline leaks often manifest through sound-related signals, leveraging machine learning for signal classification is still in its nascent stages. We propose a hybrid technological approach for detecting and pinpointing gas pipeline leaks, which is rooted in machine learning principles. In this paper, we simulate and classify the degree of pipe-line leakage by adjusting the bolt aperture. We analyze and select original data acquired through acquisition and processing using eigenvalue methods. Subsequently, we employ a deep learning algorithm for training and testing the data post feature extraction, enabling the realization of pipeline leakage diagnosis and classification. To fully leverage the information embedded in the original data, we adopt the radial basis function neural network (RBF-NN) machine learning technique. Moreover, we utilize the genetic algorithm (GA) for optimization, resulting in the generation of a radial basis function neural network model (GA-RBF) optimized through genetic algorithmic techniques. We compare the predictive performance of the GA-RBF model with that of BP-NN and RBF-NN. The diagnostic F1 scores of the GA-RBF model under various operational conditions of gas pipelines are as follows: 98.30%, 97.50%, 95.10%, and 95.80%, respectively, with an overall accuracy of 96.68%. The diagnostic outcomes align well with theoretical expectations, demonstrating the superior performance of the GA-RBF model in gas pipe-line leakage diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered adaptive neural prescribed performance admittance control (ETANPPAC) scheme is proposed to control the constrained robotic systems without velocity sensors. To ensure compliance during human–robot interaction, the reference trajectory is obtained by reshaping the desired trajectory for the robotic systems based on the admittance relationship, where a saturation function is used to constrain the reference trajectory, avoiding excessive contact forces that could render the trajectory inexecutable. Moreover, a barrier Lyapunov function is used to constrain the tracking errors for prescribed performance, where a velocity observer and a radial basis function neural network are designed to estimate the velocity and the uncertainty of the robotic systems, respectively, to enhance control performance. To reduce the communication burden, an event-triggered mechanism is introduced and the Zeno behavior is avoided with the event-triggered condition. The stability of the whole control scheme is analyzed by the Lyapunov function. Simulation and experimental tests demonstrate that the proposed ETANPPAC scheme can track the desired trajectory well under constraints and reduce the communication burden, thereby achieving better efficiency for controlling the robotic systems compared with similar control schemes. • The ETANPPAC scheme achieves compliance and prescribed performance with constraints. • Barrier Lyapunov function constrains tracking errors, achieving desired performance. • Event-triggered method reduces the system burden and the Zeno behavior is avoided. • The proposed ETANPPAC scheme is tested on a numerical model and actual manipulator. [ABSTRACT FROM AUTHOR]

This work presents the formulation and numerical results of the Dual Reciprocity Boundary Element Method (DRBEM), adapted to include the procedure known as the addition of polynomial functions. This technique is used to improve the interpolation scheme's accuracy using radial basis functions. This idea works quite effectively in simple interpolation procedures, especially when the field profile to be approximated has similarities with added polynomials. Although applied as a supplementary resource in some works, the efficiency and restrictions of the procedure were not discussed in detail as necessary. In this article, the addition of functions is applied to solve non-homogeneous stationary heat transfer problems. The main objective is to analyze its capability, especially in reducing the demand for internal interpolation points. The three simulations show that although introducing polynomials can improve the results, other characteristics are mandatory for achieving suitable accuracy. Three examples of numerical simulation are solved and show that its effectivity is relative, mainly limited by the type of application. [ABSTRACT FROM AUTHOR]

Information pertaining to a reservoir's bathymetry is of utmost significance for water resource sustainability and management. The current study evaluated and compensated the reservoir's bathymetric patterns established using radial basis function (RBF) approaches. Water depth data were acquired by conventionally rolling out a measuring tape into the water. The water depth data were split into three (3) categories, i.e., training data, validation data, and test dataset. Spatial variations in the field-measured bathymetry were determined through descriptive statistics. The thin-plate spline (TPS), multiquadric function (MQF), inverse multiquadric (IMQF), and Gaussian function (GF) were integrated into RBF to establish bathymetric patterns based on the training data. Spatial variations in bathymetry were assessed using Levene's k-comparison of equal variance. The coefficient of determination ( R 2 ), root mean square error (RMSE) and absolute error of mean (AEM) techniques were used to evaluate the uncertainties in the interpolated bathymetric patterns. The regression of the observed estimated (ROE) was used to compensate for uncertainties in the established bathymetric patterns. The Levene's k-comparison of equal variance technique revealed variations in the predicted bathymetry, with the standard deviation of 8.94, 6.86, 4.36, and 9.65 for RBF with thin-plate spline, multi quadric function, inverse multiquadric function, and Gaussian function, respectively. The bathymetric patterns predicted with thin-plate spline, multiquadric function, inverse multiquadric function, and Gaussian function revealed varying accuracy, with AEM values of −1.59, −2.7, 2.87, and −0.99, respectively, R 2 values of 0.68, 0.62, 0.50, and 0.70, respectively, and RMSE values of 4.15, 5.41, 5.80 and 3.38, respectively. The compensated mean bathymetric values for thin-plate spline, multiquadric function, inverse multiquadric function, and Gaussian-based RBF were noted to be 18.21, 17.82, 17.35, and 18.95, respectively. The study emphasized the ongoing contribution of geospatial technology towards inland water resource monitoring. [ABSTRACT FROM AUTHOR]

