Sum Of Squared Errors Research Articles

A Novel k-Means Framework via Constrained Relaxation and Spectral Rotation.

Owing to its simplicity, the traditional k - means (Lloyd heuristic) clustering method plays a vital role in a variety of machine-learning applications. Disappointingly, the Lloyd heuristic is prone to local minima. In this article, we propose k - mRSR, which converts the sum-of-squared error (SSE) (Lloyd) into a combinatorial optimization problem and incorporates a relaxed trace maximization term and an improved spectral rotation term. The main advantage of k - mRSR is that it only needs to solve the membership matrix instead of computing the cluster centers in each iteration. Furthermore, we present a nonredundant coordinate descent method that brings the discrete solution infinitely close to the scaled partition matrix. Two novel findings from the experiments are that k - mRSR can further decrease (increase) the objective function values of the k - means obtained by Lloyd (CD), while Lloyd (CD) cannot decrease (increase) the objective function obtained by k - mRSR. In addition, the results of extensive experiments on 15 datasets indicate that k - mRSR outperforms both Lloyd and CD in terms of the objective function value and outperforms other state-of-the-art methods in terms of clustering performance.

Utilizing VSWIR spectroscopy for macronutrient and micronutrient profiling in winter wheat

This study explores the use of leaf-level visible-to-shortwave infrared (VSWIR) reflectance observations and partial least squares regression (PLSR) to predict foliar concentrations of macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), micronutrients (boron, copper, iron, manganese, zinc, molybdenum, aluminum, and sodium), and moisture content in winter wheat. A total of 360 fresh wheat leaf samples were collected from a wheat breeding population over two growing seasons. These leaf samples were used to collect VSWIR reflectance observations across a spectral range spanning 350 to 2,500 nm. These samples were then processed for nutrient composition to allow for the examination of the ability of reflectance to accurately model diverse chemical components in wheat foliage. Models for each nutrient were developed using a rigorous cross-validation methodology in conjunction with three distinct component selection methods to explore the trade-offs between model complexity and performance in the final models. We examined absolute minimum predicted residual error sum of squares (PRESS), backward iteration over PRESS, and Van der Voet’s randomized t-test as component selection methods. In addition to contrasting component selection methods for each leaf trait, the importance of spectral regions through variable importance in projection scores was also examined. In general, the backward iteration method provided strong model performance while reducing model complexity relative to the other selection methods, yielding R2 [relative percent difference (RPD), root mean squared error (RMSE)] values in the validation dataset of 0.84 (2.45, 6.91), 0.75 (1.97, 18.67), 0.78 (2.13, 16.49), 0.66 (1.71, 17.13), 0.68 (1.75, 14.51), 0.66 (1.72, 12.29), and 0.84 (2.46, 2.20) for nitrogen, calcium, magnesium, sulfur, iron, zinc, and moisture content on a wet basis, respectively. These model results demonstrate that VSWIR reflectance in combination with modern statistical modeling techniques provides a powerful high throughput method for the quantification of a wide range of foliar nutrient contents in wheat crops. This work has the potential to advance rapid, precise, and nondestructive field assessments of nutrient contents and deficiencies for precision agricultural management and to advance breeding program assessments.

Utilising date palm fibres as a permeable reactive barrier to remove methylene blue dye from groundwater: a batch and continuous adsorption study.

This study aimed to utilise cheap and abundantly available date palm fibre (DPF) wastes for the remediation of methylene blue (MLB) dye-contaminated groundwater. The DPF adsorbents were first prepared, followed by various characterisation analyses, including surface morphology, functional groups, and material structure. Subsequently, the DPF adsorbents were applied in the batch and continuous adsorption studies to assess the MLB dye removal from aqueous environments. The batch adsorption study achieved 98% maximum removal efficiency with a contact time, adsorbents dosage, initial pH, temperature, particle size, initial dye concentration, and agitation speed of 105min, 3g/L, 7.0, 45°C, 0.075mm, 50mg/L, and 150rpm, respectively. Langmuir was the best-fitted isotherm model depending on a higher correlation coefficient (R2 = 0.985), with a maximum monolayer dye adsorption capacity (qmax) of 54.204mg/g. Additionally, the second order was the best-fitted kinetic model (R2 = 0.990), indicating that MLB dye was removed through chemisorption. Besides, the positive enthalpy change (ΔH°) and negative Gibb's free energy (ΔG°) values verified the endothermic process and spontaneous adsorption. According to the impact analysis of initial dye concentrations and flow rates on the permeable reactive barrier (PRB) performance in the continuous adsorption study using the Thomas, Belter, and Yan models, the experimental results and predicted breakthrough curves reflected an excellent agreement (R2 ≥ 0.8767) and a sum of squared errors (SSE) ≤ 0.4834. In short, the results demonstrated DPF as an effective adsorbent material in PRB technology.

