Residual Mean Research Articles

Right anterior mini thoracotomy approach for isolated aortic valve replacement: Early outcomes at a Canadian center.

The goal of this manuscript was to report the clinical outcomes of the initial series of 100 consecutive Right Anterior Mini Thoracotomy (RAMT) aortic valve replacement (AVR) implantations at a Canadian Center. This retrospective study reported the clinical outcomes of the first 100 patients who underwent the RAMT approach for isolated surgical AVR in Calgary, Canada, between 2016 and 2020. Primary outcomes were death within 30 days of surgery and disabling stroke. Secondary outcomes included surgical times, the need for permanent pacemaker (PPM), incidence of postoperative blood transfusion in the intensive care unit (ICU), postsurgical atrial fibrillation (AF), length of ICU/hospital stay, postsurgical AF, residual paravalvular leak (PVL), postoperative transvalvular gradient, need for postsurgical intravenous opioids, duration of invasive ventilation in the ICU, and chest tube output in the first 12 h postsurgery. In this study, 54 patients were male, and the average age of the cohort was 72 years. Mortality within 30 days of surgery was 1% with no disabling postoperative strokes. Mean cardiopulmonary bypass and cross clamp was 84 and 55 min, respectively. PPM rate was 3%, incidence of blood transfusion in the ICU was 4%, and the rate of postoperative AF was 23%. Median length of ICU and hospital stay was 1 and 5 days, respectively. Rate of mild or greater residual PVL was 3%, while the average residual transvalvular mean gradient was 8.5 mmHg. The sternum-sparing RAMT approach can be safely integrated into surgical practice as a minimally invasive alternative for isolated AVR, and can reduce postoperative bleeding and narcotic requirements.

Correction to: Effects of residual stresses and compressive mean stresses on the fatigue strength of longitudinal fillet-welded gussets

Correction to: Effects of residual stresses and compressive mean stresses on the fatigue strength of longitudinal fillet-welded gussets

A correct method to calculate the IDD value-added indicator in the Brazillian higher education quality assurance framework

This paper investigates the value-added indicator used in the Brazilian higher education quality assurance framework, the so-called IDD indicator for undergraduate programmes (“Indicator of the difference between observed and expected outcomes''). The two main claims are that since 2014 this indicator is calculated incorrectly and that this mistake has relevance for public policy. INEP, the educational statistical agency responsible for educational quality indicators in Brazil, incorrectly uses multilevel modeling in their value-added analysis. The IDD indicator is calculated by estimating a varying intercept linear mixed model, but instead of identifying the intercepts with the value added of courses, INEP uses the mean of the student residuals. That this was indeed the error made is shown by reproducing exactly INEP’s published values using the incorrect method with the microdata for the 2019 assessment cycle. I then compare these values to the ones obtained with the same model and same data, but using the correct value-added measure. A comparison of reliability estimates for both methods shows that this measure of internal consistency is indeed higher for the correct method. As an example of policy relevance, I calculate the number of courses that would change from “satisfactory” to “unsatisfactory” and vice-versa, using the usual criteria established by INEP, if the correct method is applied.

Analysis of Arctic Spring Ozone Anomaly in the Phases of QBO and 11-Year Solar Cycle for 1979–2017

Arctic ozone amount in winter to spring shows large year-to-year variation. This study investigates Arctic spring ozone in relation to the phase of quasi-biennial oscillation (QBO)/the 11-year solar cycle, using satellite observations, reanalysis data, and outputs of a chemistry climate model (CCM) during the period of 1979–2017. For this duration, we found that the composite mean of the Northern Hemisphere high-latitude total ozone in the QBO-westerly (QBO-W)/solar minimum (Smin) phase is slightly smaller than those averaged for the QBO-W/Smax and QBO-E/Smax years in March. An analysis of a passive ozone tracer in the CCM simulation indicates that this negative anomaly is primarily caused by transport. The negative anomaly is consistent with a weakening of the residual mean downward motion in the polar lower stratosphere. The contribution of chemical processes estimated using the column amount difference between ozone and the passive ozone tracer is between 10–20% of the total anomaly in March. The lower ozone levels in the Arctic spring during the QBO-W/Smin years are associated with a stronger Arctic polar vortex from late winter to early spring, which is linked to the reduced occurrence of sudden stratospheric warming in the winter during the QBO-W/Smin years.

