Optimal Parameter Values Research Articles

The influence of intraoperative hemodynamic parameters on the results of combined kidney and pancreas transplantation

Introduction. Reperfusion syndrome has been proven to impact the early results of simultaneous pancreas and kidney transplantation. The optimal values of hemodynamic parameters at the moment of reperfusion of the kidney graft and the pancreas graft have been the subject of discussion in relation to possible early complications and outcomes of simultaneous pancreas and kidney transplantation. This issue needs additional research.The objective was to evaluate how the intraoperative hemodynamic parameters may influence early results of simultaneous pancreas and kidney transplantation.Material and methods. The retrospective study was conducted to analyze the impact of intraoperative hemodynamic parameters on the early results of treatment in 83 patients who underwent simultaneous pancreas and kidney transplantation in the N.V. Sklifosovsky Research Institute for Emergency Medicine in the period from 2008 to 2023.Given the primary ROC analysis results, we allocated the patients into 2 groups, according to their mean arterial pressure (MAP) values at reperfusion. Group I consisted of patients with MAP<90 mmHg (n=21), group II included patients with MAP>90 mmHg (n=62). The characteristics of donors and recipients were comparable between the groups (p>0.05). The intraoperative hemodynamic parameters of the recipients (MAP, central venosus pressure, heart rate) were analyzed at the beginning of surgery, at reperfusion stages, at the time of making the interintestinal anastomosis, and on surgery completion; the incidence of postoperative complications was studied; the primary functions of the kidney and pancreas grafts were evaluated; the in-hospital graft and recipient survival rates were calculated.Results. The median values of MAP (mm Hg) were significantly lower in group I compared to those in group II at all stages of surgery, except for the surgery beginning: 87 (86;87) mmHg versus 101 (97;104) mmHg at the time of the kidney graft reperfusion; 89 (83;95) mmHg versus 97 (93;102) mmHg at the time of the pancreatic graft reperfusion; 91 (85;95) mmHg versus 97 (89;99) mmHg at the time of making interintestinal anastomosis; 90 (82;100) mmHg and 103 (90;116) mmHg on surgery completion, respectively (p<0.05). The remaining hemodynamic parameters had no statistically significant differences between the groups (p>0.05). There were no statistically significant differences between the groups in the incidence of postoperative complications, either (p>0.05). The rate of primary kidney graft function was significantly higher in group II (96.8%; n=60) compared to group I (42.9%; n=11) (p<0.05). All recipients displayed a primary pancreatic graft function. The median hospital length of stay in group I days was statistically significantly longer compared to that of the patients in group II, making 45 (28.5;72) versus 34.5 (25;60) days, respectively (p<0.05).The hospital survival rates of kidney grafts, pancreas grafts and recipients were significantly higher in patients of group II compared to those in patients of group I: 93.5% (n=58), 87.1% (n=54), and 96.8% (n=60) versus 57.1% (n=12), 57.1% (n=12), and 66.7% (n=14), respectively (p<0.05).Conclusion. MAP 90 mmHg at the timepoint of reperfusion is a factor that has a statistically significant effect on the primary function of a kidney graft in the early postoperative period, associates with the increase in hospital survival rates of grafts and recipients at early stages after simultaneous pancreas and kidney transplantation.

Hydrogen Production by Formic Acid Decomposition with Nanoscale Zero-Valent Iron (nZVI): Effects of nZVI Dosage, Temperature and Time

Amidst growing interest in renewable hydrogen gas production, this paper examines three important parameters affecting hydrogen production via formic acid decomposition reaction with nanoscale zero-valent iron (nZVI). The study investigates variations in nZVI dosage (200 – 1000 g/L), reaction temperature (25 – 75°C), and reaction time (5 -30 minutes) to identify optimum conditions for maximum hydrogen yield. Results indicate that the maximum hydrogen yield occurred at nZVI dosage, reaction temperature, and time of 800 g/L, 25°C and 30 minutes, respectively, yielding approximately 215 mL of hydrogen at optimal parameter values. The synthesized nZVI was also analysed before and after the reaction, focusing on the specific surface area and pore size of the nZVI. The results from BET characterization regarding specific surface area and pore size are consistent with experimental results, suggesting smaller pores correspond to higher surface area, enhancing reactivity with formic acid to produce hydrogen gas. Conversely, larger pore sizes after the reaction signify reduced surface area and lower reactivity of nZVI.