CONSTRUCTION OF A THREE LEVEL SPLIT-LOT DESIGNS IN SUDOKU SQUARE DESIGN STRUCTURE

In this research, a situation where blocking is required for a split lot design in order to tease out noise from the dependent variable (prevent factors other than that of interest influencing the outcome) was considered. Blocking for every stage of the design is administered, an elaborate construction procedure for the design was developed by infusing the ordinary split-lot design into a Sudoku Square design structure, the hybridisation gave it a convenient structure for the research. The linear model and the sum of squares of the design were derived, the ANOVA table was constructed and the table was used to analyse the whole system. The clear advantage of this design has been observed to be the additional source of variation, because the introduction of the Block Sum of Squares will reduce the Error Sum of Squares as a result makes it more efficient, but the fact that precision and cost are both functions of the number of sublots per step as well as the total number of items, comparison is complicated. The new design provides greater precision for main effects.

Application of chaotic teaching-learning-based optimization technique for estimating unknown parameters of proton exchange membrane fuel cell model.

Proton exchange membrane fuel cells (PEMFC) possess features like high specific power density, low operating temperature, and low operating pressure and thus are most widely used. The performance of PEMFC highly depends on its output voltage which further affects its efficiency. This research paper aims to improve this output voltage by minimizing the losses through parameter estimation technique for three well-known commercial PEMFC stacks, namely, 250W, BCS 500W, and Ballard Mark V. The multiobjective function framed for optimization serves two goals. One is to extract the unknown parameters of FC, and second is to achieve the minimum sum of squared errors (SSE) that occur due to the difference between experimental voltage and estimated voltage. Chaotic teaching-learning-based optimization (CTLBO) algorithm is used for optimization process. SSE values obtained for three commercial PEMFC stacks 250W, BCS 500W, and Ballard Mark V are 7.02267, 4.00150, and 0.8100, respectively. The applicability of this technique is further checked for different operating conditions of each model. The I-P (current-power) curve and I-V (current-voltage) curve obtained using estimated data closely matched the experimental data that showed the practical relevance and efficacy of the algorithm. A comparison is done among the SSE results obtained using CTLBO to other competitive algorithms mentioned in the literature. CTLBO showed its superiority over other algorithms in estimating the unknown parameters of PEMFC stack parameters.

The role of the O2O blended teaching model in improving the teaching effectiveness of physical education classes

Abstract The deep fusion of Internet technology and education is constantly pushing forward the reform of university education. Traditional educational ideas, concepts, and models cannot keep pace with the times, and hybrid teaching has become a new way of education in colleges and universities. To improve the teaching effect of physical education classes, the study used a blended teaching model and designed a teaching evaluation and performance prediction model under the blended teaching model based on an improved cluster analysis method and attention mechanism. The lab results indicated that under the blended teaching model, students’ performance increased by 12.89 points, and the level of skill mastery and proficiency increased by 26.52 and 28.55%, respectively, with grades more inclined to high score distribution. “Excellent” grade clustering increased by 77.71%, and “Good” grade clustering increased by 19.01%. The minimum error sum of squares of the improved clustering algorithm was 58.18 and 36.25% lower than the other two algorithms, and the clustering results were more relevant. The two-way attention mechanism algorithm predicted higher accuracy results and performed best on all four evaluation metrics, with a prediction accuracy of 98.23%, an accuracy of 98.42%, and an F1 value of 91.78%. This hybrid teaching model is more in line with the characteristics of the physical education teaching discipline, successfully cultivates students’ independent learning ability, stimulates students’ love for physical education courses, and achieves better teaching results.

