Residual Sum Of Squares Research Articles

Analog and Photon Signal Splicing for CO2-DIAL Based on Piecewise Nonlinear Algorithm

In the CO2 differential absorption lidar (DIAL) system, signals are simultaneously collected through analog detection (AD) and photon counting (PC). These two kinds of signals have their own characteristics. Therefore, a combination of AD and PC signals is of great importance to improve the detection capability (detection range and accuracy) of CO2-DIAL. The traditional signal splicing algorithm cannot meet the accuracy requirements of CO2 inversion due to unreasonable data fitting. In this paper, a piecewise least square splicing algorithm is developed to make signal splicing more flexible and efficient. First, the lidar signal is segmented, and according to the characteristics of each signal, the best fitting parameters are obtained by using the least square fitting with different steps. Then, all the segmented and fitted signals are integrated to realize the effective splicing of the near-field AD signal and the far-field PC signal. A weight gradient strategy is also adopted in signal splicing, and the weights of the AD and PC signals in the spliced signal change with the height. The splicing effect of the improved algorithm is evaluated by the measured signal, which are obtained in Wuhan, China, and the splice of the AD and PC signals in the range of 800–1500 m are completed. Compared with the traditional method, the evaluation parameter R2 and the residual sum of squares of the spliced signal are greatly improved. The linear relationship between the AD and PC signals is improved, and the fitting R2 of differential absorption optical depth reaches 0.909, indicating that the improved signal splicing algorithm can well splice the near-field AD signal and the far-field PC signal.

Optimal Estimation of Brownian Penalized Regression Coefficients

In this paper we introduce a new methodology to determine an optimal coefficient of penalized functional regression. We assume the dependent, independent variables and the regression coefficients are functions of time and error dynamics follow a stochastic differential equation. First we construct our objective function as a time dependent residual sum of square and then minimize it with respect to regression coefficients subject to different error dynamics such as LASSO, group LASSO, fused LASSO and cubic smoothing spline. Then we use Feynman-type path integral approach to determine a Schr¨odinger-type equation which have the entire information of the system. Using first order conditions with respect to these coefficients give us a closed form solution of them.

Diffusion-based microstructure models in brain tumours: Fitting in presence of a model-microstructure mismatch

Diffusion-based biophysical models have been used in several recent works to study the microenvironment of brain tumours. While the pathophysiological interpretation of the parameters of these models remains unclear, their use as signal representations may yield useful biomarkers for monitoring the treatment and the progression of this complex and heterogeneous disease. Up to now, however, no study was devoted to assessing the mathematical stability of these approaches in cancerous brain regions. To this end, we analyzed in 11 brain tumour patients the fitting results of two microstructure models (Neurite Orientation Dispersion and Density Imaging and the Spherical Mean Technique) and of a signal representation (Diffusion Kurtosis Imaging) to compare the reliability of their parameter estimates in the healthy brain and in the tumoral lesion. The framework of our between-tissue analysis included the computation of 1) the residual sum of squares as a goodness-of-fit measure 2) the standard deviation of the models’ derived metrics and 3) models’ sensitivity functions to analyze the suitability of the employed protocol for parameter estimation in the different microenvironments. Our results revealed no issues concerning the fitting of the models in the tumoral lesion, with similar goodness of fit and parameter precisions occurring in normal appearing and pathological tissues. Lastly, with the aim of highlight possible biomarkers, in our analysis we briefly discuss the correlation between the metrics of the three techniques, identifying groups of indices which are significantly collinear in all tissues and thus provide no additional information when jointly used in data-driven analyses.

Contactless temperature and distance measuring device: A low-cost, novel infrared ―based "Badge"-shaped structural model for measuring physical distance and body temperature

<abstract> <p>This work eases the feasibility of infrared thermometer application and reliability to introduce a novel design with upgraded applications & functions. The custom-designed compact device "Badge" structured comprises the operative methods through the electronic packages of an optimal level. The physical and social distance measured by the ToF (Time of Flight) infrared laser sensor within 1 m from the subject and the measuring equipment (MLX90632 SMD QFN and VL530LX ToF). When the distance is not maintained, or the physical distance condition is not met, the flashing LED, or vibration should trigger an indication (warning for physical distancing and alteration for pyrexia warning, respectively). Statistical analysis and simulation-based studies criticized the accuracy of ±0.5°F and relational model of the independent and dependent variable for this device with significant R<sup>2</sup> = 0.99 and P < = 1; values with the lowest accuracy error of ±0.2°F and least residual sum of squares 0.01462 values. The portable, lightweight, and dynamic body temperature monitoring altered the application from static to continuous, complete structural design. This alternative provides the best technique to combine worn (personnel) medical devices with primary healthcare instruments to help body temperature measurements that are not contactable, fast, and accurate. It builds a way of processing through the protocol Covid-19.</p> </abstract>

