Order Model Research Articles

Morphodynamic Cartography Visualization of Wulan River Estuary Systems from Space to Numerical Approach Based on Multi-Season Analysis

This research aims to investigate the morphodynamics of the Wulan River estuary in Demak Regency using the integration of multispectral remote sensing images and numerical modeling. In this study, PlanetScope for a manualvisual analysis of estuary morphodynamics and Sentinel-2 MSI Level 2A to obtain periodic total suspended solids (TSS) information for the east and west monsoon seasons. MIKE by DHI software used to develop hydrodynamic numerical modeling in order to characterize the current circulation and sediment transport model. Based on a marine cartography aspect, the obtained results illustrated that the climatological phenomenon of seasonal forcing plays a role in the development of the current circulation and indirectly influences the sediment transport. During the west season, the morphodynamics in the Wulan River estuary are much more massive and significant as compared to the east season. A projection of the deposition that results from the sediment transport is described in the bed-thickness change; this occurs in the western part of the Wulan River estuary during the east season, while the bed-thickness change occurs predominantly in the northern part during the west season (where there was previously a beach sandbar phenomenon). This was verified through multi-temporal satellite imagery that the deposition that occurs in the northern part of the Wulan River estuary during the west season is increasingly progressive and massive.

Stability analysis study for the time-fractional Galilei invariant advection-diffusion model of distributive order using an efficient hybrid approach

Abstract In this manuscript, a new model of the time-fractional Galilei-invariant advection-diffusion model of distributed order is studied. An efficient hybrid numerical approach with high accuracy is used to estimate this equation. The finite difference numerical method is used to approximate the fractional operator in terms of the time variable and to approximate the integral term of distributed order, the Gaussian--Legendre integration is applied. To obtain a fully discrete numerical approach, we used a spectral element numerical approach, in which Legendre polynomials are used as the basis function. For the proposed numerical approach, the error and stability analysis are studied. For the efficiency of the numerical approach, some numerical examples are presented with graphs and tables.

Reduced order modeling of turbulent reacting flows on low-rank matrix manifolds

A new low-rank approximation, referred to as time-dependent principal component analysis (t-PCA), is developed for reduced-order modeling (ROM) of scalar transport in turbulent reactive flows. In t-PCA, the evolution of the composition matrix is constrained to a low-rank matrix manifold, similar to that in standard PCA. Specifically, the t-PCA approximates the composition matrix through the multiplication of two thin, time-dependent matrices that represent spatial and composition subspaces. The evolution equations for these subspaces are derived by projecting the composition transport equation onto the tangent space of the low-rank matrix manifold. While the evolution equations for the spatial subspace in both PCA and t-PCA are similar, there are differences in how the composition subspace is obtained: (i) In t-PCA, the composition subspace is time-dependent, whereas in PCA, it is static. (ii) The t-PCA does not require any prior data, and an evolution equation for the composition subspace is derived. In PCA, the composition subspace is obtained from data. The t-PCA can be regarded as an on-the-fly low-rank approximation that can adapt to changes in the flow instantaneously. It is shown that the low-rank t-PCA approximations achieve residual levels lower than those obtained via PCA. For demonstrations and a comparative assessment of the ROMs, simulations are conducted of a non-premixed CO/H2 flame in a temporally evolving jet. Two cases are considered, based on the mechanisms previously suggested for combustion kinetics of this flame: (i) the GRI-Mech 3.0 model involving 53 species for a two-dimensional flame, (ii) the skeletal syngas model involving 11 species for a three-dimensional turbulent flame. The results are appraised via a posteriori comparisons against data generated via full-rank direct numerical simulation (DNS) of the same flame, and also with the PCA-reduced data from the DNS. It is shown that t-PCA yields excellent predictions of various features of the thermo-chemistry field.

