Sum Of Errors Research Articles

Regression Tool in MS Excel® Spreadsheets for Biological Data: R-BioXL

A user-friendly MS Excel® spreadsheet as a freeware (R-BioXL) was developed to fit mathematical models to experimental data. (R-BioXL is available to everyone at https://drive.google.com/drive/folders/1GyjT3Z_CJQZu6ASb4LQBlS-ajLa_nF6X?usp=sharing) Initially, users are expected to enter their X-Y data and define their parameters of the model. Then, a model equation should also be entered again by users. Users can visualize data (scatter plot) and model fit (line plot) with the defined initial estimates of parameters on the same graph by default. Squared differences between experimental data and model estimates are calculated automatically. Users can change the initial estimates of the parameters to make the model closer to the data instantly, and Solver Add-In of Excel® should be used to minimize the sum of squared error by changing the parameter values. After the parameters are obtained, standard errors (by using “SolverAid” macro), 95 and 99% confidence intervals of the parameters, p values to determine the statistical significance of the parameters, and goodness-of-fit indices are calculated as the last step. All results can be saved on a different Excel® working page. Whole procedure takes a couple of minutes (~3 to 10 min) depending on the Excel® experience of the user. The utility, accuracy and reliability of the spreadsheet was shown by applying two-parameter (non-linear) Michealis-Menten equation for enzyme kinetics, three-parameter (linear) van Deemter equation for chromatography, and four-parameter (non-linear) modified Gompertz equation for microbial growth. In conclusion, R-BioXL can be safely and freely used to describe the experimental data with Excel® knowledge, without any skills in programming and without additional cost for other software package.

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 R 2 [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.

Phase-Angle-Encoded Snake Optimization Algorithm for K-Means Clustering

The rapid development of metaheuristic algorithms proves their advantages in optimization. Data clustering, as an optimization problem, faces challenges for high accuracy. The K-means algorithm is traditaaional but has low clustering accuracy. In this paper, the phase-angle-encoded snake optimization algorithm (θ-SO), based on mapping strategy, is proposed for data clustering. The disadvantages of traditional snake optimization include slow convergence speed and poor optimization accuracy. The improved θ-SO uses phase angles for boundary setting and enables efficient adjustments in the phase angle vector to accelerate convergence, while employing a Gaussian distribution strategy to enhance optimization accuracy. The optimization performance of θ-SO is evaluated by CEC2013 datasets and compared with other metaheuristic algorithms. Additionally, its clustering optimization capabilities are tested on Iris, Wine, Seeds, and CMC datasets, using the classification error rate and sum of intra-cluster distances. Experimental results show θ-SO surpasses other algorithms on over 2/3 of CEC2013 test functions, hitting a 90% high-performance mark across all clustering optimization tasks. The method proposed in this paper effectively addresses the issues of data clustering difficulty and low clustering accuracy.

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.

Sweep frequency response analysis of three-phase induction motors using high frequency circuit model

Three-phase induction motor (TPIM) is one of the most important and expensive component in many industries. If a failure happens in service, the impact can be far reaching. Resulting in prolonged downtimes and expensive repairs. Sweep frequency response analysis (SFRA) is a new method that is being proposed to assess the condition of windings in motors. This paper proposes an approach for measuring the SFRA of the TPIMs using its high frequency (HF) circuit model. It also introduces an easy and efficient way to estimate the parameter values of the HF model based on the data from the SFRA test. The method first simulates the HF model and then aligns the simulated SFRA curve with the experimental one by adjusting the parameter values. This adjustment is based on individual parameter effects on the SFRA signature. The aim of this paper is to understand the SFRA signatures of TPIMs by using both experimental and simulation methods. SFRA measurements are carried out on two TPIMs (1 HP and 3 HP) under the same phases (U1U2, V1V2, W1W2) and different phase connections (U1V1, U1W1, V1W1). This paper utilizes two statistical indicators, Absolute Sum of Logarithmic Error (ASLE) and Correlation Coefficient (CC), for interpreting SFRA signatures. The CC values, ranging from 0.98 to 0.99, and the ASLE values, between 0.4 and 1.5 dB, indicate a good agreement between the measured and simulated SFRA signatures. This validates that the proposed HF model is accurate and can be applied to SFRA measurements for any rating of TPIM. This accuracy is verified in this paper using two different rating TPIMs.

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.