Joint extented Kalman filter-based parameter estimation in networked SEIR models with stochastic variability: analysis using COVID-19 daily data from southern India

Graph Laplacian diffusion is utilised to augment the susceptible-infected-removed (SEIR) epidemic model by integrating a weighted network and introducing variability in the transmission rate parameter to simulate population movement across network nodes. Employing the Joint Extended Kalman filter (JEKF), our objective is to derive three critical parameters: the transmission rate (β), recovery rate (γ), and latent period rate (α). The JEKF's iterative approach continuously anticipates and updates the system state based on current parameter estimations, making it adaptable for parameter estimation in dynamic systems. Integration of model predictions with observed data further enhances accuracy. Subsequently, we offer visual insights and demonstrate our methodology through experiments conducted on a simple tree network and a small-world Watts-Strogatz graph. Our ultimate aim is to precisely determine optimal parameter values to formulate effective COVID-19 management plans tailored for South India.

A Parametric Six-Step Method for Second-Order IVPs with Oscillating Solutions

In this paper, we develop an explicit symmetric six-step method for the numerical solution of second-order initial value problems (IVPs) with oscillating solutions. The proposed method is phase-fitted and incorporates a free coefficient as a parameter to optimize its performance. By exploring a wide range of values for this parameter, we computationally determine the periodicity interval. The objective of this procedure is to identify the range of the parameter’s values for which the method remains stable. Based on the output from the periodicity interval analysis, we then aim to define the optimal values for the parameter by numerically solving three initial value problems. The results guided us in identifying these optimal values and confirm the high efficiency of the new method. The method’s efficiency is further validated for the chosen optimal parameter value for specific oscillatory problems, where it is compared with well-known phase-fitted methods.

A High-Precision Real-Time Temperature Acquisition Method Based on Magnetic Nanoparticles

The unique magnetothermal properties of magnetic nanoparticles enable the development of a high-precision, real-time, noninvasive temperature measurement method with significant potential in the biomedical field. Based on a low-frequency alternating magnetic field excitation model, we construct two additional magnetic field excitation models—alternating current–direct current superposition and dual-frequency superposition—to extract harmonic amplitude components from the magnetization response. To increase the accuracy of harmonic information acquisition, the effects of the truncation error, excitation magnetic field frequency, and amplitude are thoroughly analyzed, and optimal parameter values are selected to minimize the error. A single algorithm is designed for temperature inversion, and a joint algorithm is proposed to optimize the performance of the single algorithm. Under low-frequency alternating-current magnetic field excitation, the autonomous group particle swarm optimization method achieves superior real-time performance in terms of temperature inversion and running time. Compared with the opposition learning gray wolf optimizer and particle swarm optimization–gray wolf optimization, the proposed method achieves reductions of 52% and 68%, respectively. Additionally, under dual-frequency superimposed magnetic field excitation, a higher temperature inversion accuracy is achieved compared with that of the particle swarm optimization–gray wolf optimization algorithm, reducing the error from 0.237 K to 0.094 K.

Enhanced Monte Carlo Techniques for Solving Linear Systems

Abstract A novel hybrid Monte Carlo algorithm for solving systems of linear algebraic equations is introduced and evaluated in this study. This algorithm builds on the recent “Walk on Equations” Monte Carlo method developed by Ivan Dimov, Sylvain Maire, and Jean Michel Sellier. For the first time, this approach has been compared with the Gauss-Seidel method for matrices of big size. Significant improvements have been made to the algorithm by selecting optimal values for the relaxation parameters, resulting in a considerable reduction in computational time and lower relative errors across a specified number of iterations. The convergence of the algorithm has been rigorously established through a new theorem. It has been demonstrated that the original algorithm can be optimized by properly balancing the iteration matrix. Additionally, a sequential Monte Carlo method developed by John Halton, which utilizes the control variate method iteratively, has also been implemented.