Estimation of the Hydraulic Parameters of a Stratified Alluvial Soil in the Region of El Haouareb—Central Tunisia. Experiments, Empirical, Analytical and Inverse Models

Modeling water flow and contaminant transport in the unsaturated zone is a difficult task that relies heavily on good hydrodynamic soil characterization. This article presents a complementarity between experimentation, direct modeling and inverse modeling in order to provide a better estimate of the hydrodynamic parameters of stratified alluvial soil in the El Haouareb region of the Kairouane plain in Tunisia. A field sampling campaign was carried out. The samples collected underwent particle size analysis, bulk density measurements and infiltration tests using a mini-Muntz. In parallel, simple evaporation tests were applied to separate strata. In addition, a 2 m soil column was reconstituted and fitted with sensors to monitor water content, tension, temperature and electrical conductivity. An internal drainage test was performed on this monolith. Three methods were applied using experimental data to estimate soil hydrodynamic parameters. In the first method, pedotransfer functions were used (Rosetta platform) based on granulometric results and bulk density. In the second, water tension and water content monitored during the simple evaporation test were used to plot the soil–water retention curve (SWRC) using SWRC-Fit. In the third method, inverse modeling was applied to the internal drainage test. A comparison of the results showed that the inverse method had the lowest RMSE. Uncertainty analysis has been implemented for both the experimental and numerical set up.

The Investigation of Hyperparameters Influence on Fruit and Vegetable Image Recognition Performance Using SVM

This study aims to improve the efficiency of automatic classification and quality control of fruits and vegetables through image recognition technology, to achieve efficient and accurate intelligent sorting in agricultural production, reduce labor costs and improve market competitiveness. A high-quality image dataset of 36 fruit and vegetable categories from Kaggle is used in this study. The images in the dataset have been preprocessed to ensure that the data is suitable for the classification task and sets the stage for efficient training and evaluation of the model. Logistic regression was first used as the baseline model in order to compare the performance with the Support Vector Machine (SVM) model. Subsequently, hyperparameter tuning is performed to optimize the model to achieve the best cross-validation accuracy. Next, the SVM model is trained with the selected hyperparameters, and the training time is recorded. The performance of the model was evaluated in detail by confusion matrix and classification reports, and the test set was used for final validation to ensure that the model would also perform well on unseen data. The SVM model achieves an accuracy of 96% on both validation and test sets, which is a very good performance. The hyperparameters optimized by GridSearchCV (C = 10, gamma = 0.1, kernel = rbf) effectively improve the performance of the model, verifying that reasonable hyperparameter selection is crucial to the SVM model. These results show that the model has good generalization ability and potential to be applied to real-time classification tasks.

Enhanced Photodegradation of Congo Red Using RSM‐Optimized TiO₂ on Borassus flabellifer Male Inflorescence Biochar

AbstractThis study explores the use of biochar, derived from Borassus flabellifer male inflorescence biomass as an alternative support for titanium dioxide (TiO2) to enhance its photocatalytic activity in degrading Congo red (CR) dye pollutant. The biochar was synthesized through controlled pyrolysis and subsequently combined with TiO2 to form a nanocomposite via hydrothermal method. The primary goal of creating this nanocomposite is to breakdown azo dyes, such as CR, using UV irradiation. Comprehensive characterization techniques, including XRD, FTIR, SEM coupled with EDAX, XPS, and UV‐DRS confirmed the successful integration of biochar with TiO2. The enhanced photocatalyst performance is attributed to the increased surface area, improved light absorption, and efficient charge separation facilitated by the biochar support. Furthermore, the experimental parameters were optimized using the response surface methodology (RSM). These studies demonstrated that the CR concentration, contact duration, pH, and photocatalyst quantity are critical factors in the photocatalytic oxidation process. The strong correlation coefficient for the first order model indicated that the data described by the RSM, agreed with the obtained results. This study highlights the potential of agricultural waste‐derived biochar as a cost‐effective and sustainable support for TiO₂, offering a viable solution for the remediation of dye‐contaminated water.

Multi-objective optimal control of a hybrid offshore wind turbine platform integrated with multi-float wave energy converters

Offshore floating wind turbines will play a pivotal role in achieving net-zero emissions. This study explores a hybrid platform combining an offshore floating wind turbine with wave energy converters (WECs) from an active control perspective to mitigate turbine damage risk while maximizing wave energy conversion. Initially, the control-oriented state-space modelling of the hybrid platform and the validation of the time-domain Cummins-type models of different orders are performed by the Eulerian-Lagrangian method with model linearization and downscaling. Then, a multi-objective non-causal optimal controller is introduced, balancing wave energy capture and penalizing the nacelle acceleration. When wave energy capture maximization is set as the control target, the proposed optimal controller can improve energy output by 184% as compared against a well-tuned passive damper across all peak periods tested. However this causes peak hub acceleration to increase marginally beyond the desirable limits of 3–4 m/s2 for wind turbine operation. When hub acceleration is penalized, the multi-objective optimal controller can reduce peak values below limits by between 40% and 61% with trivial loss of wave energy capture.