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Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO (Fair Isaac Corporation) or Vantage scores, remain proprietary and inaccessible to the public. This study aims to devise an alternative credit scoring metric that mirrors the FICO score, using an extensive dataset from Lending Club. The challenge lies in the limited available insights into both the precise analytical formula and the comprehensive suite of credit-specific attributes integral to the FICO score's calculation. Our proposed metric leverages basic information provided by potential borrowers, eliminating the need for extensive historical credit data. We aim to articulate this credit risk metric in a closed analytical form with variable complexity. To achieve this, we employ a symbolic regression method anchored in genetic programming (GP). Here, the Occam's razor principle guides evolutionary bias toward simpler, more interpretable models. To ascertain our method's efficacy, we juxtapose the approximation capabilities of GP-based symbolic regression with established machine learning regression models, such as Gaussian Support Vector Machines (GSVMs), Multilayer Perceptrons (MLPs), Regression Trees, and Radial Basis Function Networks (RBFNs). Our experiments indicate that GP-based symbolic regression offers accuracy comparable to these benchmark methodologies. Moreover, the resultant analytical model offers invaluable insights into credit risk evaluation mechanisms, enabling stakeholders to make informed credit risk assessments. This study contributes to the growing demand for transparent machine learning models by demonstrating the value of interpretable, data-driven credit scoring models. [ABSTRACT FROM AUTHOR]

This paper proposes a linear quadratic regulation (LQR) tracking control method based on a radial basis function (RBF) that successfully compensates for the shortcomings of the LQR method. The LQR method depends on the linearity of a model. Specifically, an RBF neural network is used to approximate and compensate for the nonlinear part of a controlled object in the PID type-I, type-II and type-III control loops to improve the performance of the system. Through the simulation of different industrial systems, such as underdamped, overdamped and critically damped systems, the method significantly improves the dynamic response performance indices, such as the rise time and settling time, of the system. [ABSTRACT FROM AUTHOR]

This paper is concerned with the finite‐time tracking control problem of fractional‐order nonlinear systems (FONSs) with uncertainty and external disturbance. A novel design scheme of the adaptive neural network finite‐time controller (ANNFTC) is developed by utilizing the theory of finite‐time stability and fractional‐order dynamic surface control (DSC) scheme combined with backstepping method. Radial basis function neural networks (RBF NNs) are employed to estimate the unknown nonlinear function. Furthermore, an auxiliary function is introduced to approximate the unknown upper bounds of the approximation error in RBF NNs and external disturbance. The ANNFTC ensures the finite‐time boundedness of all signals in FONSs and enhances the system output's tracking performance. The effectiveness of the proposed approach is demonstrated through a simulation example, providing empirical evidence to support the theoretical framework presented in this paper. [ABSTRACT FROM AUTHOR]

Malware poses a significant threat to cybersecurity, continuously evolving to evade traditional detection methods. Deep learning (DL) offers a promising avenue for addressing this challenge by leveraging its ability to learn complex patterns from large amounts of data. This paper introduces a novel deep learning architecture that combines convolutional neural network (CNN), long short-term memory network (LSTM), and radial basis function network (RBF) to extract discriminative features from malware images. Our model captures both low-level visual details and high-level semantic information for robust and accurate malware classification. The Softmax layer assigns probabilities to different malware classes, enabling precise classification. We evaluated the model on three diverse datasets: Malimg, MaleVis, and Microsoft Big 2015, each differing in malware families, image characteristics, and collection methods. The model achieved accuracy rates of 99.06%, 93.55%, and 97.52% on these datasets, respectively, demonstrating its effectiveness and potential in malware image classification. [ABSTRACT FROM AUTHOR]