Parameter adaptive system to learning processes for emerging serious games using cultural algorithms

One of the general problems of serious games is their poor ability to adapt to players while they play. One response to that need has been emerging serious games, which allow for real-time adjustment of serious games. To do this, they can use different approaches such as adjusting their strategies, and their plots, among others. In this paper, an adaptive system of parameters for emerging serious games is proposed. This adaptive system enables the emergence of parameters in a serious game to adapt it to an educational environment where it takes place. In particular, this adaptive system uses a cultural algorithm to modify the parameters of the emerging serious games in real-time based on the information of the learning process where it is used. This system is evaluated in a smart classroom for the parameter adaptation of several serious games used in it. Thus. in each of them, it is able to obtain the optimal values of the serious game parameters that allow the players to achieve the objectives of the serious game (a minimum number of points to achieve) in an optimal playing time. The most relevant scientific contribution of the work is the development of an architecture based on Cultural Algorithms to adapt the parameters of a serious game to the characteristics of the players. The results are very encouraging because the serious games are adapted to the behavior of the students in the smart classroom.

Inkjet printing of sustainable ZTO/AlOx thin film transistors

Abstract Even though printed metal oxide thin film transistors (TFTs) have been a central topic of research in the past decade, the most notable results still require scarce elements such as gallium and indium, or high annealing temperatures (≥ 400 °C) when used non-critical raw materials such as zinc and tin.
In this work, safe, abundant and inexpensive materials such as zinc, tin and aluminum are explored to reach low-cost thin films and devices with both the semiconductor and dielectric layers deposited by inkjet printing and annealed at lower temperatures (300 °C). Alumina (AlOx) and zinc tin oxide (ZTO) inks containing a theoretical optimal V% of ethylene glycol (EG) were optimized for production of uniform and reproducible AlOx/ZTO thin film layers. Common ink parameters (such as the reverse Ohnesorge, capillary, Webber and Reynolds numbers) were evaluated and compared with relevant literature on inkjet drop formation mechanisms. Inks within theoretical optimal parameter values were printing optimized in terms of drops per inch, number of layers, UV substrate surface activation, print speed, and post annealing. A high-quality dielectric of two alumina layers was printed, having a breakdown field above 2.93 ± 0.33 MV.cm-1, and a dielectric constant of 7.74 ± 0.73 at 1 kHz. Thin film transistors (TFTs) of inkjet printed (IJP) ZTO/AlOx layers were produced with a maximum IOn/IOff ratio of 103 and a saturation mobility of 2.2 cm2.V-1.s‑1. This approach not only advances the field of printed electronics but also addresses concerns related to material scarcity, thermal budget, and production costs.

Relating Rainfall Retrieval Parameters to Network and Environmental Features to Improve Rainfall Estimates from Commercial Microwave Links in the Tropics

Abstract Potentially, the greatest benefit of commercial microwave links (CMLs) as opportunistic rainfall sensors lies in regions that lack dedicated rainfall sensors, most notably low- and middle-income countries. However, current CML rainfall retrieval algorithms are predominantly tuned and applied to (European) CML networks in temperate or Mediterranean climates. This study investigates whether local quantitative precipitation estimates from CMLs in a tropical region, specifically Sri Lanka, can be improved by optimizing two dominant parameters in the rainfall retrieval algorithm RAINLINK, namely, the wet antenna attenuation correction factor Aa and the relative contribution of minimum and maximum received signal levels α. Using a grid search, based on 10 months of CML data from 22 link–gauge clusters consisting of 105 sublinks that lie within 1 km of a daily rain gauge, the optimal values of Aa and α are first derived for the entire country and compared to the default RAINLINK values. Subsequently, the CMLs are grouped by link length, frequency, climate zone, and daily rainfall depth classes, and Aa and α are derived for each of these classes. Calibrating parameters on all clusters across the country only leads to minor improvements. The actual optimal Aa and α values depend on the performance metric favored. Calibrating on network properties, particularly short link length and high-frequency classes, does significantly improve rainfall estimates. By relating the optimal Aa and α values to known network metadata, the results from this study are potentially applicable to other tropical CML networks that lack nearby reference rainfall data. Significance Statement The purpose of this study is to improve rainfall estimates from commercial microwave links in Sri Lanka by optimizing two important rainfall retrieval algorithm parameters. Our results show that relating the optimal parameter values to operating frequency and pathlength improves rainfall estimates more than applying a single optimal parameter set to the entire network. By relating the optimal parameter values to readily known network properties, we aim to make these results applicable to other tropical countries, particularly low- and middle-income countries, that lack adequate reference rainfall data to calibrate rainfall estimates from commercial microwave links on.