Joint Beamforming Design and User Clustering Algorithm in NOMA-Assisted ISAC Systems.

To enhance the performance of non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC) systems in multi-user distributed scenarios, an improved Gaussian Mixture Model (GMM)-based user clustering algorithm is proposed. This algorithm is tailored for ISAC systems, significantly improving bandwidth reuse gains and reducing serial interference. First, using the Sum of Squared Errors (SSE), the algorithm reduces sensitivity to the initial cluster center locations, improving clustering accuracy. Then, direction weight factors are introduced based on the base station position and a penalty function involving users' Euclidean distances and sensing power. Modifications to the EM algorithm in calculating posterior probabilities and updating the covariance matrix help align user clusters with the characteristics of NOMAISAC systems. This improves users' interference resistance, lowers decoding difficulty, and optimizes the system's sensing capabilities. Finally, a fractional programming (FP) approach addresses the non-convex joint beamforming design problem, enhancing power and channel gains and achieving co-optimizing sensing and communication signals. The simulation results show that, under the improved GMM user clustering algorithm and FP optimization, the NOMA-ISAC system improves user spectral efficiency by 4.3% and base station beam intensity by 5.4% compared to traditional ISAC systems.

Extracting optimal fuel cell parameters using dynamic Fick’s Law algorithm with cooperative learning strategy and k-means clustering

This article introduces an enhanced stochastic search method tailored for optimizing the parameters of fuel cells (FCs), which hold significant relevance across various applications. The nonlinear nature of FCs poses a modeling challenge, prompting the proposal of an advanced Dynamic Fick’s Law Algorithm (DFLA). This improved approach incorporates a cooperative learning strategy, leveraging K-Means clustering, to derive optimal FC parameters. DFLA introduces a dynamic swarm topology by segmenting the population into subswarms, boosting diversity, and enabling broader global exploration. Simultaneously, the cooperative learning strategy fosters information exchange among subswarms, enhancing the algorithm’s ability to explore vast solution spaces and exploit diverse subswarm-derived solutions. The evaluation of DFLA utilized real-world datasets from commercial PEMFC stacks: 250-W stack, BCS 500-W, and NedStack PS6. Performance assessment relied on the Sum Squared Error (SSE), a standard evaluation metric, with comparisons drawn against established competing methods. The results underscored DFLA’s superior efficiency, showcasing improved performance metrics and convergence behaviors compared to the tested algorithms.

Optimal parameter identification of solid oxide fuel cell using modified fire Hawk algorithm

An accurate and efficient approach is required to identify the unknown parameters of solid oxide fuel cell (SOFC) mathematical model for a robust design of any energy system considering SOFC. This research study proposes a modified fire hawk algorithm (MFHA) to determine the values of SOFC model parameters. The performance evaluation of MFHA is tested on two case studies. Firstly, the performance of MFHA is tested on commercially available cylindrical cell developed by Siemens at four temperatures. Results reveal that the least value of sum of squared error (SSE) is 1.04E−05, 2.30E−05, 1.03E−05, and 1.60E−05 at 1073 K, 1173 K, 1213 K, and 1273 K respectively. Results obtained using MFHA have been compared with original fire hawk algorithm (FHA) and other well established and recent algorithms. Secondly, MFHA is implemented for estimating unknown parameters of a 5 kW dynamic tabular stack of 96 cells at various pressures and temperatures. The obtained value of SSE at different temperatures of 873 K, 923 K, 973 K, 1023 K and 1073 K is 1.18E−03, 6.12E−03, 2.21E−02, 5.18E−02, and 6.00E−02, respectively whereas, SSE at different pressures of 1 atm, 2 atm, 3 atm, 4 atm, and 5 atm is 6.05E−02, 6.11E−02, 5.53E−02, 5.11E−02, and 6.64E−02 respectively.