We have presented a novel lattice Boltzmann approach for the non‐uniform rectangular mesh based on the radial basis function approximation (RBF‐LBM). The non‐uniform rectangular mesh is a good option for local grid refinement, especially for the wall boundaries and flow areas with intensive change of flow quantities. Which allows, the total number of grid cells to be reduced and so the computational cost, therefore improving the computational efficiency. But the grid structure of the non‐uniform rectangular mesh is no longer applicable to the classic lattice Boltzmann method (CLBM), which is based on the famous BGK collision‐streaming evolution. This is why the present study is inspired by the idea of the interpolation‐supplemented LBM (ISLBM) methodology. The ISLBM algorithm is improved in the present manuscript and developed into a novel LBM approach through the radial basis function approximation instead of the Lagrangian interpolation scheme. The new approach is validated for both steady states and unsteady periodic solutions. The comparison between the radial basis function approximation and the Lagrangian interpolation is discussed. It is found that the novel approach has a good performance on computational accuracy and efficiency. Proving that the non‐uniform rectangular mesh allows grid refinement while obtaining precise flow predictions. [ABSTRACT FROM AUTHOR]

By analyzing the results of compliance minimization of thermoelastic structures, we observed that microstructures play an important role in this optimization problem. Then, we propose to use a multiple variable cutting (M–VCUT) level set-based model of microstructures to solve the concurrent two–scale topology optimization of thermoelastic structures. A microstructure is obtained by combining multiple virtual microstructures that are derived respectively from multiple microstructure prototypes, thus giving more diversity of microstructure and more flexibility in design optimization. The effective mechanical properties of microstructures are computed in an off-line phase by using the homogenization method, and then a mapping relationship between the design variables and the effective properties is established, which gives a data-driven model of microstructure. In the online phase, the data-driven model is used in the finite element analysis to improve the computational efficiency. The compliance minimization problem is considered, and the results of numerical examples prove that the proposed method is effective. [ABSTRACT FROM AUTHOR]

It has been proven that robot-assisted rehabilitation training can effectively promote the recovery of upper-limb motor function in post-stroke patients. Increasing patients' active participation by providing assist-as-needed (AAN) control strategies is key to the effectiveness of robot-assisted rehabilitation training. In this paper, a greedy assist-as-needed (GAAN) controller based on radial basis function (RBF) network combined with 3 degrees of freedom (3-DOF) potential constraints was proposed to provide AAN interactive forces of an end-effect upper limb rehabilitation robot. The proposed 3-DOF potential fields were adopted to constrain the tangential motions of three kinds of typical target trajectories (one-dimensional (1D) lines, two-dimensional (2D) curves and three-dimensional (3D) spirals) while the GAAN controller was designed to estimate the motor capability of a subject and provide appropriate robot-assisted forces. The co-simulation (Adams-Matlab/Simulink) experiments and behavioral experiments on 10 healthy volunteers were conducted to validate the utility of the GAAN controller. The experimental results demonstrated that the GAAN controller combined with 3-DOF potential field constraints enabled the subjects to actively participate in kinds of tracking tasks while keeping acceptable tracking accuracies. 3D spirals could be better in stimulating subjects' active participation when compared to 1D and 2D target trajectories. The current GAAN controller has the potential to be applied to existing commercial upper limb rehabilitation robots. [ABSTRACT FROM AUTHOR]

Practical Application Conventional methods for evaluating of fish freshness based on physiological and biochemical methods are often destructive, complicated, and costly. This study aimed to predict the freshness of large yellow croaker which was sampled every second day in 9 consecutive days at 4°C, using computer vision technology combined with pupil color parameters and different machine learning algorithms (back propagation neural network, BPNN; radial basis function neural network; support vector regression; and random forest regression, RFR). In the process of model building, the RFR model provided the most accurate prediction for the value of total volatile basic nitrogen (TVB‐N), with the R‐square of the test set (Rp2$R^{2}_p $) of 0.993. The BPNN model exhibited the best fit for predicting the value of thiobarbituric acid (TBA), with Rp2$R^{2}_p $ of 0.959. Additionally, the RFR model was the most effective in forecasting total viable count (TVC), with Rp2$R^{2}_p $ of 0.935. After validation, the root mean square error values of the RFR model for predicting TVB‐N value, TBA value, and TVC value were the lowest, which were 0.764, 0.067, and 0.219, respectively. It demonstrated the applicability and good predictive performance of the RFR model for predicting biochemical and microbiological indicators. These findings also demonstrated that monitoring the changes in pupil color could successfully predict the freshness of chilled fish.Application Scenario: Quality inspectors detect changes in the freshness of large yellow croaker in real time from the beginning of distribution to the selling site. [ABSTRACT FROM AUTHOR]

In this work, the tracking control problem for a class of nonlinear strict-feedback systems with input nonlinearity is addressed. In response to the influence of input nonlinearity, an auxiliary control system is constructed to compensate for it. To process unknown nonlinear dynamics, radial basis function neural networks (RBFNNs) are introduced to approximate them, and some adaptive updating control laws are designed to estimate unknown parameters. Furthermore, during the dynamic surface control (DSC) design process, first-order low-pass filters are introduced to solve the complexity explosion problems caused by repeated differentiation. After that, an NN-based adaptive dynamic surface tracking controller is proposed to achieve the tracking control. By applying the proposed controller, it can be guaranteed that not only the output of the system can track the desired trajectory, but also that the tracking error can converge to a small neighborhood of zero, while all signals of the closed-loop system are bounded. Finally, the effectiveness of the proposed controller is verified through two examples. [ABSTRACT FROM AUTHOR]