Simultaneous extraction and determination of Norfloxacin and Lomefloxacin based on magnetic dual-template molecularly imprinted polymer in water samples under multivariate optimization

In the current investigation, we developed a pretreatment strategy for samples comprising Norfloxacin (NOR) and Lomefloxacin (LOM) using the molecularly imprinting technique. NOR and LOM were extracted from water samples using a magnetic dual-template molecularly imprinted polymer (Fe3O4@NH2@MIP), which was synthesized, and studied by SEM and TEM. A central composite design optimized the main experimental variables, such as pH, Fe3O4@NH2@MIP, ionic strength, contact time, and eluent solvent. The study determined that a pH of 4, 15 mg of Fe3O4@NH2@MIP, 2 % ionic strength, 4 min of contact time, and 165 μL of eluent solvent were the optimal values of extraction parameters. With determination coefficients (R2) over 0.99, the calibration curves showed good linearity within the range of 2–500 ng mL−1 under optimal circumstances. The results showed that the limits of detection (LODs) for LOM and NOR were 0.5 and 0.4 ng mL−1, respectively. Additionally, 1.33 and 1.67 ng mL−1 were obtained as the limits of quantification (LOQs) for NOR and LOM, respectively. Additionally, it was discovered that the enrichment factors for NOR and LOM were 69.25 and 66.83, respectively. Fe3O4@NH2@MIP has a high adsorption capability (41.36 and 38.95 mg g−1 for NOR and LOM, respectively) and positive imprinting effects (4.35 and 4.30 for NOR and LOM, respectively), according to the findings of the adsorption and enrichment investigation. Furthermore, the recoveries (89.4 % - 94.3 %) for the spiked samples were satisfactory, with relative standard deviations ≤4.75.

Control of Technological Parameters of Detonation Spraying for Obtaining Coatings Based on Titanium Dioxide with Specified Wetting Properties

The study of the water-repellent properties of the surface of a metal-ceramic coating for protective purposes based on dioxide titanium is carried out. It is shown that the water-repellent properties of the coating surface can be effectively changed by varying the technological parameters of spraying. During the production of coatings, such technological parameters as the distance from the substrate to the barrel of the detonation gun and the speed of its passage are changed. A pattern is identified that connects the technological parameters of detonation coating spraying and the contact angle of wetting. It is established that the dependence of the contact angle on the spraying distance obeys a parabolic law under certain conditions. The parameters of phenomenological equation that adequately describes the observed parabolic dependence are calculated. The optimal values of the technological parameters of detonation spraying necessary to achieve maximum hydrophobicity of the produced coatings have been determined.

Intelligent hybrid approaches utilizing time series forecasting error for enhanced structural health monitoring

Over the past decade, the growing importance of machine learning-based structural health monitoring (SHM) for early-stage damage detection has become evident. Time series forecasting, using deep learning, has emerged as a key focus, significantly contributing to improving damage detection, localization, and quantification processes. Researchers in SHM have conducted numerous studies utilizing neural networks based on time series forecasting, grounded in traditional methods. This study diverges from existing research by directly incorporating neural network prediction errors in time series for detecting, localizing, and quantifying damage. The proposed methods are well-suited for online structural monitoring. They eliminate the need for data classification methods and damage-sensitive feature extraction techniques by relying solely on training the neural network with data from structurally sound conditions. However, the testing process does require data from damaged conditions. To address the non-linear and non-stationary characteristics of the signals, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method is applied for signal processing. This method processes response signals (i.e., time series) from three well-known benchmark structures: the University of Central Florida structure, the Qatar University Grandstand Simulator, and the Z24 Bridge. Subsequently, the first intrinsic mode function (IMF) obtained from signal decomposition is independently input into Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for time series prediction. Optimal parameter values for the LSTM and GRU neural networks are chosen using the Bayesian Optimization (BO) algorithm before the prediction process. By introducing three indices—Statistical Distance Function (SDF), error index, and accuracy index—the evaluation not only emphasizes the accuracy of the methods but also explores the localization and quantification of damage. The results demonstrate that both ICEEMDAN-BO-LSTM-SDF and ICEEMDAN-BO-GRU-SDF methods have successfully achieved accurate detection, localization, and quantification without the need for data classification and damage-sensitive feature extraction methods, and merely by utilizing data from healthy states for neural network training.