Signal anomaly identification strategy based on Bayesian inference for nuclear power machinery

In the machinery industry, signal anomalies are generally identified using the threshold method, which exhibits shortcomings in setting reasonable thresholds, in decision-making when signals approach thresholds or fluctuate, and in quantification of fault confidence. In this paper, a long short-term memory (LSTM) model is established to predict the time-series signals. For prediction residual, a novel decision-making strategy of signal anomaly identification based on Bayesian inference is then proposed that considers data uncertainty. Various signal abnormality conditions are analyzed, and a Bayesian hypothesis test approach is developed to determine the signal status and quantify the fault probability. After fully mining the prior information of the residuals to reduce the influence of randomness, estimates of the key parameters, namely residual mean and variance, are determined by obtaining the posterior distribution based on the normal-inverse-gamma distribution. In two nuclear power machinery examples, all potential signal anomalies are identified by the proposed method. The results of a comparative analysis with existing methods demonstrate that the proposed method can issue an alarm several hours in advance and provide a fault probability, which improves the accuracy and reliability of prediction.

Valorisation of watermelon (Citrullus lanatus) rind waste into bioethanol: an optimization and kinetic studies

Bioethanol from renewable resources has gained great attention as one of the alternatives to partially replace transportation fuel from fossil fuel. Watermelon rind waste (WRW) is among of the renewable resources generated from agriculture industry that content high level of cellulose fraction which can be converted into bioethanol. To operate the fermentation process effective and efficiently, the fermentation is needed to be carried out under optimum condition. Furthermore, understanding the bioethanol production and growth kinetic model are necessary to predict the production of bioethanol under the optimized condition. Thus, this work was design to investigate the influence of fermentation operating parameters such as initial biomass concentration, rate of agitation and temperature towards bioethanol production. Further, the kinetic model for yeast growth during WRW hydrolysate fermentation was determined using logistic and Gompertz models. The maximum bioethanol concentration of 29.59 ± 2.52 gL−1 corresponding to bioethanol yield of 0.48 gbioethanol/gsugar was obtained when fermentation was performed using 150 gL−1 biomass at 150 rpm and 35 °C. Unstructured logistic model described well the growth of Saccharomyces cerevisiae during the ethanol production from WRW hydrolysate with maximum biomass concentration (Xmax), and growth rate (umax) is 2.92 gL−1 and 0.27 h−1. High significant R2 and low residual mean standard deviation (RMSD) value of 0.92 and 2.74 were obtained from this analysis. Furthermore, the product kinetic analysis using Luedeking–Piret analysis study adequately described bioethanol production from WRW hydrolysate as mixed-growth associate product formation manner (R2 = 0.92). The result obtained from this study provides a useful information on further bioprocessing system for bioethanol production from watermelon waste.

Modelling the residual mean meridional circulation at different stages of sudden stratospheric warming events

Abstract. Ensemble simulation of the atmospheric general circulation at altitudes up to the lower thermosphere is performed using the 3-D nonlinear mechanistic numerical model MUAM. The residual mean meridional circulation (RMC), which is the superposition of the mean Eulerian and wave-induced eddy components, is calculated for the boreal winter. Changes in the vertical and meridional RMC velocity components are analysed at different stages of a simulated composite sudden stratospheric warming (SSW) event averaged over 19 model runs. The simulation results show a general decrease in RMC velocity components up to 30 % during and after SSW in the mesosphere and lower thermosphere of the Northern Hemisphere. There are also increases in the downward and northward velocities at altitudes of 20–50 km at the northern polar latitudes during SSW. Associated vertical transport and adiabatic heating can contribute to warming the stratosphere and downward shifting of the stratopause during the composite SSW. The residual mean and eddy mass fluxes are calculated for different SSW stages. It is shown that before the SSW, planetary wave activity creates wave-induced eddy circulation cells in the northern upper stratosphere, which are directed upwards at middle latitudes, northward at high latitudes and downwards near the North Pole. These cells increase heat transport and adiabatic heating in the polar region. During SSW, the region of upward eddy vertical velocity is shifted to high latitudes, but the velocity is still downward near the North Pole. After SSW, upward eddy-induced fluxes span the entire polar region, producing upward transport and adiabatic cooling of the stratosphere and providing the return of the stratopause to higher altitudes. The obtained statistically significant results on the evolution of RMC and eddy circulation at different SSW stages at altitudes up to the lower thermosphere can be useful for a better understanding the mechanisms of planetary wave impacts on the mean flow and for the diagnostics of the transport of conservative tracers in the atmosphere.