Optimal error bounds for the two-point flux approximation finite volume scheme

We consider a finite volume scheme with two-point flux approximation (TPFA) to approximate a Laplace problem when the solution exhibits no more regularity than belonging to H 0 1 ( Ω ) H^1_0(\Omega ) . We define an error between the approximate solution and the exact one, involving their difference and the difference of normal gradients to the faces of the mesh. We prove that this error behaves as the sum of an interpolation error and a conformity error. As a consequence, some error bounds, depending on the regularity of the continuous solution, can be obtained for both the approximate solution and an approximate gradient post-processed from this approximate solution. A numerical example illustrates the error estimate in the context of a solution with minimal regularity. This result is extended to evolution problems discretized via the implicit Euler scheme in an appendix.

Quantile-based robust Kibria–Lukman estimator for linear regression model to combat multicollinearity and outliers: Real life applications using T20 cricket sports and anthropometric data

The performance of ordinary least squares (OLS) and ridge regression (RR) are influenced when outliers are present in y-direction with multicollinearity among independent variables. The robust RR with ridge parameters provides a biased estimator that has a smaller variance than conventional OLS and RR estimators. The optimal value of the ridge parameter has a vital role in bias-variance tradeoff. This study proposes quantile-based estimation of ridge parameter in robust RR to deal with the joint issue of y-direction outliers and multicollinearity. The effectiveness of the proposed estimators is evaluated using intensive Monte Carlo simulation and two real data sets in terms of mean square error (MSE) and predictions sum of squared error (PSSE) criterion. Simulation findings reveal that the newly developed estimators of ridge parameter in robust RR have better performance than OLS, RR, and existing robust RR estimators when errors are normally and non-normally distributed. The results from two numerical examples of T20 Cricket sports and anthropometric data show that the new estimator with quantile probability 0.50 and 0.99 respectively has winning performance among all competing and proposed estimators.

Nonlinear integral backstepping control based on particle swarm optimization for a grid-connected variable wind energy conversion system during voltage dips

Double-fed induction generator (DFIG) wind turbines connected to the grid are particularly subject to grid problems such as voltage dips. Because of this, it might be challenging to maintain system stability and prevent system disconnections when using a Proportional-Integral (PI) Controller to operate this system. This paper applies the Integral Backstepping Control for the wind power plant system connected to the power grid. The Integral Backstepping is a nonlinear and recursive method that employs the Lyapunov theory to ensure the system's stability. The best selection of gain values for the Lyapunov function guarantees improved system control. These gains are frequently adjusted using the trial-and-error strategy, which is time-consuming and inefficient. This technique becomes more complex when many parameters need to be determined. It also limits the system's performance and restricts this nonlinear approach's advantages. The objective is to apply the particle swarm optimization for computing several constant values of the nonlinear approach by minimizing an integral absolute error criterion index. The weighted sum of errors is employed to solve the multiple objective problems. The proposed controller tracks the maximum power point, maintains the voltage of the DC-Link constant, and controls active and reactive power. The robustness of this method is verified in critical conditions, including system parameter variation and asymmetrical and symmetrical grid faults. The simulation findings highlight the effectiveness and robustness of the combination of Integral Backstepping with Particle Swarm Optimization in terms of reducing response time from 10.6 (ms) to 2 (ms), canceling static error, and improving overshoot compared to the vector control based on PI regulator. Besides, the DC-Link voltage ripples during the asymmetrical grid faults are reduced to ±1 (V) using the suggested controller. The latter can be implemented thanks to advancements in Central Processor Unit technology.

Verification of frequency-dependent impedance boundaries for the Finite-Volume Time-Domain scheme

Numerical simulation methods are widely used to approximate solutions to the wave-equation, taking into account the initial condition ('initial sources') and boundary conditions ('wall materials'). Trustworthy simulations are essential; a primary step is to ensure the correctness of the implementation through a process known as code verification. In this work, we do code verification by studying the convergence of the known finite-volume time-domain (FVTD) method with a frequency-dependent boundary condition modeled as an inductance/resistance/capacitance (LRC) termination. Due to the difficulties of obtaining an analytical solution for frequency-dependent impedance boundaries, we thoroughly derive and implement The Method of Manufactured Solutions tailored to the wave-equation inside a rectangular room for uniform frequency- dependent wall impedance. Asymptotic estimates are found empirically based on a convergence study compared against the manufactured ground truth solution. We investigate the impact of the frequency-dependent boundaries on the convergence affected by the total sum of numerical errors and validate the theoretical properties of the FVTD scheme for various LRC model configurations. The method should also serve as a useful resource for others to verify the correctness of their code implementations.