Parametric Optimization of Surface Textures in Oil-Lubricated Long-Life Aircraft Valves

Parametric Optimization of Surface Textures in Oil-Lubricated Long-Life Aircraft Valves

Treatment of molasses-based alcohol distillery wastewater using Electro-Fenton oxidation: Optimization and model prediction by response surface methodology

In this study, an Electro-Fenton process was employed to treat high-strength alcohol distillery wastewater. The simultaneously removal of chemical oxygen demand (COD), color, and turbidity were examined. The optimum value of the operational parameters including the number of electrodes and their arrangement, electrodes’ interval, initial pH, and electrolyte concentration were determined by one-factor-at-a-time (OFAT). The effect of essential parameters including current density, hydrogen peroxide concentration, and the duration of the process were optimized using response surface methodology (RSM). At fixed defined operational conditions by OFAT as four electrodes in a parallel arrangement with a 1 cm interval, 0.3 mol/L of electrolyte salt, and pH of 4, respectively, the optimal condition by RSM suggested the current density of 24 mA/cm2, 40 g/L of H2O2, and reaction time of 2 h. By adopting optimal condition at defined values of operational factors the removal efficiencies for COD, color, and turbidity were approximately 83%, 99%, and 97%, respectively, using fresh water for dilution. The energy consumption of 1.9 kWh/kg COD removal was achieved. Also, to save water, the treated wastewater (instead of fresh water) was used for the dilution of the raw wastewater, indicating the satisfactory results with removal efficiencies for COD, color, and turbidity of 71%, 94%, and 95%, respectively. Overall, the Electro-Fenton process is introduced as an appropriate process for treating high-strength alcohol distillery wastewater.

Trade Off Analysis Between Fixed‐Time Stabilization and Energy Consumption of Nonlinear Stochastic Systems

ABSTRACTThe trade off analysis between the fixed‐time stabilization in probability and energy consumption of nonlinear stochastic system is studied in this paper. By constructing a switching controller and using inequality techniques, sufficient conditions for fixed‐time stabilization in probability in the Lyapunov sense are given, and the upper bounds of the settling time function and energy consumption are estimated. Then, by analyzing the relationship between control parameters, control time and energy consumption, the existence of trade off between control time and energy consumption is proposed, and the corresponding optimal parameter values are given. Finally, a numerical example is used to verify the validity of the theoretical results.

A novel integrated nutrition-combined prognostic index for predicting overall survival after radical gastrectomy.

The objectives of this study were to integrate the Prognostic Nutritional Index, Controlling Nutritional Status, and Nutritional Risk Index, into a novel Nutrition-combined Prognostic Index (NCPI), and to develop and validate a nomogram to predict overall survival (OS) in patients with gastric cancer (GC). Data from 609 patients with GC, collected between January 1, 2017, and April 30, 2023, were retrospectively analyzed. Optimal cut-off values for nutritional parameters were determined using X-Tile software, and the Kaplan-Meier method applied for survival analysis. Univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were conducted, and a nomogram for predicting OS in patients with GC constructed and validated. Inferior nutritional status was strongly correlated with worse clinicopathologic features and prognosis of patients with GC. NCPI, body mass index, American Joint Committee on Cancer T stage, and lymph node ratio were identified as independent risk factors for OS. A nomogram including these factors predicted 1-, 3-, and 5-year OS, with training and validation set C-index values of 0.716 and 0.77, respectively. Calibration curves demonstrated that the predicted outcomes closely matched the actual results, and decision curve analysis highlighted the high practical value of the model. The novel nutritional marker, NCPI, is closely associated with the clinicopathologic features and OS of patients with GC. The practical value of the NCPI-based nomogram was demonstrated and a web-based calculator developed.