Objective and subjective evaluation of trifocal diffractive intraocular Lens after cataract extraction with phacoemulsification: a prospective clinical study

BackgroundThis study aimed to assess visual outcomes, quality of vision and patients’ satisfaction of a trifocal diffractive intraocular lens after cataract surgery with phacoemulsification.ResultsThe study included 36 eyes that underwent implantation of trifocal diffractive intraocular lens (IOL). The residual mean postoperative spherical equivalent was − 0.40 ± 0.29 diopters. Mean Uncorrected distance visual acuity was 0.80 ± 0.16 decimal (snellen equivalent 25 ft) while mean Uncorrected intermediate visual acuity was 0.82 ± 0.31 decimal (snellen equivalent 25 ft) and mean Uncorrected near visual acuity (UCNVA) was 0.87 ± 0.20 decimal (snellen equivalent 23 ft). In defocus curve, there was infinitesimal gradual change between the three foci. Contrast sensitivity was just below the inferior limit of normal.ConclusionTrifocal diffractive IOL created a true intermediate focus proved by VA and defocus curve and better quality of vision assessed by contrast sensitivity and high order aberration. Moreover, it was safe and effective for correcting distance and near vision in these patients. Most of the patients were very satisfied and achieved spectacle independence.Trial registrationRegistration number and date: NCT04465279 on July 10, 2020.

A psychometric evaluation of the Functional assessment of cancer therapy\u2014kidney symptom index (FKSI-19) among renal cell carcinoma patients suggesting an alternative two-factor structure

PurposeTo psychometrically evaluate the hypothesized four-factor structure of the 19-item Functional Assessment of Cancer Therapy—Kidney Symptom Index (FKSI-19) health-related quality of life (HRQoL) instrument in a sample of surgically treated renal cell carcinoma (RCC) patients and examine if an alternative factor structure with good psychometric properties may be derived from the available items.MethodsThe model fit of the hypothesized four-factor structure was examined using confirmatory factor analysis on cohort data from 1731 individuals included in the National Swedish Kidney Cancer Register who had undergone surgery for RCC during the three years 2016–2018 and answered the FKSI-19 instrument within 6–12 months after surgery. Exploratory factor analysis was applied to the same dataset to derive a possible alternative factor solution.ResultsThe four-factor structure did not reach the thresholds for good model fit using the normed χ2-value or the Comparative Fit Index, although the Standardized Root Mean Square Residual and Root Mean Square Error of Approximation measures indicated good and acceptable model fits, respectively. An alternative 14-item trimmed FKSI version (FKSI-14) with a two-factor structure derived from the available FKSI-19 items was found to measure the same aspects of HRQoL as the full FKSI-19 instrument.ConclusionThe present study is the first to use psychometric methods for examining the factor structure of the FKSI-19 instrument. The hypothesized four-factor structure of FKSI-19 provided a barely acceptable model fit. The two-factor FKSI-14 structure may be used as an alternative or complement to the four-factor structure when interpreting the FKSI-19 instrument.

Effect of local equivalent stress on fatigue life prediction of carburized Cr-Ni alloy steel based on evaluation of maximum crack sizes

The fatigue assessment methods considering surface strengthening process were proposed through experimental investigation of fatigue properties of carburized Cr-Ni alloy under stress ratios of 0 and 0.3. Interior-failure is caused by continuous debondings of refined grains due to stress concentration around interior-inclusion, while surface-failure is caused by grain dislocation slip around surface-inclusion. In view of the effect of local equivalent stress (residual stress, mean stress and stress concentration around the inclusion), the PRx-S-N curves of carburized Cr-Ni alloy steel were constructed based on the prediction of crack characteristic sizes by Generalized Extreme Values (GEV) and Generalized Pareto (GP) distributions under different cumulative probabilities, and the comparison shows that GP distribution can predict the fatigue strength better than GEV distribution. The fatigue life prediction models can be established by GP distribution for surface and interior failures, and the predicted results are in good agreement with the experimental results.