Direct Identification of the Continuous Relaxation Time and Frequency Spectra of Viscoelastic Materials

Relaxation time and frequency spectra are not directly available by measurement. To determine them, an ill-posed inverse problem must be solved based on relaxation stress or oscillatory shear relaxation data. Therefore, the quality of spectra models has only been assessed indirectly by examining the fit of the experiment data to the relaxation modulus or dynamic moduli models. As the measures of data fitting, the mean sum of the moduli square errors were usually used, the minimization of which was an essential step of the identification algorithms. The aim of this paper was to determine a relaxation spectrum model that best approximates the real unknown spectrum in a direct manner. It was assumed that discrete-time noise-corrupted measurements of a relaxation modulus obtained in the stress relaxation experiment are available for identification. A modified relaxation frequency spectrum was defined as a quotient of the real relaxation spectrum and relaxation frequency and expanded into a series of linearly independent exponential functions that are known to constitute a basis of the space of square-integrable functions. The spectrum model, given by a finite series of these basis functions, was assumed. An integral-square error between the real unknown modified spectrum and the spectrum model was taken as a measure of the model quality. This index was proved to be expressed in terms of the measurable relaxation modulus at uniquely defined sampling instants. Next, an empirical identification index was introduced in which the values of the real relaxation modulus are replaced by their noisy measurements. The identification consists of determining the spectrum model that minimizes this empirical index. Tikhonov regularization was applied to guarantee model smoothness and noise robustness. A simple analytical formula was derived to calculate the optimal model parameters and expressed in terms of the singular value decomposition. A complete identification algorithm was developed. The analysis of the model smoothness and model accuracy for noisy measurements was carried out. The equivalence of the direct identification of the relaxation frequency and time spectra has been demonstrated when the time spectrum is modeled by a series of functions given by the product of the relaxation frequency and its exponential function. The direct identification concept can be applied to both viscoelastic fluids and solids; however, some limitations to its applicability have been pointed out. Numerical studies have shown that the proposed identification algorithm can be successfully used to identify Gaussian-like and Kohlrausch–Williams–Watt relaxation spectra. The applicability of this approach to determining other commonly used classes of relaxation spectra was also examined.

Hybrid beamforming for ISAC based on mmWave cell-free MIMO

Aiming at the scarcity of communication spectrum resources and the advantages of cell-free MIMO in resource utilization, a millimeter-wave cell-free MIMO-ISAC system is proposed. In this system, while the base station provides services for users, the multi-antenna user equipment can actively detect multiple targets. The research goal is to design analog and digital beamformers to optimize the weighted sum of communication and radar beamforming errors. Two kinds of power constraints are considered in the study, and the improved orthogonal matching pursuit algorithm and the Riemann conjugate gradient algorithm are used for optimization. Through the simulation analysis of the communication spectrum efficiency and the radar beam pattern, the results show that the hybrid beam scheme can achieve a compromise performance in terms of spectrum utilization and beam pointing, and this scheme has potential application prospects for improving system performance.

Preliminary study of feasibility of surface-guided radiotherapy with concurrent tumor treating fields for glioblastoma: region of interest

ObjectiveTo evaluate the impact of the residual setup errors from differently shaped region of interest (ROI) and investigate if surface-guided setup can be used in radiotherapy with concurrent tumor treating fields (TTFields) for glioblastoma.MethodsFifteen patients undergone glioblastoma radiotherapy with concurrent TTFields were involved. Firstly, four shapes of region of interest (ROI) (strip-shaped, T-shaped, ⊥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\perp$$\\end{document}-shaped and cross-shaped) with medium size relative to the whole face were defined dedicate for patients wearing TTFields transducer arrays. Then, ROI-shape-dependent residual setup errors in six degrees were evaluated using an anthropomorphic head and neck phantom taking CBCT data as reference. Finally, the four types of residual setup errors were converted into corresponding dosimetry deviations (including the target coverage and the organ at risk sparing) of the fifteen radiotherapy plans using a feasible and robust geometric-transform-based method.ResultsThe algebraic sum of the average residual setup errors in six degrees (mm in translational directions and ° in rotational directions) of the four types were 6.9, 1.1, 4.1 and 3.5 respectively. In terms of the ROI-shape-dependent dosimetry deviations, the D98% of PTV dropped off by (3.4 ± 2.0)% (p < 0.05), (0.3 ± 0.5)% (p < 0.05), (0.9 ± 0.9)% (p < 0.05) and (1.1 ± 0.8)% (p < 0.05). The D98% of CTV dropped off by (0.5 ± 0.6)% (p < 0.05) for the strip-shaped ROI while remained unchanged for others.ConclusionSurface-guided setup is feasible in radiotherapy with concurrent TTFields and a medium-sized T-shaped ROI is appropriate for the surface-based guidance.