Full‐tensor magnetic gradiometry data have been collected commercially for the last few years. However, to date, there is still no clarity on how to compare these data to scalar total field surveys. Some users display the vertical gradient of the vertical component (Bzz) and compare that to a first vertical derivative of total field with the caveat that ‘they are similar’. Others compute the length of the measured vector and call that total field. We establish the basic formulas to calculate total field from the tensor components and demonstrate this with a real data example from Thompson, Manitoba, Canada. Another key question is whether full‐tensor interpolation is required to obtain total field from tensor data. We compare the results from using a commercial full‐tensor interpolation algorithm with standard minimum curvature of the tensor components individually and with another open‐source code that uses a radial basis function interpolator on the individual tensor components. All three applications produced a total field grid of superior quality to that calculated from a scalar total field survey available for the area of study. [ABSTRACT FROM AUTHOR]

The widespread use of low-density polyethylene (LDPE) and high-density polyethylene (HDPE) plastics has resulted in a large amount of waste plastic that requires appropriate disposal or reuse. One potential solution is to use them in the modification of asphalt concrete (AC) mixtures for more sustainable highways. To study this possibility, permanent deformation and dynamic modulus (DM) of the LDPE and HDPE modified AC mixtures was investigated by conducting flow number (FN), flow time (FT) and DM tests on Superpave gyratory compacted specimens. Machine learning models; multi-layer perceptron (MLP), radial basis function neural network (RBFNN), generalized regression neural network (GRNN) and support vector machine (SVM) were used to predict the DM on the basis of frequency and temperature parameters. The model's performance was gauged by analyzing the root mean square error, mean relative error, and coefficient of determination. The study findings revealed that the LDPE and HDPE modified AC mixtures provide 2.07 times and 1.27 times better resistance to permanent deformation, respectively, than their counterpart. It was also found that the LDPE and HDPE modified AC mixtures have 2.1 times and 1.4 times higher DM values, respectively, than the Control AC mixtures. Among the machine learning models, MLP (R2 = 0.98) showed best accuracy in predicting DM and thus is recommended to be used in similar studies due to its robustness. Additionally, the feature importance analysis revealed that frequency has the highest impact on DM predictions, followed by temperature and the inclusion of the LDPE. [ABSTRACT FROM AUTHOR]

A simple, effective and high-precision boundary meshfree method called virtual boundary meshfree Galerkin method (VBMGM) is used to tackle 2D anisotropic heat conduction problems with complex boundaries. Temperature and heat flux are expressed by virtual boundary element method. The virtual source function is constructed through the utilization of radial basis function interpolation. Calculation model diagram and discrete model diagram of real boundaries, and schematic diagram of VBMGM are demonstrated. Using Galekin method and considering boundary conditions, the integral equation and the discrete formula of VBMGM are given in detail. The benefits of the Galerkin, meshfree, and boundary element methods are all presented in VBMGM. Seven numerical examples of general anisotropic heat conduction problems (including three numerical examples with complex boundaries and four numerical examples with mixed boundary conditions) are computed and contrasted with precise solutions and different numerical methods. The computation time of each example is given. The number of degrees of freedom used in many examples is half or less than that of the numerical method being compared. The suggested method has been demonstrated to be effective and high-precision for solving the 2D anisotropic heat conduction problems with complex boundaries. [ABSTRACT FROM AUTHOR]

This research focuses on modeling CO2 absorption into alkanolamine solvents using multilayer perceptron (MLP), radial basis function network (RBF), Support Vector Machine (SVM), networks, and response surface methodology (RSM). The parameters, including solvent density, mass fraction, temperature, liquid phase equilibrium constant, CO2 loading, and partial pressure of CO2, were used as input factors in the models. In addition, the value of CO2 mass flux was considered as output in the models. Trainlm, trainbr, and trainscg algorithms trained the networks. The results showed that the best number of neurons for MLP with one layer is 16; with two layers, 5 neurons in the first layer and 12 neurons in the second layer; and with three layers, 9 neurons in the first layer, 5 neurons in the second layer, and 1 neuron in the third layer. The best spread in RBF was found to be 2.202 for optimal network performance. Furthermore, statistical data analysis revealed that the trainlm function performs best. The coefficients of determination for RSM, MLP, RBF, and SVM for optimized structures are obtained at 0.9802, 0.9996, 0.9940, and 0.8946, respectively. The results demonstrate that MLP and RBF networks can model CO2 absorption using the trainlm, trainbr, and trainscg algorithms. [ABSTRACT FROM AUTHOR]