Geometrical defect identification of a SCARA robot from a vector modeling of kinematic joints invariants

This article introduces a new geometric vector modeling method of serial kinematic robot consistent with the identification process. This method is based on the definition of position and orientation of the robot joint invariants. For example, the invariant of the rotational joint is a straight-line (rotational joint axis). Thus, only independent geometrical parameters are introduced to model the joint axis position and orientation in space. Note that, the orientation is not constrained as in the Denavit–Hartenberg (DH) formalism. This article presents the methodology to define these geometrical parameters and the geometrical model. In this context, the identification method relies on "Circle Point Analysis". The points are measured with a laser tracker. Indeed, with a relevant processing of the measured points, we directly identify the invariants of joints. This method is applied to a SCARA robot geometric modeling. After an identification process, this methodology allows improving inverse kinematic error compared to the classical DH geometrical model with first and second-order defects. Moreover, the obtained residual error mean value is close to the accuracy of the measurement process.

Proposals of non-linear models to adjust in vitro gas production at different incubation times in cassava genotypes

The present study aimed to propose new two-compartment models from the combination of the Gompertz, Logistic and Von Bertalanffy models and to identify between Gompertz and Logistic models, in their uni and two-compartiment versions, the one that presents the highest quality of fit to cumulative gas production curves of five cassava genotypes: Brasília, Engana Ladrão, Dourada, Gema de Ovo e Amansa Burro. The gas production readings were 2, 4, 6, 8, 10, 12, 14, 17, 20, 24, 28, 32, 48, 72, and 96 hours after the start of the in vitro fermentation process. The estimation of the parameters for the models was made by the least squares method through the Gauss-Newton iterative process. The selection of the best model to describe the gas accumulation was based on the adjusted coefficient of determination, residual mean squares, mean absolute deviation, Akaike information criterion and Bayesian information criterion. Among the adjusted models, the proposed models were the best to describe the accumulation of gases over time according to the methodology and conditions under which this study was developed.

Assessing roles of geochemical reactions on CO2 plume, injectivity and residual trapping

With increasing CO2 concentration in the atmosphere, CO2 geo-aequestration has become a popular technique to counter the dangers of global warming resulting from high levels of CO2 in the atmosphere. This paper examins sequestration parameters such as CO2 plume behaviour, residual gas trapping and injectivity as a means of achieving safe and successful CO2 storage in saline aquifers. Mineral precipitation/dissolution rates are used to establish a relationship between these parameters and geochemical reactions in saline aquifers. To achieve this, mechanistic models (6 models with different inputs, created using CMG – GEM, 2016 and WINPROP, 2016) are simulated using input data from literature and studying changes in fluids and formation properties as well as mineral precipitation/dissolution rates in aquifers when subjected to different conditions in the different models.The results from the models show that high CO2 dissolution, which creates large CO2 plume, leads to high mineral dissolution/precipitation as results of increased fluid-rock interactions (geochemical reactions); whereas injectivity, although enhanced by CO2-water cyclic injection, does not show much increase in bottom hole pressure when mineral trapping (thus geochemical reactions) is introduced into the model. Sensitivity study on residual gas trapping shows that high residual gas saturation leads to reduced mineral precipitation/dissolution due to the reduced amount of dissolved CO2 in brine. Also, rapid changes in the bottom hole pressure at high residual gas saturation means that a formation that fosters high residual gas trapping, rather than CO2 dissolution in brine, is more likely to experience injectivity issues during the sequestration process.

A Positive Zonal Wind Feedback on Sudden Stratospheric Warming Development Revealed by CESM2 (WACCM6) Reforecasts

AbstractSudden stratospheric warmings (SSWs) are an extreme weather event with impacts on the ionosphere and on tropospheric weather and predictability. The mechanisms governing their formation remain elusive, despite their deterministic predictability at nearly 2 weeks. This study uses high resolution CESM2 (WACCM6) subseasonal reforecasts to examine the dynamics that differentiate successful and unsuccessful SSW predictions. Successful reforecasts are generally initialized with a weaker stratospheric jet. However, the basic relationships between jet deceleration, wave drag, and the residual mean angular momentum flux do not fundamentally differ between successful and unsuccessful reforecasts. Instead, the projection of the residual circulation onto a weakened jet produces a weaker angular momentum flux, which leads to a more rapid erosion of the jet as the residual circulation cannot effectively balance the sustained wave drag. This information could be used to develop forecasting practices that could probe the likelihood of SSWs at longer timescales.