Hybrid sea surface temperature inversion model for the South China sea based on IMLP and DBN

ABSTRACT Sea surface temperature (SST) is a key variable in the study of the global climate system and one of the important parameters in the process of air–sea interaction. Therefore, the demand for SST is developing towards high quality and high precision. In this paper, the ability of the multi-layer perceptron (MLP) optimized by the Aquila optimizer (AO) to solve nonlinear problems and the advantages of the deep belief network (DBN) to effectively process complex data are used to construct the IMLP-DBN inversion algorithm. The algorithm takes into account the influences of atmospheric conditions and satellite zenith angles. The data set is the infrared remote sensing data of the moderate resolution imaging spectroradiometer (MODIS) and the actual measurement data of buoys on sunny days and few clouds. Analysis of the inversion results shows that the root mean square error (RMSE) of the inversion value and the measured value is 0.14, and the sum of square errors (SSE) is 0.78. Compared with the MOD28 product data, the RMSE and SSE of the inversion are reduced by 66% and 24%, respectively.

Prediction of Heteroscedastic Sports Data By using Regression and Various Machine Learning Models

This study investigates the impact of extra deliveries—wide’s, no balls, byes, and leg byes—on T20I cricket match outcomes. It aims to find out whether these extras have a significant effect on the result and which type contributes the most runs. Using heteroscedastic data from the previous three T20I World Cups (20212024), the analysis underlines the importance of reducing extras to boost a team's chances of winning. OLS assumes that the error component has constant variance; when this assumption is violated, heteroscedasticity arises, resulting in incorrect predictions and conclusions. This study employs several types of machine learning techniques to increase prediction accuracy for heteroscedastic data. We applied machine learning models such as Decision Tree, Random Forest, Gradient Boosting, and K Nearest Neighbor, as well as Linear Regression, to predict values in test datasets. Machine learning models typically outperformed linear regression, resulting in decreased sum of squares errors, with some models producing predictions that were very close to observed values. This study shows that machine learning works well with heteroscedastic data. DOI: https://doi.org/10.52783/pst.836

Parameters optimization of PEMFC model based on gazelle optimization algorithm

Fuel cells (FCs) play a crucial role in converting stored hydrogen energy into electricity. However, accurately modeling and optimizing their performance is challenging due to the lack of critical parameter data—specifically, seven key parameters including four semi-empirical coefficients (ξ1, ξ2, ξ3, ξ4), an adjusting parametric coefficient (β), a constant equivalent resistance (Rc), and an adjustable parameter (λ). These essential parameters are typically not provided in manufacturers' datasheets, creating a significant gap in the precise calibration and optimization of Proton Exchange Membrane Fuel Cells (PEMFCs).This study addresses this gap by applying seven population-based meta-heuristic algorithms to estimate and optimize these unknown parameters. Among these, the Gazelle Optimization Algorithm (GOA) is identified as particularly effective, offering superior precision and rapid convergence. Our research evaluates the performance of these algorithms using indicators such as Standard Deviation (StD) and Sum of Squared Errors (SSE). The GOA achieved exceptionally low SSE values of 7.637606 × 10^-3, 1.28694222 × 10−2, and 2.288128 for the Horizon 500W, BCS 500W, and NedStack PS6 stacks, respectively, along with corresponding StD values of 2.275703 × 10−9, 9.12077649 × 10−15, and 3.26518838 × 10−14. These results underscore the algorithm's accuracy and effectiveness in optimizing PEMFC parameters, closely aligning with the manufacturers' polarization curves.The study's findings, validated across these three different fuel cell stacks, highlight the GOA's superiority over other methods in terms of accuracy and convergence speed. This manuscript contributes to the field by providing a robust method for accurately optimizing PEMFC parameters, which are critical for enhancing the overall performance of fuel cells. The results also demonstrate the GOA's potential as a superior optimization tool in the field of fuel cell technology.

A hybrid genetic-fuzzy ant colony optimization algorithm for automatic K-means clustering in urban global positioning system