Nanoparticles impose multidimensional effects on living cells that significantly vary among different studies. Machine learning (ML) methods are recommended to elucidate more consistence and predictable relations among the affected parameters. In this study, nine ML algorithms [Support-Vector Regression (SVR), Linear, Bagging, Stochastic Gradient Descent (SGD), Gaussian Process, Random Sample Consensus (RANSAC), Partial Least Squares (PLS), Kernel Ridge, and Random Forest] were applied to evaluate their efficiency in predicting the effects of zinc oxide nanoparticles (ZnO NPs: 0.5, 1, 5, 25, and 125 µM) and microparticles (ZnO MPs: 1, 5, 25, and 125 µM) on Carum copticum. The plant root/shoot biomass; number of leaves, branches, umbellates, and flowers; protein content; reducing sugars; phenolic compounds; chlorophylls (a, b, Total); carotenoids; anthocyanins; H2O2; proline; malondialdehyde (MDA); tissue zinc content; superoxide dismutase (SOD) activity; and media ΔpH were measured and considered input variables. All levels of ZnO MPs treatments increased growth parameters compared to the control (ZnSO4). The highest shoot/root fresh and dry mass were recorded at 5 µM ZnO MPs compared with the control. The root fresh/dry mass under ZnO NPs treatments was more sensitive than shoot parameters. The number of flowers increased by 134 and 79% in MPs and NPs treatments compared to the control, respectively. ZnO NPs reduced protein content by up to 81% in 125 µM NPs compared to ZnSO4. Reducing sugar content increased to 25, 40 and 36% in 5, 25, 125 µM MPs and 67, 68, 26, 26 and 21% in 0.5, 1, 5, 25 and 125 µM NPs treatments, respectively. The pH alteration was more significant under NPs and affected zinc uptake. All levels of ZnO NPs treatments increased growth parameters compared to the control. All ML algorithms showed varied efficiencies in predicting the nonlinear relationships among parameters, with higher efficiency in predicting the behavior of root and shoot dry mass, root fresh weight and number of flowers according to R2 index. The model obtained from SVR with the radial basis function (RBF) kernel was selected as a comprehensive model for predicting and determining the efficacy of the results. [ABSTRACT FROM AUTHOR]

This paper introduces a novel leader–follower formation control strategy for autonomous vehicles, aimed at achieving precise trajectory tracking in uncertain environments. The approach is based on a graph guidance law that calculates the desired yaw angles and velocities for follower vehicles using the leader's reference trajectory, improving system stability and predictability. A key innovation is the development of a Neural Adaptive Prescribed Performance Controller (NA-PPC), which incorporates a Radial Basis Function Neural Network (RBFNN) to approximate nonlinear system dynamics and enhances disturbance estimation accuracy. The proposed method enables high-precision trajectory tracking and formation maintenance under random disturbances, which are vital for autonomous vehicle logistics and detection technologies. Leveraging a graph-based guidance law reduces control complexity and improves robustness against external disturbances. The inclusion of second-order filters and adaptive RBFNNs further enhances nonlinear error handling, improving control performance, stability, and accuracy. The integration of guidance laws, leader–follower control strategies, backstepping techniques, and RBFNNs creates a robust formation control system capable of maintaining performance under dynamic conditions. Comprehensive computer simulations validate the effectiveness of this controller, highlighting its potential to advance autonomous vehicle formation control. [ABSTRACT FROM AUTHOR]

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents a fixed‐time state observer‐based robust adaptive neural fault‐tolerant control (RANFTC) for attitude and altitude tracking and control of quadrotor unmanned aerial vehicles (UAVs), considering multiple actuator faults, parametric uncertainty, and unknown external disturbances simultaneously. A novel fixed‐time state error estimation based on sliding mode observer is designed, which is independent of initial conditions. A proportional–integral–derivative (PID) based sliding mode control (SMC) is proposed to handle actuator faults and unknown disturbances in combination with the fixed‐time observer within the fault‐tolerant control (FTC) design scheme. The radial basis function neural network (RBFNN) is employed with the controller to approximate the uncertain parameters of the system. Furthermore, two new adaptive laws are designed to estimate the sudden actuator fault and the unknown upper bound of disturbances independently. Implementing these estimation schemes avoids overestimation, enhances the robustness of the presented controller, and substantially eliminates the control chattering problem. By applying the Lyapunov stability concept, the suggested control strategy guarantees that the states of the quadrotor UAV converge to the origin in a finite time. Finally, simulation studies are conducted to demonstrate the tracking performance and highlight the effectiveness of the proposed FTC design compared to the existing FTC methods. [ABSTRACT FROM AUTHOR]