Poisson Transmuted-G Family of Distributions: Its Properties and Applications

In this article, an extension of the transmuted-G family is proposed, in the so-called Poison transmuted-G family of distributions. Some of its statistical properties including quantile function, moment generating function, order statistics, probability weighted moment, stress-strength reliability, residual lifetime, reversed residual lifetime, Rényi entropy and mean deviation are derived. A few important special models of the proposed family are listed. Stochastic characterizations of the proposed family based on truncated moments, hazard function and reverse hazard function, are also studied. The family parameters are estimated via the maximum likelihood approach. A simulation study is carried out to examine the bias and mean square error of the maximum likelihood estimators. The advantage of the proposed family in data fitting is illustrated by means of two applications to failure time data sets.

A Note on the Tracing of Herbage Contribution to Grazing Sheep Diet Using Milk and Feces Biomarkers.

Milk from grazing ruminants is usually rich in beneficial components for human health, but distinguishing milks sourced from grazing is difficult, and this hinders the valuing of the grazing benefit. This study aimed at evaluating the ability of milk biomarkers (1) to trace milks sourced from sheep submitted to different access times (ATs) to pasture and (2) to estimate sheep herbage dry matter intake (HDMI, g DM ewe−1 d−1) and herbage percentage (HP, % DM) in sheep diet. Animal data derive from a published experiment in which six replicated groups of mid-lactation Sarda sheep had ATs of 2, 4, or 6 h d−1 to a ryegrass pasture. Sheep HDMI and HP of each group were measured on four dates in April 2013. Group milk was sampled, and milk fatty acids (FAs) and n-alkanes were determined by gas chromatography. The latter markers were also measured in feces samples bulked by group. The data (N = 24 records) were submitted to Linear Discriminant Analysis (LDA) aimed at distinguishing the AT to pasture based on biomarkers previously selected by Genetic Algorithms (GA). Partial Least Square Regression (PLSR) models were used to estimate HDMI and HP using biomarkers selected by GA. Based on one milk alkane and six milk FAs as biomarkers, estimates of the AT using GA-LDA were 95.8% accurate. The estimation of HDMI by GA-PLSR based on five milk FAs was moderately precise [explained variance = 75.2%; percentage of the residual mean square error of cross-validation over the mean value (RMSECV%) = 15.0%]. The estimation of HP by GA-PLSR based on 1 milk alkane and 10 FAs was precise (explained variance = 80.8%; RMSECV% = 7.4%). To conclude, these preliminary results suggest that milks sourced from sheep flocks with AT to pasture differentiated by 2 h in the range 2–6 h d−1 can be precisely discriminated using milk biomarkers. The contribution of herbage to sheep diet can also be precisely estimated.

Thyroid gland delineation in noncontrast-enhanced CTs using deep convolutional neural networks

The purpose of this study is to develop a deep learning method for thyroid delineation with high accuracy, efficiency, and robustness in noncontrast-enhanced head and neck CTs. The cross-sectional analysis consisted of six tests, including randomized cross-validation and hold-out experiments, tests of prediction accuracy between cancer and benign and cross-gender analysis were performed to evaluate the proposed deep-learning-based performance method. CT images of 1977 patients with suspected thyroid carcinoma were retrospectively investigated. The automatically segmented thyroid gland volume was compared against physician-approved clinical contours using metrics, the Pearson correlation and Bland–Altman analysis. Quantitative metrics included: the Dice similarity coefficient (DSC), sensitivity, specificity, Jaccard index (JAC), Hausdorff distance (HD), mean surface distance (MSD), residual mean square distance (RMSD) and the center of mass distance (CMD). The robustness of the proposed method was further tested using the nonparametric Kruskal–Wallis test to assess the equality of distribution of DSC values. The proposed method’s accuracy remained high through all the tests, with the median DSC, JAC, sensitivity and specificity higher than 0.913, 0.839, 0.856 and 0.979, respectively. The proposed method also resulted in median MSD, RMSD, HD and CMD, of less than 0.31 mm, 0.48 mm, 2.06 mm and 0.50 mm, respectively. The MSD and RMSD were 0.40 ± 0.29 mm and 0.70 ± 0.46 mm, respectively. Concurrent testing of the proposed method with 3D U-Net and V-Net showed that the proposed method had significantly improved performance. The proposed deep-learning method achieved accurate and robust performance through six cross-sectional analysis tests.

Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy

Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT is expected to enable accurate multi-organ segmentation in HN cancer patients. In our proposed method, MR images are firstly synthesized using a pre-trained cycle-consistent generative adversarial network given CBCT. The features of CBCT and synthetic MRI (sMRI) are then extracted using dual pyramid networks for final delineation of organs. CBCT images and their corresponding manual contours were used as pairs to train and test the proposed model. Quantitative metrics including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance, and residual mean square distance (RMS) were used to evaluate the proposed method. The proposed method was evaluated on a cohort of 65 HN cancer patients. CBCT images were collected from those patients who received proton therapy. Overall, DSC values of 0.87 ± 0.03, 0.79 ± 0.10/0.79 ± 0.11, 0.89 ± 0.08/0.89 ± 0.07, 0.90 ± 0.08, 0.75 ± 0.06/0.77 ± 0.06, 0.86 ± 0.13, 0.66 ± 0.14, 0.78 ± 0.05/0.77 ± 0.04, 0.96 ± 0.04, 0.89 ± 0.04/0.89 ± 0.04, 0.83 ± 0.02, and 0.84 ± 0.07 for commonly used OARs for treatment planning including brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord, respectively, were achieved. This study provides a rapid and accurate OAR auto-delineation approach, which can be used for adaptive radiation therapy.

Assessment of cadmium pollution and subsequent ecological and health risks in Jiaozhou Bay of the Yellow Sea

Million tons of cadmium (Cd) are annually discharged into China's coastal regions, creating a persistent hazard to marine organisms and human health. This study assessed Cd residues in the Yellow Sea's semi-enclosed Jiaozhou Bay (JZB), finding concentrations of 0.05–0.94 μg/L in seawater and 0.03–0.18 mg/kg in sediment. For marine organisms, mollusks had the highest Cd concentration (0.44 ± 0.09 mg/kg), followed by crustaceans (0.26 ± 0.08 mg/kg) and fish (0.10 ± 0.02 mg/kg). Cd was clearly accumulated by mollusks, with biota-sediment accumulation factor (BSAF) values >1 and biota-water accumulation factor (BWAF) values >1000. Stable nitrogen isotope (δ15N) analysis showed that Cd underwent biomagnification in mollusks, but was significantly bio-diluted with increasing trophic level among other marine organisms. In general, Cd contamination levels were low in the JZB's seawater and sediment, and fish was estimated to be certainly polluted due to strict safety limitations on seafood in China. Current Cd residues mean that few aquatic species (<< 5%) would be affected by acute exposure, and ~ 10% of the species would be affected by chronic exposure. Based on target hazard quotients (THQ) and estimated weekly intakes (EWIs), urban residents around the JZB would experience higher health risks in comparison with rural residents due to higher seafood consumption rates, especially from mollusk consumption. Therefore, urban households in the area should increase their fish consumption rate and reduce that of mollusks.

Improved fixed bed models for correlating asymmetric adsorption breakthrough curves

Breakthrough curves of water contaminants are usually analyzed using simple fixed bed models such as the Bohart-Adams, Thomas, and Yoon-Nelson equations, which are by design symmetric. Because breakthrough data often follow an asymmetric pattern, the use of models that do not account for asymmetry could lead to poor fits, consequently resulting in erroneous estimates of breakthrough and exhaustion times. To address this issue, the Bohart-Adams, Thomas, and Yoon-Nelson models were modified by a logarithmic transformation to enhance their data fitting ability. The three modified models were found capable of providing robust fits to seven separate sets of previously reported asymmetric breakthrough data of water contaminants (fluoride, methylene blue, salicylic acid, lead, mercury, nickel, and arsenic), with reported residual root mean square error (RRMSE) values ranging from 0.019 to 0.046. In consequence, the new models were found capable of providing reliable estimates of breakthrough and exhaustion times corresponding to any predetermined concentration level. By contrast, the three original models were found to perform poorly, reporting inferior RRMSE values ranging from 0.038 to 0.086 for data fits and providing grossly inaccurate estimates of breakthrough and exhaustion times. The new models contain only parameters that appear in the original models, and are highly flexible, being able to assume virtually all monotonically increasing sigmoid shapes. They represent a far more accurate alternative to the original models.