Study of the drying behaviour and color kinetics of convective drying of yam (Dioscorea hispida) slices

Drying is a crucial step in processing for enhancing the storability of yam (Dioscorea hispida) slices. In this present work, the drying of yam slices was carried out in a lab-scale auto weighing forced convective hot air drier at a temperature span of 50–70 0C after 30 min of boiling. The study focused on drying behavior, fitting mathematical models to drying and color kinetics, examining the texture and rehydration ratio of dries yam slices. To exhibit the mass and color change characteristics, experimental data were fitted to seven drying kinetics and two color kinetics models. Model fitness was compared and selected based on chi-square (X2), goodness of fit (R2), residual sum of squares (RSS), and root mean square error (RMSE). Moisture diffusivity ranged from 4.5133 × 10−10- 7.522 × 10−10 m2/s and increased with temperature. The activation energy of 23.53 kJ/mol was obtained. Midilli-kucuk model confirmed the superior fitting to experimental data followed by two-term model. Color kinetics was determined using L, a, b values, total color change (∆E), chroma, hue and browning index. L value reduced with drying time, while b and a value increased. Zero order kinetics fitted a and ∆E values adequately while first order kinetics described the changes in L and b values. It can be concluded that the Midilli-kucuk model can be used for predicting drying process behavor, system design of convective hot air drying of Dioscorea hispida for industrial scale up.

Curve fitting and jump detection on nonparametric regression with missing data

In this paper, by virtual of the inverse probability weighted technique, we considered the jump-preserving estimation on the nonparametric regression models with missing data on response variable. First, we used local piecewise-linear expansion respectively with left and right kernel to approximate the unknown regression function. Second, we obtained the left- and right-limit estimation of regression function at each observed points and then determinated the final estimators by residual sums of squares. Third, we presented the convergence rate of estimators and the residual sums of squares. Finally, we illustrated the performance of our proposed method through some simulation studies and a conjunctivitis example from The Affiliated Hospital of Hangzhou Normal University.

Impact of the COVID-19 Pandemic on Objectively Measured Physical Activity and Sedentary Behavior Among Overweight Young Adults: Yearlong Longitudinal Analysis.

BackgroundThe COVID-19 pandemic has impacted multiple aspects of daily living, including behaviors associated with occupation, transportation, and health. It is unclear how these changes to daily living have impacted physical activity and sedentary behavior.ObjectiveIn this study, we add to the growing body of research on the health impact of the COVID-19 pandemic by examining longitudinal changes in objectively measured daily physical activity and sedentary behavior among overweight or obese young adults participating in an ongoing weight loss trial in San Diego, California.MethodsData were collected from 315 overweight or obese (BMI: range 25.0-39.9 kg/m2) participants aged from 18 to 35 years between November 1, 2019, and October 30, 2020, by using the Fitbit Charge 3 (Fitbit LLC). After conducting strict filtering to find valid data on consistent wear (>10 hours per day for ≥250 days), data from 97 participants were analyzed to detect multiple structural changes in time series of physical activity and sedentary behavior. An algorithm was designed to detect multiple structural changes. This allowed for the automatic identification and dating of these changes in linear regression models with CIs. The number of breakpoints in regression models was estimated by using the Bayesian information criterion and residual sum of squares; the optimal segmentation corresponded to the lowest Bayesian information criterion and residual sum of squares. To quantify the changes in each outcome during the periods identified, linear mixed effects analyses were conducted. In terms of key demographic characteristics, the 97 participants included in our analyses did not differ from the 210 participants who were excluded.ResultsAfter the initiation of the shelter-in-place order in California on March 19, 2021, there were significant decreases in step counts (−2872 steps per day; 95% CI −2734 to −3010), light physical activity times (−41.9 minutes; 95% CI −39.5 to −44.3), and moderate-to-vigorous physical activity times (−12.2 minutes; 95% CI −10.6 to −13.8), as well as significant increases in sedentary behavior times (+52.8 minutes; 95% CI 47.0-58.5). The decreases were greater than the expected declines observed during winter holidays, and as of October 30, 2020, they have not returned to the levels observed prior to the initiation of shelter-in-place orders.ConclusionsAmong overweight or obese young adults, physical activity times decreased and sedentary behavior times increased concurrently with the implementation of COVID-19 mitigation strategies. The health conditions associated with a sedentary lifestyle may be additional, unintended results of the COVID-19 pandemic.