Soil thermal conductivity in the near-phase-transition zone is a key parameter affecting the thermal stability of permafrost engineering and its catastrophic thermal processes. Therefore, accurately determining the soil thermal conductivity in this specific temperature zone has important theoretical and engineering significance. In the present work, a method for testing the thermal conductivity of fine sandy soil in the near-phase-transition zone was proposed by measuring thermal conductivity with the transient plane heat source method and determining the volumetric specific heat capacity by weighing unfrozen water contents. The unfrozen water content of sand specimens in the near-phase-transition zone was tested, and a corresponding empirical fitting formula was established. Finally, based on the testing results, temperature variation trends and parameter influence laws of thermal conductivity in the near-phase-transition zone were analyzed, and thermal conductivity prediction models based on multiple regression (MR) and a radial basis function neural network (RBFNN) were also established. The results show the following: (1) The average error of the proposed test method in this work and the reference steady-state heat flow method is only 7.25%, which validates the reliability of the proposed test method. (2) The variation in unfrozen water contents in fine sandy soil in the range of 0~−3 °C accounts for over 80% of the variation in the entire negative temperature range. The unfrozen water content and thermal conductivity curves exhibit a similar trend, and the near-phase-transition zone can be divided into a drastic phase transition zone and a stable phase transition zone. (3) Increases in the thermal conductivity of fine sandy soil mainly occur the drastic phase transition zone, where these increases account for about 60% of the total increase in thermal conductivity in the entire negative temperature region. With the increase in density and total water content, the rate of increase in thermal conductivity in the drastic phase transition zone gradually decreases. (4) The R2, MAE, and RSME of the RBFNN model in the drastic phase transition zone are 0.991, 0.011, and 0.021, respectively, which are better than those of the MR prediction model. [ABSTRACT FROM AUTHOR]

Customer retention is a key priority for mobile telecommunications companies, as acquiring new customers is significantly more costly than retaining existing ones. A major challenge in this field is predicting customer churn—users discontinuing services. Traditional predictive models such as rule-based systems often struggle with the complex, non-linear nature of customer behavior. To address this, we propose the use of deep learning techniques, specifically multi-layer perceptron (MLP) and radial basis function (RBF) networks, to improve the accuracy of churn predictions. However, while neural networks excel in predictive performance, they are often criticized for being "black-box" models, lacking interpretability. A real-world data set is considered, which originally contained information about 15,000 randomly selected clients. Various network structures and configurations are analyzed. The obtained results are compared with results generated using fuzzy rule-based and rough-set rule-based systems. The MLP model achieved an almost perfect accuracy of 0.999 with an F-measure of 0.989 , outperforming traditional methods such as fuzzy rule-based and rough-set systems. Although the RBF model slightly lagged in accuracy, it demonstrated a superior recall of 0.993 , indicating better identification of potential churners. These results demonstrate that neural network models significantly enhance predictive performance in churn modeling. The interpretability of the model is also discussed since it bears significance in real applications. Our contribution lies in showing that deep learning methods significantly enhance churn prediction accuracy, though the challenge of model interpretability remains a critical area for future work. [ABSTRACT FROM AUTHOR]

Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly used technique for distinguishing pure milk from adulterated milk, even when it comes from different animal species. More recently, portable spectrometers have enabled in situ analysis with analytical performance comparable to that of benchtop instruments. Partial Least Square (PLS) analysis is the most popular tool for developing calibration models, although the increasing availability of portable near infrared spectroscopy (NIRS) has led to the use of alternative supervised techniques, including support vector machine (SVM). The aim of this study was to develop and implement a method based on the combination of a compact and low-cost Fourier Transform near infrared (FT-NIR) spectrometer and variable cluster–support vector machine (VC-SVM) hybrid model for the rapid classification of milk in accordance with EU Regulation EC No. 1308/2013 without any pre-treatment. The results obtained from the external validation of the VC-SVM hybrid model showed a perfect classification capacity (100% sensitivity, 100% specificity, MCC = 1) for the radial basis function (RBF) kernel when used to classify whole vs. not-whole and skimmed vs. not-skimmed milk samples. A strong classification capacity (94.4% sensitivity, 100% specificity, MCC = 0.95) was also achieved in discriminating semi-skimmed vs. not-semi-skimmed milk samples. This approach provides the dairy industry with a practical, simple and efficient solution to quickly identify skimmed, semi-skimmed and whole milk and detect potential fraud. [ABSTRACT FROM AUTHOR]