This paper introduces an innovative automatic K-means clustering algorithm, namely HGA-FACO, which seamlessly integrates the noise algorithm, Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Adaptive Fuzzy System (AFS). The rationale behind the HGA-FACO algorithm is to mitigate the shortcomings of traditional K-means, particularly the reliance on pre-determined cluster centers and the need for specifying the number of clusters in advance. By optimizing the search strategy, HGA-FACO efficiently circumvents local optima and effectively explores the global optimal solution, resulting in more accurate and stable clustering outcomes. To validate the superiority of the HGA-FACO over conventional K-Means Clustering (KMeans) and other intelligent clustering approaches such as ACO-KMeans, GA-KMeans (GAK), particle swarm optimization KMeans (PSOK), and ACO-GAK, we conducted comprehensive experiments on taxi Global Positioning System (GPS) datasets sourced from four distinct cities. Employing rigorous evaluation metrics including Silhouette Coefficient (SC), Partition Coefficient (PBM), Davies-Bouldin Index (DBI), and Sum of Squared Errors (SSE), the experimental results convincingly demonstrate that the HGA-FACO significantly outperforms its counterparts across all metrics, highlighting its exceptional performance in clustering effectiveness and compactness. While the HGA-FACO faces challenges related to computational complexity and the necessity for initial parameter tuning, its performance limitations on small-sized or unevenly distributed datasets are acknowledged. Nevertheless, the algorithm's advancements in the field of clustering algorithms are undeniable and hold immense potential for practical applications, notably in city hotspot identification.

Combining ACE, PLS-R, and SVM-R for rapid detection of adulteration in saffron samples by diffuse reflectance infrared fourier transform spectroscopy

Accurate estimation of saffron purity is essential for ensuring product integrity, particularly in the presence of common chemical adulterants such as tartrazine, sunset yellow, and quinoline yellow. This study introduces the alternating conditional expectations (ACE) algorithm, coupled with diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), as an innovative chemometric approach to address this challenge. The ACE algorithm utilizes optimal transformations for both independent and dependent variables, revealing non-linear relationships and maximizing linear effects between transformed variables. To prepare the spectral data for ACE modeling, we will perform moving average smoothing and Standard Normal Variate (SNV) transformation for preprocessing. Subsequently, we will employ the duplex algorithm to select both calibration and prediction sets from the preprocessed data matrix. The effectiveness of the ACE model was evaluated using several metrics, including the coefficient of determination (R2), adjusted R-squared (R2adj), sum of squared errors (SSE), Regression Sum of Squares (SSR) and mean squared error (MSE). ACE model exhibited excellent performance in determining saffron content even in the presence of adulterants like tartrazine, sunset yellow, and quinoline yellow, on both calibration and prediction sets. Our model achieved excellent performance on both datasets. On the calibration set, a high R2 value (0.9951) indicates a strong correlation between predicted and observed saffron content in the model. The R2adj (0.9950) further strengthens this conclusion by accounting for model complexity, suggesting the model avoids overfitting. These findings are complemented by robustness measures. The statistical parameters of the ACE model, such as total sum of squares (SST) (1.103∗104), SSR (1.098∗104), SSE (54.414), and MSE (0.878), were calculated to compare the performance of the developed methods for determining Saffron's concentration in each mixed sample.

Radial Basis Function (RBF) and Multilayer Perceptron (MLP) Comparative Analysis on Building Renovation Cost Estimation: The Case of Greece

Renovation of buildings has become a major area of development for the construction industry. In the building construction sector, generating a precise and trustworthy cost estimate before building begins is the greatest challenge. Emphasizing the value of using ANN models to forecast the total cost of a building renovation project is the ultimate objective. As a result, building firms may be able to avoid financial losses as long as there is as little discrepancy between projected and actual costs for remodeling works in progress. To address the gap in the research, Greek contractors specializing in building renovations provided a sizable dataset of real project cost data. To build cost prediction ANNs, the collected data had to be organized, assessed, and appropriately encoded. The network was developed, trained, and tested using IBM SPSS Statistics software 28.0.0.0. The dependent variable is the final cost. The independent variables are initial cost, estimated completion time, actual completion time, delay time, initial and final demolition-drainage costs, cost of expenses, initial and final plumbing costs, initial and final heating costs, initial and final electrical costs, initial and final masonry costs, initial and final construction costs of plasterboard construction, initial and final cost of bathrooms, initial and final cost of flooring, initial and final cost of frames, initial and final cost of doors, initial and final cost of paint, and initial and final cost of kitchen construction. The first procedure that was employed was the radial basis function (RBF). The efficiency of the RBFNN model was evaluated and analyzed during training and testing, with up to 6% sum of squares error and nearly 0% relative error in the training sample, which accounted for roughly 70% of the total sample. The second procedure implemented was the method called the multi-layer perceptron (MLP). The efficiency of the MLPNN model was assessed and examined during training and testing; the training sample, which made up around 70% of the overall sample, had a relative error of 0–7% and a sum of squares error ranging from 1% to 5%, confirming specifically the efficacy of RBFNN in calculating the overall cost of renovations.