A high-order isogeometric vibrational method for adaptive analysis of athlete diving equipment using advanced kinematic and geometric modeling

Isogeometric high-order analysis of a sandwich beam is presented in this article. The kinematic and geometric modeling is applied using the higher order modeling based on the stretchable models. The suggested model in this article can be applied for the sport equipment specially for analysis of diving beams boards and beams. The Variational-based approach is extended to derive governing equations. The results are analytically obtained in order to seek impact of significant parameters of the problem on the responses. The results are confirmed through comparison with available ones in the literature.

Effect of ultrasound assisted H2O2 degradation on longan polysaccharide: degradation kinetics, physicochemical properties and prebiotic activity

This study aimed to investigate the effect of ultrasound-assisted H2O2 (US/H2O2) reaction on degradation parameters and kinetics, physicochemical properties and prebiotic activity of longan polysaccharide (LP). Results showed that US/H2O2 had a synergistic effect on the degradation of LP, and its kinetic equation followed to the fist – order model. US/H2O2 degradation did not change the chemical and monosaccharide composition of LP but altered their ratio. Compared with LP, three degraded polysaccharides (DLPs) displayed lower molecular weight, particle size and viscosity, but higher solubility. SEM and AFM revealed that US/H2O2 degradation led to significant differences in the microstructure and solution conformation of LP. Moreover, LP and DLPs showed different proliferation effects on four lactobacilli and bifidobacteria strains, among which DLP-8 (degraded for 8 h) exhibited the strongest prebiotic activity. US/H2O2 could be effectively applied to the degradation of LP to improve its physicochemical properties and bioactivities.

Feature importance explanation of prosthetic valve endocarditis through machine learning via SHAP

Abstract Introduction Diagnosing prosthetic valve endocarditis poses a significant challenge in daily clinical practice. Accurately identifying these patients is vital due to its treatment implications. Clinical and imaging assessment of suspected cases yields a substantial number of interrelated variables, which can be analyzed through machine learning techniques in order to improve diagnostic accuracy. Feature ranking facilitates identification and interpretability of relevant clinical and imaging variables in this task. Purpose To utilize machine learning feature ranking and modeling in order to improve interpretable identification of patients with prosthetic valve endocarditis. Methods 89 cases defined by the endocarditis team and 173 controls were analyzed. We implemented the SHapley Additive exPlanations (SHAP) method to evaluate feature importance. 5-fold cross-validated Xgboost models were trained and independently tested with additive clinical, echocardiographic and 18F-FDG PET/CT data. Performance was evaluated through confusion matrices, F1-scores and AUCs. These were compared against conventional logistic regression of the Duke Criteria classification. Results Mean age was 59.6 ± 15.7, with 29.7% women. 14 clinical (demographic, laboratory and cultures), 2 echocardiographic, and 5 PET/CT features were included. The XGBoost model trained with all additive features demonstrated an AUC of 0.91±0.1 and outperformed simpler models as well as the Duke classification model (AUC=0.85). The clinical variables alone achieved an AUC of 0.85±0.11, while integration with echocardiography enhanced its performance to 0.90±0.04 (Fig. 1). Feature ranking demonstrated that antibiotic treatment duration, positive blood cultures, valve type, weight and CRP were the most relevant clinical variables, while the presence of vegetation or abscess, and abnormal uptake and uptake focality on PET/CT represented the most relevant imaging variables (Fig. 2). Conclusion Machine learning-based feature ranking and modeling may significantly enhance identification of patients with prosthetic valve endocarditis beyond standard clinical criteria. Feature selection offers interpretable performance in identifying these cases, model testing in clinical decision support may be warranted.Fig. 1:Comparative AUC Analysis.Fig. 2:SHAP feature importance.