Buffalo meat is naturally perishable, making it susceptible to spoilage due to its high moisture content and vulnerability to microbial contamination. Edible coatings have attracted attention as a packaging method that can prolong the shelf life of meat. The study aimed to examine the impact of a combination of Lepidium sativum mucilage (LS) coating and propolis extract (PE) on prolonging the shelf life of buffalo meat. The chemical characteristics (chemical compounds, total phenol content (TPC), total flavonoid content (TFC), antioxidant activity, and cytotoxicity) and antimicrobial activity of the PE (disk diffusion agar, well diffusion agar, minimum inhibitory concentration, and minimum bactericidal concentration) were investigated. The effect of the PE on the cell wall of pathogenic bacteria was examined using a scanning electron microscope. Biological properties of LS (TPC, TFC, antioxidant activity and antimicrobial effect (pour plate method)) was investigated. Different concentrations of PE (0, 0.5, 1.5, and 2.5%) were added to the coating mixture containing LS, and their effects on extending the shelf life of buffalo meat samples stored at 4°C for 9 days were assessed. The PE included gallic acid, benzoic acid, syringic acid, 4–3 dimethoxy cinnamic acid, p-coumaric acid, myricetin, caffeic acid, luteolin, chlorogenic acid, and apigenin. The PE was determined to have a TPC of 36.67 ± 0.57 mg GAE/g and a TFC of 48.02 ± 0.65 mg QE/g. The extract's radical scavenging activity ranged from 0 to 76.22% for DPPH radicals and from 0 to 50.31% for ABTS radicals. The viability of C115 HeLa cell was observed to be 94.14 μg/mL. The PE and LS, exhibited strong antimicrobial properties against pathogenic bacteria. The LS was determined to have a TPC of 15.23 ± 0.43 mg GAE/g and a TFC of 11.51± 0.61 mg QE/g. The LS was determined to have a DPPH of 429.65 ± 1.28 μg/mL and a ABTS of 403.59 ± 1.46 μg/mL. The microbiological analysis revealed that the LS+2.5%PE treatment was the most effective in inhibiting the growth of total viable count (6.23 vs. 8.00 log CFU/g), psychrotrophic bacteria count (3.71 vs. 4.73 log CFU/g), coliforms count (2.78 vs. 3.70 log CFU/g), and fungi count (2.39 vs. 3.93 log CFU/g) compared to the control sample. The addition of PE to the edible coating also demonstrated a concentration-dependent effect on preserving the moisture, pH, color, and hardness of the buffalo meat. Sensory evaluation results suggested that incorporating PE into the edible coating extended the shelf life of buffalo meat by three days. In the second stage of this paper, this investigation employed two distinct forecasting methodologies: the Radial Basis Function (RBF) and the Support Vector Machine (SVM), to predict a range of quality indicators for coated meat products. Upon comparison, the RBF model exhibited a higher level of accuracy, showcasing its exceptional capacity to closely match the experimental outcomes. Therefore, this type of food coating, renowned for its strong antimicrobial properties, has the potential to effectively package and preserve perishable and delicate food items, such as meat. [ABSTRACT FROM AUTHOR]

Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this article, the adaptive fixed‐time prescribed performance (FTPP) regulation is investigated for a class of time‐varying state constrained switched stochastic systems with input delay. The time‐varying barrier Lyapunov function and a compensation system are presented, respectively, to deal with the design problems caused by the existence of both time‐varying state constraints and input delay. Some radial basis function neural networks are used to approximate unknown functions, and the common Lyapunov function method is displayed to handle the switched signals. Besides, by designing a fixed‐time prescribed performance function, the desired adaptive neural controller is constructed. Compared with the existing works for state constrained control problem, the FTPP regulation control scheme is first proposed for time‐varying state constrained stochastic switched systems under input delay, and the adaptive dynamic surface control scheme with the nonlinear filter is designed to solve the problem of “explosion of complexity.” Based on the stochastic stability theory, the FTPP of system output is achieved, other system state variables are restricted in the predefined regions, and all signals of this closed‐loop system remain bounded in probability. Finally, the availability of the proposed control scheme is illustrated via two simulation examples. [ABSTRACT FROM AUTHOR]