How Does the Effect Fade over Distance? An Inquiry into the Decay Pattern of Distance Effect on Property Values in the Case of Taipei, Taiwan

It is generally accepted that the perception of homeowners towards certain potential risks or amenities fades as distance from the risk or amenity increases. This study aims to illustrate the distance–decay pattern with an appropriate mathematical function. Distance–decay functions and parameters that yield the minimum residual sum of squares (RSS) for a given regression model are considered to be the optimal approximation for the pattern of decay. The effect of flood risk and mass rapid transit (MRT) accessibility on residential housing prices in Taipei, Taiwan, are used as examples to test the optimization process. The results indicate that the type of distance function affects both the significance and the magnitude of the regression coefficients. In the case of Taipei, concave functions provide better fits for both the flood risk and MRT accessibility. RSS reduction is up to 10% compared to the blank. Surprisingly, the impact range for the flood risk is found to be larger than that for MRT accessibility, which suggested that the impact range of perception for uncertain risks is larger than expected.

Mechanism of Electrocatalytic Wet Air Oxidation of PPCPs over Solid Catalysts: Kinetic Insight with a Universal Model

Electrocatalytic wet air oxidation (ECWAO) is a novel wet air oxidation technology using an anodic electric field to stimulate oxygen for catalytic oxidation of water pollutants at ambient condition. Herein, a mechanistic kinetic model is established based on the degradation of bisphenol A in an ECWAO process with manganese oxide (MnOx) catalysts, and the general applicability of the model is verified by triclosan degradation over a Ni@NiO catalyst and sulfamethoxazole degradation over a MnMoOx catalyst. Elementary steps involved in solid-catalyzed reaction and the assistant role of anodic electric field in the catalysis are taken into consideration. A group of kinetic equations based on the Langmuir–Hinshelwood–Hougen–Watson (LHHW) or Eley–Rideal (ER) rate laws are evaluated using the criteria of the minimization of the residual sum of squares and average error. The results show that the ECWAO reaction is best described by the LHHW model in which the surface reaction between adsorbed oxygen and adsorbed organic molecules is the rate-determining step. The key parameters controlling ECWAO reaction kinetics are the adsorption equilibrium constant of oxygen and rate constant of the surface reaction. According to the kinetic model, the reaction rates of organic pollutants are nearly linear dependent upon their concentrations within a certain range. With the increment of the circuit current, the reaction rates of organic pollutants first rise, then reach a plateau, and finally decline. The mechanistic kinetic model proves to be applicable for interpreting and predicting the ECWAO rates of organics over the transition metal oxide catalysts.

Effect of distribution modulus (q) on the properties and microstructure development of a sustainable Ultra-High Performance Concrete (UHPC)

This paper discusses the effect of distribution modulus (q) on the properties and microstructure development of a sustainable Ultra-High Performance Concrete (UHPC) incorporating gold tailings. To be specific, the Modified Andreasen and Andersen (MAA) model is used to design the UHPC matrix, in which a previously seldom focused value --- distribution modulus (q) is carefully investigated here. The deviation between experimental mixtures and different target curves (with different q) are concluded based on Weighted Residual Sum of Squares (WRSS). Then, the fresh and hardened behaviors of the developed UHPCs with different q value are evaluated. The obtained experimental results show that the declination of q can increase the wet packing density of UHPC. Besides, when the distribution modulus (q) is controlled in the range of 0.17–0.19, a sustainable UHPC with advanced properties and microstructure can be produced. For instance, the compressive strength of the optimum UHPC matrix in this study can reach about 136.2 MPa after curing in standard condition for 28 days. This implies that the normally selected distribution modulus (q) value – 0.23 can not always guarantee the maximum packing density and best characteristics for UHPC matrix.