Costs of Adjustment, Portfolio Separation, and the Dynamic Behavior of Bank Loans and Deposits – Revised

AbstractWe revise the solutions of the dynamic optimization model of Elyasiani, Kopecky, and Van Hoose (1995), and show that the optimal policy function for the case of adjustment costs and no portfolio separation is a vector autoregressive model of order 1, and not of order 2. In addition, the empirical approach of Elyasiani, Kopecky, and Van Hoose (1995) is incorrect. The optimal policy functions for the no portfolio separation case have to be estimated with a panel vector autoregression model and not with a simultaneous equation approach. We also describe how the portfolio separation hypothesis can be tested empirically based on the correct policy functions.

Study of fractional order rabies transmission model via Atangana–Baleanu derivative

In this work, we aim at disentangling the theoretical contribution through mathematical modeling approach to advance the understanding of rabies dynamics and control in livestock population. A fractional order model of rabies, using Atangana–Baleanu fractional operator is created. The analysis of suggested system and its application are both conducted. The capacity of proposed model to forecast the disease can help researchers and livestock health care agencies to take preventive actions.

Ag2O/ZnO/CuO and Ag2O/ZnO/NiO immobilised on reduced graphene oxide: synthesis, characteristics and its adsorption, catalytic behaviour and antibacterial activities

ABSTRACT Ag2O/ZnO/CuO (A/Z/C) and Ag2O/ZnO/NiO (A/Z/N) nanometal oxides were synthesised in different rations by co-precipitation method and were immobilised on reduced graphene oxide (rGO). The prepared nanocomposites were characterised by Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). Their application in the adsorption of MB, RhB and MO dyes, olefin oxidation and antibacterial properties were investigated. The results showed that A/Z/N/rGO adsorbent has 99% (10 min), 93% (20 min) and 97% (12 min) adsorption of MB, RhB and MO, respectively. From the investigation of Freundlich and Langmuir models, it was found that the adsorbent follows Langmuir. Moreover, the reaction thermodynamics was also studied, the reaction was endothermic and spontaneous. The kinetics of dye adsorption MB, RhB and MO on the surface follow a pseudo-second order model. At optimum condition, the conversion and selectivity of α-methylstyrene to acetophenone was >99% by A/Z/N and A/Z/C catalysts. A/Z/C/rGO nanocomposite has good antibacterial properties against two bacteria E. coli and S. aureus.

Viscoelastic modeling based force control framework for enhancing operation safety of soft tissue for orthopedic surgical robots

In robot-assisted laminectomy, precise control of touch force on the spinal cord during the dissection of ossified ligaments is critical to prevent dura mater tearing and ensure surgical safety. However, the complex viscoelastic characteristics of tissue at the lesion site pose a significant challenge for accurately applying touch force. This paper proposes a safe touch force servo framework based on tissue model identification and preoperative optimization of the force controller. First, a soft tissue model is established using elastic and viscoelastic elements, and the transfer function from tissue deformation to touch force is derived. Subsequently, the path and parameters of a safe pre-touch are designed, and force information required for tissue model identification is obtained through the pre-touch experiment. The model order and specific parameter values are then identified using a differential evolution algorithm. Next, force control simulation is conducted based on the identified model, and a specifically designed loss function is introduced to achieve preoperative tuning of the force controller. Finally, step force control experiments on sheep spines validate the effectiveness of the proposed method. This paper provides a safe and available touch motion control framework that can be expandable to impose precise force on various vulnerable soft tissues, with the potential for widespread application.

Model complexity optimization of equivalent dynamical linearization data models used in model‐free adaptive control

AbstractThis paper discusses a strategy for optimizing the complexity of time‐varying data models as used in model‐free adaptive control (MFAC). Here the dynamic linearization in compact form (CFDL), partial form (PFDL), and full form (FFDL) are considered as data models used to describe input/output (I/O) data sets. These data models are built only for control purpose and can have various degrees of complexity depending on the size of the considered time‐window as well as the underlying algorithms. The methodology is to compare the performance of the data models according to an evaluation criterion, to analyze the order of different data models based on bias‐variance trade‐off, and to select the best‐performing model. The complexity of the selected model is compared with the reduced linear time‐invariant (LTI) model obtained by applying a combination of the eigensystem realization algorithm (ERA) and observer/Kalman filter identification (OKID) on the same I/O dataset. The I/O data are obtained from applying the MFAC controllers on a nonlinear three‐tank system (3TS) to track various desired references. The results indicate that the model complexity optimization can also be used for selecting an appropriate MFAC data model with optimal order to reduce the complexity and computational burden of the MFAC control algorithms.