Effect of Ultrasound and Osmotic Dehydration as Pretreatments on the Infrared Drying of Banana Slices.

There is a growing interest in foods with added nutritional value and extended shelf life. This study investigates the use of infrared technology in the drying of banana slices to improve their stability and quality by minimizing moisture content and water activity. The drying experiments were carried out at a temperature of 70 °C, using the following pretreatments: ultrasound-assisted (UA) immersion in water for 20 and 30 min, osmotic dehydration (OD) and ultrasound-assisted osmotic dehydration (UAOD) for 20, 30 and 40 min. The osmotic process consisted of immersing the samples in the isomaltulose solution (40.0 g/100 g deionized water) for 60 min. All mathematical models used to describe the drying process showed a good fit with high R2 values (>0.98) and low value of the relative mean error E (%), the sum of squared error and the root mean squared error. The Fick's diffusion coefficient (D eff) was higher for the samples previously treated with ultrasound for 20 and 30 min. The ultrasonic treatment resulted in shorter drying times with a reduction in average time of up to 29 %. OD was not efficient in reducing drying time, resulting in samples with lower drying rates. The samples treated with ultrasound showed less isotropic shrinkage and better color parameters. The osmotic process resulted in samples with greater rehydration capacity. The impregnation of a carbohydrate with low glycemic index in banana slices was achieved by the osmotic pretreatment, resulting in a new food product with attractive nutritional properties. This advancement represents not only a significant step in the development of functional foods, but also a major innovation in terms of processing technologies. The OD was combined with infrared drying, a method known for its superior drying rates, high heat transfer coefficient and energy efficiency. The synergy of these promising techniques not only shortens the processing times but also ensures more uniform dehydration of food products, resulting in end products that not only maintain but also optimize their nutritional value. These advances offer innovative solutions to improve food quality and also minimize environmental impact through low-energy technologies such as ultrasound and infrared treatment.

Thin layer drying and effect of temperature on the drying characteristics of bushbuck (Gongronema latifolium) leaves

Determination of the drying properties of agricultural products holds several benefits, such as nutrient preservation, processing and storage. The effect of air temperature on the drying properties and kinetics of bushbuck leaves at 30, 40, 50, 60 and 70 °C temperature was investigated. The drying time declined as the temperature rose, indicating a falling rate drying phenomenon. Drying models such as Henderson and Pabis, Newton, Modified Page, Page, Midilli and others, Logarithmic, and Two-term were applied and fitted to the experimental data's moisture ratio. The non-linear regression fitting method results showed that the Midilli and other models gave the best-fit description for the drying process. The values obtained for the coefficient of determination (R2) were observed to have varied from 0.9948 to 0.9995 for the various drying models evaluated. In contrast, the sum of square error (SSE) and root mean square (RMSE) estimate was observed to vary from 0.0056 to 0.0007 and 0.0310 to 0.0163, respectively. On the estimate for the effective moisture diffusivity (Deff), it was observed that it depends on temperature and extends from 1.33×10−9 to 4.83×10−9 m2/s. The value of the activation energy was 26.05 kJ/mol. From this study, it can be deduced that the Arrhenius model could predict the temperature effect on the drying process of bushbuck leaves.

Effect of ultrasonic pretreatment on textural properties and sensory attributes of cooked faba beans