A Review of Well-Known Robust Line Search and Trust Region Numerical Optimization Algorithms for Solving Nonlinear Least-Squares Problems

The conditional, unconditional, or the exact maximum likelihood estimation and the least-squares estimation involve minimizing either the conditional or the unconditional residual sum of squares. The maximum likelihood estimation (MLE) approach and the nonlinear least squares (NLS) procedure involve an iterative search technique for obtaining global rather than local optimal estimates. Several authors have presented brief overviews of algorithms for solving NLS problems. Snezana S. Djordjevic (2019) presented a review of some unconstrained optimization methods based on the line search techniques. Mahaboob et al. (2017) proposed a different approach to estimate nonlinear regression models using numerical methods also based on the line search techniques. Mohammad, Waziri, and Santos (2019) have briefly reviewed methods for solving NLS problems, paying special attention to the structured quasi-Newton methods which are the family of the search line techniques. Ya-Xiang Yuan (2011) reviewed some recent results on numerical methods for nonlinear equations and NLS problems based on online searches and trust regions techniques, particularly on Levenberg-Marquardt type methods, quasi-Newton type methods, and trust regions algorithms. The purpose of this paper is to review some online searches and trust region's more well-known robust numerical optimization algorithms and the most used in practice for the estimation of time series models and other nonlinear regression models. The line searches algorithms considered are: Gradient algorithm, Steepest Descent (SD) algorithm, Newton-Raphson (NR) algorithm, Murray’s algorithm, Quasi-Newton (QN) algorithm, Gauss-Newton (GN) algorithm, Fletcher and Powell algorithm (FP), Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. While the only trust-region algorithm considered is the Levenberg-Marquardt (LM) algorithm. We also give some main advantages and disadvantages of these different algorithms.

Mathematical Modelling of Infra-Red Evaporation Characteristics of Wheat Straw Black Liquor

Infrared (IR) evaporation characteristics of Weak Soda Black Liquor (WSBL) were determined at five different temperatures of 80, 90, 100, 110 and 120oC. The effect of constant temperature on evaporation rate and moisture content (on a dry basis) of 1.5 gm approx. WSBL tests were contemplated and required a careful time frame of IR dissipation to vanish the dampness content at a different consistent temperature. The dissipation rate expanded with expanding infrared temperature. Therefore, different numerical models, such as Page and Logarithmic, Henderson, Pabis and Lewis, were utilised to fit the experimental data properly. A Gaussian model equation was developed for evaporation rate and moisture fraction of black liquor. The probable empirical parameters, along with the relating of reduced chi-square (X2), Residual Sum of Square (RSS), and coefficients of determination (adjusted R2) from non-linear regression analysis of all the numerical model equations, were examined. In addition, the effect of evaporation temperature on the water removal rate, the effective diffusion coefficient and activation energy were also estimated. The effective diffusion coefficient ranges from 2.67 × 10–10 m2/s to 10.4 × 10–10 m2/s, and the activation energy was 39.19 kJ/mol. The statistical indicators (chi-square and determination coefficient) showed that the Decay model equation and Gaussian equation are the most suitable models for describing the evaporation process of WSBL.

The Influence of Global Financial Liquidity on the Indonesian Economy: Dynamic Analysis with Threshold VAR

Empirical studies of the global liquidity spillover on Indonesia’s economy are still relatively limited. Most of the global contagion literature on Indonesia’s economy focuses only on the effects of real shock (on output) due to financial shock. We assert that the effect of global output on Indonesia macroeconomic conditions is a fairly relevant issue to be studied. This research aims to investigate the interdependent relationships between world GDP, world commodity prices, world inflation, trade flows, capital inflow, capital account transactions, reserve accumulation, global liquidity (e.g., global broad money), and monetary aggregates, with regard to Indonesia’s GDP variables and inflation. This paper uses threshold vector autoregression (TVAR) to capture regime changes in the variables of the world economy. World economic data and Indonesia’s economic data were utilized to prove different responses to the world economic situation in two different regimes. This research identified two groups of upper regime and lower regime world variables—namely, world inflation, world GDP, and world commodity prices. TVAR estimation resulted in a smaller residual sum of squares compared to VAR estimation. Different regimes resulted in differences in Indonesia’s economic responses due to the shock of world economic variables. The findings generated by this research are expected to be insightful to monetary policymakers in Indonesia.

Indices reflecting muscle contraction performance during exercise based on a combined electromyography and mechanomyography approach

Electromyography (EMG) and mechanomyography (MMG) have been used to directly evaluate muscle function through the electromechanical aspect of muscle contraction. The purpose of this study was to establish new absolute indices to describe muscle contraction performance during dynamic exercise by combining EMG and displacement MMG (dMMG) measured simultaneously using our previously developed MMG/EMG hybrid transducer system. Study participants were eight healthy male non-athletes (controls) and eight male athletes. EMG and dMMG of the vastus medialis were measured for 30 s during four cycles of recumbent bicycle pedaling (30, 60, 90, and 120 W) and on passive joint movement. Total powers were calculated based on the time domain waveforms of both signals. Muscle contraction performance was verified with the slope of regression line (SRL) and the residual sum of squares (RSS) obtained from EMG and dMMG correlation. EMG and dMMG has increased with the work rate. Force and EMG were similar between groups, but dMMG showed a significant difference with load increase. Athletes had significantly higher SRL and significantly lower RSS than controls. The average value divided by SRL and RSS was higher in athletes than in controls. The indices presented by the combined approach of EMG and dMMG showed a clear contrast between the investigated groups and may be parameters that reflect muscle contraction performance during dynamic exercise.