A frequency-independent absorption function surrogate for perfectly matched layer in exterior acoustics

In many engineering applications, the solution of acoustic wave problems in the infinite domain is required over a broad frequency range with densely sampled increments. In order to achieve efficient numerical simulations via a spatial discretization, e.g. finite element method, additional artificial absorbing boundaries are necessary to truncate the computational domain into appropriate bounded sizes. One of the most commonly used non-reflecting techniques to attenuate propagating waves is known as the perfectly matched layer. However, the system matrices arising from the finite element treatment of the Helmholtz equation in the absorbing layers are frequency-dependent, implying that they must be formed and inverted at each frequency of interest. Such a procedure is rather troublesome for frequency sweeps. To address this, a surrogate of perfectly matched layers is proposed, which enables the corresponding system matrices to be independent of the frequency. Moreover, it avoids the use of a relatively large computational domain and relatively thick enclosed layers at low frequencies, thus improving the ability of perfectly matched layers across the entire frequency range. After that, an adaptive projection-based model order reduction scheme is further developed to reduce the computational complexity of exterior acoustic systems. A robust error indicator based on the relative error of two constructed reduced order models is accordingly introduced. The performance of the present solution framework is discussed and compared with other implementation strategies, in the context of multi-frequency solution of two-dimensional test models with single or multiple scatterers.

High-fidelity simulations of microramp-controlled shock wave/boundary layer interaction

Microvortex generators (MVGs) are a promising solution to control shock wave/turbulent boundary layer interactions (SBLIs), especially in supersonic inlets. In this study, we examine the effects of a microramp vortex generator on an SBLI generated by an oblique shock wave and a turbulent boundary layer using direct numerical simulations (DNSs). Two cases, with and without the presence of a microramp, are compared in terms of their mean and unsteady flow features at free-stream Mach number equal to 2 and friction Reynolds number at the inviscid shock impingement equal to 600. The long integration period allows us to assess how microramps affect the typical low-frequency unsteadiness observed in SBLIs, and the data generated may serve as a reference for simulations of lower fidelity or reduced order models. The analysis shows that the three-dimensional microramp wake alters the interaction region dramatically, inducing a significant spanwise modulation and topology change of the separation. For example, tornado-like structures redistribute the flow in both the spanwise and wall-normal directions inside the recirculation region. The increase in momentum close to the wall by the ramp vortices effectively delays the onset of the separation and, thus, the separation length, but at the same time leads to a significant increase in the intensity of the wall-pressure fluctuations. We then characterise the mutual interaction between the arch-like vortices around the ramp wake and the SBLI. The specific spanwise vorticity shows that these vortices follow the edge of the separation and their intensity, apart from mean compressibility effects, is not affected by the shocks. The shocks, instead, are deformed in shape by the periodic impingement of the vortices, although the spectral analysis did not reveal any significant trace of their shedding frequency in the separation region. These Kelvin–Helmholtz vortices, however, may be relevant in the closure of the separation bubble. Fourier analysis also shows a constant increase, in both value and magnitude, in the low-frequency peak all along the span, suggesting that the motion of the separation shock remains coherent while being disturbed by the arch-like vortices and oscillating at a higher frequency in absolute terms.

Design, Simulation, and Implementation of MRAC Controller with PMAC Target Hardware for Controlling 7.5 m Antenna

The article presents the design of an adaptive controller, simulation, code generation, and porting of the algorithm into target hardware, movement of the 7.5 m antenna, testing, verification, and validation of theoretical and experimental results of the antenna system. The conventional servo control methods based on PID controllers are being used for tracking X-band missions. As the need for higher data rates is increasing, missions are switching to the Ka band, as a result the satellite ground stations will have to track the satellite in the Ka band where the antenna band beam width is narrowed by a factor of 3 when compared to the X-band antenna beamwidth. These challenges had driven the designer to design and implement a Model Reference Adaptive Controller over PID to improve and achieve the tracking accuracy of less than 25m° from the present 30 m°. Thus, consistent and constant performance is achieved throughout the trajectory tracking of the mission using PID + MRAC (Model Reference Adaptive Control). MRAC uses a reference model that specifies the desired response of the antenna system and adjusts the controller parameters such that the plant follows the reference to achieve the desired performance. The entire drive chain modules and their parameters are considered in the design of the plant, namely MRAC controller, PID controller, Power Drive electronic (Rectifier and Inverter), Servo Motor, mechanical torque coupling, Gear reducer, Slew Ring Bearing (SRB), and absolute encoder modules of each axis for position feedback from the antenna. Model Order Reduction of higher-order actual Plant model to the standard second-order system is optimized. The modelled and analysed outcomes of the MRAC scheme using the Modified MIT are demonstrated. The developed model is tested against step, ramp, parabolic, satellite trajectory, velocity and acceleration inputs in MATLAB/Simulink and validated with practical/ experimental test results with antenna load at the satellite ground Station. The achieved results indicate that the PID + MRAC controller has significant and consistent performance improvements when compared to the PID controller alone.