Reducing measurement costs by recycling the Hessian in adaptive variational quantum algorithms

Abstract Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with Adaptive Derivative-Assembled Problem-Tailored variational quantum eigensolver (ADAPT-VQE), an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to optimal parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the approximate Hessian in the state of the optimizer to be consistently closer to the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical implementation of a quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.

Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence

In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Through numerical and physical experiments, we demonstrate that optimizing the evaluation function based on the extended power-divergence and weighted extended power-divergence measures yields high-quality image reconstruction. Notably, the optimal parameter values derived from the proposed method produce reconstruction results comparable to those obtained using the true image, even when using distance functions based on differences between forward projection data and measured projection data, as verified by numerical experiments. These results suggest that the proposed method effectively improves reconstruction quality without the need for machine-learning techniques in parameter selection. Our findings also indicate that this approach is useful for enhancing the performance of iterative reconstruction algorithms, especially in medical imaging, where high-accuracy reconstruction under noisy conditions is required.

Improved SVM Classification Using Particle Swarm Optimization for Student Completion Prediction System

Timely completion of a study program is crucial for evaluating the quality of universities. To achieve timely completion, student’s progress needs to be monitored early in order to ensure that they can complete the given task on time. This process is particularly important because universities often enroll thousands of students, thereby making individual supervision impractical. An effective solution to this problem is leveraging machine learning to develop a system that predicts whether student will complete the study without delay. Therefore, this study used Support Vector Machine (SVM) method for classification, with RBF kernel. Optimization of SVM classification was achieved by ensuring the values for Soft Margin C parameter and kernel parameter were correct. In addition, Particle Swarm Optimization (PSO) method was used to determine the optimal SVM parameter values. Consequently, the resulting model was evaluated using Cross Fold Validation. The optimized SVM parameter identified through PSO were gamma of 0.0085 and C of 0.4196. The average training accuracy recorded is 82.58%, with 81.22% validation, these results can be categorized into Good Classification. Finally, the application of PSO in optimization resulted in SVM models that avoided overfitting, as shown by the closeness of training and validation values.

A time-relaxation reduced order model for the turbulent channel flow

Regularized reduced order models (Reg-ROMs) are stabilization strategies that leverage spatial filtering to alleviate the spurious numerical oscillations generally displayed by the classical Galerkin ROM (G-ROM) in under-resolved numerical simulations of turbulent flows. In this paper, we propose a new Reg-ROM, the time-relaxation ROM (TR-ROM), which filters the marginally resolved scales. We compare the new TR-ROM with the two other Reg-ROMs in current use, i.e., the Leray ROM (L-ROM) and the evolve-filter-relax ROM (EFR-ROM) and one eddy viscosity model, the mixing-length model, in the numerical simulation of the turbulent channel flow at Reτ=180 and Reτ=395 in both the reproduction and the predictive regimes. For each Reg-ROM, we investigate two different filters: (i) the differential filter (DF), and (ii) the higher-order algebraic filter (HOAF). In our numerical investigation, we monitor the Reg-ROM performance with respect to the ROM dimension, N, and the filter order. We also perform sensitivity studies of the three Reg-ROMs with respect to the time interval, relaxation parameter, and filter radius. The numerical results yield the following conclusions: (i) All three Reg-ROMs are significantly more accurate than the G-ROM. (ii) All three Reg-ROMs are more accurate than the ROM projection in terms of Reynolds stresses. (iii) With the optimal parameter values, the new TR-ROM yields more accurate results than the L-ROM and the EFR-ROM in all tests. (iv) The new TR-ROM is more accurate than the mixing-length ROM. (v) For most N values, DF yields the most accurate results for all three Reg-ROMs. (vi) The optimal parameters trained in the reproduction regime are also optimal for the predictive regime for most N values, demonstrating the Reg-ROM predictive capabilities. (vii) All three Reg-ROMs are sensitive to the filter order and the filter radius, and the EFR-ROM and the TR-ROM are sensitive to the relaxation parameter. (viii) The optimal range for the filter radius and the effect of relaxation parameter are similar for the two Reτ values.