Osmotic, osmovacuum, sonication, and osmosonication pretreatment on the infrared drying of Ginkgo seed slices: Mass transfer, mathematical modeling, drying, and rehydration kinetics and energy consumption.

This study evaluated the mass transfer, drying, and rehydration kinetics (drying and rehydration curve, moisture diffusivity [Deff ]), energy consumption (specific energy consumption [SEC], moisture extraction rate (MER), and specific moisture extraction rate [SMER]), and mathematical modeling of infrared dried Ginkgo biloba seed (GBS) using the various nonthermal pretreatments namely: osmotic (OS), osmovacuum (V + OS), ultrasound (US, ginkgo seed immersed in a distilled water with US), and osmosonication (US + OS, ginkgo seeds immersed in an OS solution with US). Results showed that various pretreatments affected mass transfer, drying, and rehydration characteristics, and energy consumption, which was confirmed by principal component analysis. In terms of mass transfer, US pretreatment recorded the highest weight loss while the osmosonication pretreatment registered the highest solid gain. The entire drying process occurred in the falling-rate period. The Deff values were within the normal range of agroproducts (10-11 to 10-8 m2 /s). The modified Page-I and Weibull model best fitted the drying and rehydration kinetics, respectively, with the coefficient of determination (R2 )>0.991, root mean square error, residual sum of squares, and reduced chi-square closer to zero, compared with the other models. The untreated GBS (control) had the lowest energy efficiency (lowest SMER and MER) and the highest SEC than the pretreated GBS. Among the various pretreatments, the US pretreatment of GBS was superior, with the highest Deff , MER, SMER, and drying rate, and lowest drying time and SEC. Based on the findings, sequential US pretreatment and infrared drying is a feasible drying technique for GBS that could be used commercially. PRACTICAL APPLICATION: Ginkgo tree cultivation in China has exceeded market needs with 60,000 tons per annum of GBS produced. Hence, there is a compelling need to explore new chances to use GBS availability irrespective of the seasonality and address the problem where GBS utilization is limited to the early phases of home-cooked dishes. Although drying increases the shelf life of ginkgo seeds, there is a higher operation cost. Thus, pretreatment can reduce energy consumption and augment the product quality is ideal. This research reported the impact of nonthermal pretreatments on ginkgo seeds' mass transfer, drying, and rehydration characteristics. The present results will provide a comprehensive understanding of the engineering application of ginkgo seed pretreatment, allowing for the best technique to be selected.

Least squares estimation of high-order uncertain differential equations

Parameter estimation of high-order uncertain differential equations is an inevitable problem in practice. In this paper, the equivalent equations of high-order uncertain differential equations are obtained by transformation, and the parameters of the first-order uncertain differential equation including Liu process are estimated. Based on the least squares estimation method, this paper proposes a means to minimize the residual sum of squares to obtain an estimate of the parameters in the drift term, and make the noise sum of squares equal to the residual sum of squares to obtain an estimate of the parameters in the diffusion term. In addition, some numerical examples are given to illustrate the proposed method. Finally, applications of the high-order uncertain spring vibration equations verify the viability of our method.

Evaluation of models for predicting the preweaning body weight in Holstein calves

This study compared six non-linear equations [Exponential growth (4 parameters), Exponential growth (Stirling), Polynomial (Cubic), Quadratic, Brody, and Sinusoidal] for prediction of pre-weaning body weights at different ages in Holstein calves. Thirty-two calves (16 males and 16 females) were randomly divided into two treatment groups and fed with starter diets containing either corn or barley as the grain source. Starter feeding began on the third day of life, and high quality alfalfa hay and fresh cow milk were fed according to the farm schedule. The calves were weighed at birth and weekly thereafter until weaning. In this manner, ten weight records, including the birth and weaning weights, constituted the data set. The results of experiment revealed the fact that all functions mentioned earlier showed good fitness to predict weight gain in relation to age in all groups of calves. However, based on the goodness of the fit of various criteria and the statistical performance, the polynomial (cubic) function was considerably superior to other functions for predicting the calf live weight. The flexible growth functions (more parameters) very often give a closer fit to data points and a smaller residual sum of square (RSS) value than the simpler functions such as the Brody functions.