Capital structure of special-purpose governments

PurposeThe paper examines whether special purpose governments follow the pecking order model when raising capital, replacing firm equity in the original model with local intergovernmental revenues. Special purpose governments are a relatively underexamined component of state and local governments, and their capital structure choices have important implications for America’s aggregate fiscal health. This paper seeks to illuminate special purpose governments’ choices among available forms of capital.Design/methodology/approachTo address our research inquiry regarding whether special-purpose governments adhere to the pecking order theory, we narrow our focus to a specific group of transit entities. Our investigation utilizes data sourced from two distinct repositories: the United States Census of Governments and the National Transit Database. To facilitate integration of these disparate datasets, we establish a correspondence between them using the Federal Information Processing Standard and the transit identification number. Initially, our analysis employs maximum likelihood estimation, comparing these estimates to those generated by a naïve model. Subsequently, we derive parameter estimates through the application of a bivariate probit model.FindingsThe paper supports the pecking order hypothesis where internal funds are consumed first, debt is consumed next and IGR is consumed last. The results are robust and are not influenced by simultaneity bias.Research limitations/implicationsThe paper uses transit special purpose governments as the special purpose government of interest. These transit special purpose governments have high fixed costs that may inform their capital structure decisions relative to non-transit special purpose governments. Additionally, transit special purpose governments often have a profit-maximizing objective that may not uniformly apply to other special purpose governments, such as school districts, who lack such an incentive.Practical implicationsOur research should grab the attention of state and local politicians, voters, policy experts and scholars. Special-purpose governments, a key part of our governmental system, are on the rise. Understanding them better can guide decision-making on how to allocate resources, set policies and plan strategically. This knowledge boosts financial reporting quality, transparency, accountability and public confidence. Journalists can leverage our findings to ensure accurate and thorough reporting, fostering accountability and trust. Additionally, debt markets and analysts can factor this information into their risk assessments.Originality/valueThe paper enhances our understanding of how special purpose governments interact with existing models of corporate finance when making capital structure decisions.

Mathematical modelling, energy consumption, and quality evaluation of wheat seeds subjected to intermittent drying

AbstractThis work aims to evaluate the kinetic profile of intermittent drying of wheat seeds using traditional models from the literature and the fractional calculus technique. Furthermore, it aims to verify the application of the intermittent drying process on the amount of antioxidant compounds, protein, and lipid content of the grain, in addition to energy consumption to obtain the desired final moisture content of the wheat. It was verified that the prediction of drying kinetics by Page and fraction order models were similar (modelling efficiency varying between the range of 0.917–0.995, varying the drying condition and wheat cultivar). Regarding antioxidant compounds for the three wheat cultivars, it can be seen that the higher fraction of ethanol (74.0% and 90.36%) used for extraction had greater process efficiency. Regarding the protein content in the three wheat cultivars, lower drying temperatures and intermittency periods result in lower quality losses of material (12.3% for BRS‐Atobá wheat, 9.89% for BRS‐Jacana wheat, and 18.71% for BRS‐Sanhaço wheat). In terms of lipids, it was found that the influence of temperature was greater on the lipid content than on the protein content of the material (for the best drying condition, there were percentage decreases for the best condition of 36.16% for the BRS‐Atobá wheat, 34.9% for BRS‐Jacana, and 52.2% for BRS‐Sanhaço). Regarding energy consumption during the drying process, it can be seen that the conditions used for intermittency and drying time, in addition to the sample conditions, directly impact energy consumption.