Design and implementation of Golden Eagle optimized cascaded PI and LQR controller for PFC SEPIC converter in EV charging

This research article focuses on designing and implementing a single ended primary inductor converter (SEPIC) for an AC-DC converter, followed by an electric vehicle (EV) charger. It improves the high-power factor at the AC supply with minimum harmonic distortion. The Golden Eagle optimization techniques are adapted to optimize the tuning of Proportional-Integral (PI) and Linear Quadratic Regulator (LQR) controller parameters for better converter performance. The optimization is designed based on the eagle knowledge of hunting methods at various angles of spiral trajectories in capturing the animal. The SEPIC converter is designed and derived from the state space model by state space averaging, and the reduced model is obtained through the moment matching method to reduce computational complexity. The Golden Eagle Optimize the parameters of KP and KI of the PI controller and weighing matrix Q of the linear quadratic controller. The fitness function of the proposed optimization is the sum of the Integral Absolute error (IAE) and Integral Square error (ISE). The proposed optimization is implemented using MATLAB/SIMULINK software, and the simulation outcomes demonstrate an improved settling time, fast recovery against input and output variations, Total Harmonic Distortion (THD) of 1.75 %, and enhanced stability.

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

An optimized single-side inverse finite element method for displacement field reconstruction of wind turbine tower

Wind turbines are subjected to complex dynamic loads during actual operation, which are difficult to predict accurately in advance, which may lead to inaccurate structural strength assessment in the structural design stage, and thus bring safety risks to wind power towers. Inverse Finite Element Method (iFEM) is a reconstruction method of structural displacement distribution, which can estimate the full-field displacement distribution of tower, and used for structural on-line health monitoring. However, conventional iFEM require sensors to be placed on both sides of the structure, which greatly limits its engineering practicality. For this reason, a single-side inverse finite element (iFEM_S) was developed in this study to enhance the engineering applicability of iFEM. Both iFEM and iFEM_S are based on the principle of least squares variation, and the solution equations can be established when the measured strain is known. The core idea of iFEM_S is to find the optimal node displacement s to minimize the sum of mean square errors between the theoretical and measured strains on one side of the structure, thereby generating a set of algebraic equations. Solving these linear equations can achieve the structural displacement field. Additionally, A cyclic optimization algorithm (COA) was developed in this study to improve the robustness of reconstruction results through optimizing strain gauge locations, in which the condition number of pseudo stiffness matrix was used as the objective function, and the condition number is minimized, named iFEM_S_O. Finally, the reconstruction accuracy and robustness and of iFEM, iFEM_S and iFEM_S_O were verified through several numerical examples. The simulated reconstruction results indicated that iFEM_S_O is more accurate and stable than iFEM_S, and iFEM_S is more accurate and stable than iFEM.

Parameter extraction of proton exchange membrane fuel cell based on artificial rabbits’ optimization algorithm and conducting laboratory tests

Proton exchange membrane fuel cell (PEMFC) parameter extraction is an important issue in modeling and control of renewable energies. The PEMFC problem’s main objective is to estimate the optimal value of unknown parameters of the electrochemical model. The main objective function of the optimization problem is the sum of the square errors between the measured voltages and output voltages of the proposed electrochemical optimized model at various loading conditions. Natural rabbit survival strategies such as detour foraging and random hiding are influenced by Artificial rabbit optimization (ARO). Meanwhile, rabbit energy shrink is mimicked to control the smooth switching from detour foraging to random hiding. In this work, the ARO algorithm is proposed to find the parameters of PEMFC. The ARO performance is verified using experimental results obtained from conducting laboratory tests on the fuel cell test system (SCRIBNER 850e, LLC). The simulation results are assessed with four competitive algorithms: Grey Wolf Optimization Algorithm, Particle Swarm Optimizer, Salp Swarm Algorithm, and Sine Cosine Algorithm. The comparison aims to prove the superior performance of the proposed ARO compared with the other well-known competitive algorithms.

Second-hand car price prediction based on multiple linear regression and random forest

The second-hand car market is a hot topic. Buying a second-hand car has advantages in price and many other aspects. Therefore, it is important to establish a good price prediction model. This paper will explore the factors that affect the price of second-hand cars. After analyzing and learning many kinds of literature, this paper establishes a multiple linear regression model and a random forest model and makes a comparative analysis of the model effect. The sum of the square error and R-square value of the random forest are better than the multiple linear regression model. Among the factors affecting the price of second-hand cars, the year of production has the greatest impact on the price, which shows that the age of the year is an important factor in determining the price of second-hand cars. The next most important factor is the number of kilometers traveled, followed by fuel type and transmission type-finally, engine displacement, number of transfers and number of seats. The random forest model established in this paper has better application value to price prediction.

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.