Drying process extends the shelf-life of fresh faba beans and makes them available all year round. Dried and cooked faba beans are used to make a variety of traditional food products. Ultrasonic pretreatment, as a modern food processing technology, can shorten the drying time of fresh legumes and improve the quality and sensory properties of products. So, the present study aimed to analyze the impact of the ultrasonic treatment process (0, 5, 10, and 15 min, 40 kHz, and 150 W) on the mass transfer rate, drying time, and effective moisture diffusivity (Deff) of fresh faba beans. Also, the effect of ultrasonic treatment on textural properties and sensory attributes of cooked faba beans was studied. By using the ultrasonic process, the rate of water extraction from fresh faba beans, and thus their dehydration rate, can be increased. With increasing the duration of ultrasonic pretreatment from 0 to 15 min, the drying time of fresh faba beans decreased from 250 min to 150 min (p < 0.05). The Deff was calculated by Fick’s second law, and it significantly increased from 0.70 × 10−9 m2 s−1 to 1.05 × 10−9 m2 s−1 when the sonication duration was extended from 0 to 15 min (p < 0.05). The Page model best fitted the drying kinetic of fresh faba beans with a coefficient of determination (r) > 0.9968, and the sum of squared error (SSE) and root mean squared error (RMSE) were also closer to zero compared to other models. The rehydration ratio of dried faba beans (after cooking) significantly increased from 308.4 % to 327.1 % with the extension of processing time from 0 to 15 min (p < 0.05). The maximum and minimum crust hardness and texture firmness values were for the untreated and sonicated samples for 15 min, respectively. The sonication increased the sensory acceptance of the cooked faba beans and the highest appearance, odor, texture, flavor, and overall acceptance were for the 10 min sonicated faba beans.

A new short-term wind power prediction methodology based on linear and nonlinear hybrid models

Fast and accurate wind power prediction is of great significance for grid planning. However, wind power dataset tends to be highly stochastic and volatile, while showing more stable seasonal and cyclical changes, which is a linear and nonlinear superposition features, and it is difficult to use a single linear or nonlinear model to make accurate predictions. Considering these essential features of wind power data, a short-term wind power prediction method based on linear and nonlinear hybrid models is proposed in this paper. First, the complete ensemble empirical mode decomposition (CEEMD) is used to decompose and reconstruct the wind speed and direction in the original dataset to reduce the volatility. Then, to capture the linear features in the dataset, the autoregressive integrated moving average model with exogenous input variables (AIRMAX) is used for linear modeling, and the sparrow search algorithm optimized gated recurrent unit (SSA-GRU) is used to model the nonlinear features. At the same time, to improve the efficiency of model training, federated learning (FL) is utilized for distributed model training. Finally, the ARIMAX and SSA-GRU models are weighted and summed the prediction results to gain the final prediction results using equal weighted averaging (EWA), minimum sum of squares error (MSSE), and maximum grey relational analysis (MGRA), which are commonly used to compute the weights of the combined models, respectively. The wind power dataset from a wind farm in northwest China from January 1, 2022 to October 31, 2022 are used for experiments and analysis, and the results show that the root mean square error (RMSE), R-square (R2), and accuracy (ACC) of the proposed method are 11.944, 0.987, and 0.613, respectively, which are better than all the compared models. The main contribution of this paper is twofold, on the one hand, it indirectly proves that the wind power dataset is formed by the superposition of linear and nonlinear features, and on the other hand, it puts forward a theoretical model and research paradigm for predicting short-term wind power and presents a scientific basis for grid scheduling.

Enhancing parameter identification for proton exchange membrane fuel cell using modified manta ray foraging optimization

In this paper, an accurate model of proton exchange membrane fuel cell (PEMFC) for optimal identification of PEMFC parameters has been developed. The optimization methodology is based on the modified version of Manta Ray Foraging Optimization (MMRFO) technique for minimizing the sum of squared errors (SSE) between the Experimentally measured stack voltage and the estimated voltage produced by the optimized model. In the modified methodology, the sine-cosine method has been utilized to enhance the global searching capability in the exploration phase and the local searching capability in the exploitation phase of the MRFO algorithm. In order to validate the effectiveness of the suggested methodology, four different case studies comprising standard benchmark 250 W PEMFC, BCS-500 W PEMFC, SR-12 500 W FC, and 1 kW Temasek stacks were utilized, and the attainments have been compared with the measured polarization characteristics. The attainments have been intensively compared with several metaheuristic algorithms (MA) including Tree growth Algorithm (TGA), Grey wolf optimizer (GWO), Whale optimization algorithm (WOA), Salp swarm algorithm (SSA), and original Manta Ray Foraging Optimization (MRFO), to confirm the superiority of the MMRFO against the compared techniques. The obtained results give a satisfactory agreement between the MMRFO-based model and the experimentally measured data. Finally, the achievements confirmed the effectiveness of the MMRFO over the basic MRFO algorithm and other novel metaheuristic algorithms in identifying PEMFC parameters.