With the advancement of wireless networks, many mobile applications are becoming increasingly popular. These applications, which include real-time vision processing, intelligent homes, accurate tracking, and traffic monitoring, frequently necessitate an excellent experience that requires expensive computational resources. Video analytics on mobile devices necessitate a significant amount of computational power, resulting in longer processing times. Even though mobile device (MD) performance has steadily improved, running all programs on a single MD still results in excessive energy consumption and delay. The problem can be partially solved by outsourcing processing to the cloud, but uploading videos take a long time. Mobile edge computing can transfer processing to close-by edge servers to lower latency (MEC). Despite this, the performance of video analytics must be improved because edge server processing resources are frequently limited and highly dynamic. This paper aims to maximize utility, a weighted function of frame rate and precision. The lack of server and network expertise and the ever-changing system environment represent formidable obstacles to fixing this issue. However, current resource offloading systems concentrate mainly on average-based performance indicators, so missing the resource's deadline limitation. This research presents a resource-aware offloading video analytics in mobile edge computing and a resource-aware offloading algorithm (ROA) using the radial basis function networks (RBFN) method for enhancing the reward under the resource's deadline restriction. Brute-force search is then used to simplify the computation, with concave processing and convex optimization improving resource allocation and user association. According to extensive experimental data, RBFN–OROA outperforms the comparison algorithms in all parameter settings, demonstrating its resistance to MEC environment state changes. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid method for controlling bidirectional power sharing among grids and dc microgrids. The interlinking converter (IC) and current-fed dual active bridge (CFDAB) converters connect the grid and dc microgrid. The proposed approach is the joint implementation of golden eagle optimization and radial basis function neural network, hence, named the GEO-RBFNN approach. The bidirectional power distribution control signal analysis significantly diminishes power distribution deviation and circulating current. This power-sharing control system neglects the need for battery backup since the required amount of power is brought from or handled by an AC utility. The dc microgrid incorporates the dc to dc converter, from which boost converters use sources and buckconverters use supply loads. The control of bidirectional power sharing among the microgrids and the grid is attained with the aid of the GEO-RBFNN algorithm. It delivers certain aids, like higher predictability, highly reliable results, high speed, and less complexity. The outcome is investigated on the MATLAB platform. According to the comparison outcome, the proposed method delivers higher performance than the existing system. The proposed method gives higher comparison outcomes than the current methods, which can conquer the associated problems. The proposed method-based settling time is 3.356 ms, and the rise time is 1.555 ms. The efficiency based on the proposed method is 97.54%, which is higher than the existing methods. [ABSTRACT FROM AUTHOR]

In the actual marine environment, the hydrodynamic characteristics of floating counter-rotating tidal turbines (FCRTTs) are influenced by the motion responses of their carrier platforms. Therefore, accurately analyzing and predicting hydrodynamic loads under the motions of FCRTTs are crucial. In this paper, a fitting formula for hydrodynamic loads of FCRTTs applicable to rotational motion is derived. Then, the effects of surge amplitude, surge frequency, and tip speed ratio on the hydrodynamic loads of an FCRTT are also calculated. It is found that the instantaneous load fluctuation of the rear rotor is more severe than that of the front rotor. However, the average torque of both rotors is similar, which can effectively enhance the operational stability of the FCRTT. Additionally, the hydrodynamic loads are decomposed into average hydrodynamic force, damping force, and added mass force based on the least squares method. A fitting formula for the hydrodynamic loads applicable to different surge conditions is derived, incorporating 11 hydrodynamic coefficients. The results indicate that the damping coefficients n P 0 and n T 0 play a dominant role in the fluctuation amplitude of the hydrodynamic loads. Finally, an effective and fast prediction model for various hydrodynamic coefficients is successfully established using the three-dimensional radial basis function. The relative errors between the predicted peak values of all performance coefficients and the values calculated using the computational fluid dynamics (CFD) method are within −3.5%. This paper provides important guidance for engineering design and load prediction of FCRTTs. Moreover, the predictive methodology can be extended for application to other single-degree-of-freedom and couple motions. [ABSTRACT FROM AUTHOR]

Mesh deformation technology is widely used in aerodynamic applications like unsteady flow, aeroelasticity, and aerodynamic shape optimization because of its low computational costs and consistent mesh connectivity. In order to raise deformed mesh quality and improve efficiency, a new mesh deformation method based on quaternion and displacement normal propagation (named QN method) is introduced in this paper. The boundary points propagate their displacements composed of translational vectors and quaternions to corresponding volume points along the normal direction under the control of the damping function, which preserves the mesh shape and guarantees the quality near boundaries, including orthogonality and normal size. It can also prevent the volume points from being interfered by other less relevant boundary points, so dealing with complex displacement fields effectively. In addition, it avoids complicated matrix and interpolation operations, thus saving lots of computational costs. For mesh with complex topology, a hybrid method combining the QN method and the radial basis function method (RBF method) is investigated to broaden application scenarios, which are applied to the mesh with normal correspondence inside the boundary layer and the mesh outside, respectively. Benefitting from effective handling for the near-wall elements by the QN method, the RBF interpolation part in the hybrid method requires minor support points to carry out valid large deformation, improving the deformation efficiency greatly compared to the individual RBF method. Five typical test cases with different deformation modes and mesh characteristics are implemented, showing better performance of the proposed method in deformed mesh quality and deformation efficiency. [ABSTRACT FROM AUTHOR]