An existence level for the residual sum of squares of the power-law regression with an unknown location parameter

Abstract In a recent paper [JUKIĆ, D.: A necessary and sufficient criterion for the existence of the global minima of a continuous lower bounded function on a noncompact set, J. Comput. Appl. Math. 375 (2020)], a new existence level was introduced and then was used to obtain a necessary and sufficient criterion for the existence of the global minima of a continuous lower bounded function on a noncompact set. In this paper, we determined that existence level for the residual sum of squares of the power-law regression with an unknown location parameter, and so we obtained a necessary and sufficient condition which guarantee the existence of the least squares estimate.

Modeling the growth curve of Muzaffarnagari lambs from India

Body weights (BW) of 2,611 Muzaffarnagari lambs at birth and 3, 6, 9, and 12 months of age were used to compare five alternative growth curves. Four three-parameter functions (Brody, von Bertalanffy, Gompertz, and logistic) and the four-parameter Richards function were fit for each lamb. Estimates of the asymptotic final BW (A), a maturing-rate parameter (k), and the degree of maturity at birth (u0) = BW/A were estimated for each function. A shape parameter (m) was included in the Richards function and used to identify the degree of maturity (u) at the point of inflection of the growth curve (uI). Among three-parameter functions, the Brody function had the smallest pooled residual sum of squares (RSS) across all lambs and the smallest RSS for 56% of individual lambs. For the Brody function, the growth rate is maximum at birth and declines continuously as animals age. In comparison with the Brody function, fitting the Richards function with a variable inflection point did not significantly reduce RSS. Comparisons of Brody and Richards functions indicated that the Richards function converged to a Brody function for 17% of the lambs. The predicted growth rate was maximum at an average uI of 0.26 for the Richards function and at an average of u0 = 0.10 for the Brody function. Parameters k and m interacted to define the maturing rate in the Richards function, and the predicted age at u= 0.50 (t50) was proposed to measure growth efficiency. Correlations between estimates of A, u0, and t50 for the Brody and Richards functions were all 0.89, confirming limited benefit from fitting the Richards function. For the Brody function, correlations among estimates of A, k, and u0 were modest (-0.59 to 0.39). Use of these parameter estimates in selection to modify the growth curve would be relatively straightforward. Correlations among A, k, and u0 from the Richards function were also modest (-0.60 to 0.27), but the correlation between k and uI was 0.82. Selection to modify the Richards parameters would therefore be more challenging, and the Richards function likely over-parameterized the growth curve. We therefore concluded that the Brody function was the most appropriate growth function to describe BW changes from birth to 1 year of age in Muzaffarnagari lambs.

Kinetic Modeling of Slightly Acidic Electrolyzed Water Decay Characteristics in Fresh Cabbage Disinfection Against Human Norovirus.

To consistently disinfect fresh vegetables efficiently, the decay of disinfectants such as chlorine, electrolyzed oxidizing water (EOW), ozonated water, and plasma-activated water during the disinfection maintenance stage needs to be understood. The aim of our study was to evaluate the changes in the inactivation kinetics of slightly acidic electrolyzed water (SAEW) against human norovirus (HuNoV), based on the cabbage-to-SAEW ratio. After disinfection of fresh cabbage with disinfected SAEW solution, SAEW samples were collected and analyzed for physicochemical properties such as pH, available chlorine concentrations (ACCs), and oxidation-reduction potential (ORP). SAEW virucidal effects were evaluated. We confirmed the decay of post-disinfection SAEW solution and demonstrated the different patterns of the decay kinetic model for HuNoV GI.6 and GII.4. In addition, the goodness of fit of the tested models based on a lower Akaike information criterion, root-mean-square error (RMSE), and residual sum of squares (RSS) was close to zero. In particular, the change in both the HuNoV GI.6 and GII.4 inactivation exhibited a strong correlation with the changes in the ACC of post-disinfection SAEW. These findings demonstrate that physicochemical parameters of SAEW play a key role in influencing the kinetic behavior of changes in the disinfection efficiency of SAEW during the disinfection process. Therefore, to optimize the efficiency of SAEW, it is necessary to optimize the produce-to-SAEW ratio in future studies.