The Homburg-Adelaide toric IOL nomogram: How to predict corneal power vectors from preoperative IOLMaster 700 keratometry and total corneal power in toric IOL implantation.

PURPOSE: The purpose of this study is to compare the reconstructed corneal power (RCP) by working backwards from the post-implantation spectacle refraction and toric intraocular lens power and to develop the models for mapping preoperative keratometry and total corneal power to RCP. METHODS: Retrospective single-centre study involving 442 eyes treated with a monofocal and trifocal toric IOL (Zeiss TORBI and LISA). Keratometry and total corneal power were measured preoperatively and postoperatively using IOLMaster 700. Feedforward neural network and multilinear regression models were derived to map keratometry and total corneal power vector components (equivalent power EQ and astigmatism components C0 and C45) to the respective RCP components. RESULTS: Mean preoperative/postoperative C0 for keratometry and total corneal power was -0.14/-0.08 dioptres and -0.30/-0.24 dioptres. All mean C45 components ranged between -0.11 and -0.20 dioptres. With crossvalidation, the neural network and regression models showed comparable results on the test data with a mean squared prediction error of 0.20/0.18 and 0.22/0.22 dioptres2 and on the training data the neural network models outperformed the regression models with 0.11/0.12 and 0.22/0.22 dioptres2 for predicting RCP from preoperative keratometry/total corneal power. CONCLUSIONS: Based on our dataset, both the feedforward neural network and multilinear regression models showed good precision in predicting the power vector components of RCP from preoperative keratometry or total corneal power. With a similar performance in crossvalidation and a simple implementation in consumer software, we recommend implementation of regression models in clinical practice.

Protein Adsorption on Solid Surfaces: Data Mining, Database, Molecular Surface-Derived Properties, and Semiempirical Relationships.

Protein adsorption on solid surfaces is a process relevant to biological, medical, industrial, and environmental applications. Despite this wide interest and advancement in measurement techniques, the complexity of protein adsorption has frustrated its accurate prediction. To address this challenge, here, data regarding protein adsorption reported in the last four decades was collected, checked for completeness and correctness, organized, and archived in an upgraded, freely accessible Biomolecular Adsorption Database, which is equivalent to a large-scale, ad hoc, crowd-sourced multifactorial experiment. The shape and physicochemical properties of the proteins present in the database were quantified on their molecular surfaces using an in-house program (ProMS) operating as an add-on to the PyMol software. Machine learning-based analysis indicated that protein adsorption on hydrophobic and hydrophilic surfaces is modulated by different sets of operational, structural, and molecular surface-based physicochemical parameters. Separately, the adsorption data regarding four "benchmark" proteins, i.e., lysozyme, albumin, IgG, and fibrinogen, was processed by piecewise linear regression with the protein monolayer acting as breakpoint, using the linearization of the Langmuir isotherm formalism, resulting in semiempirical relationships predicting protein adsorption. These relationships, derived separately for hydrophilic and hydrophobic surfaces, described well the protein concentration on the surface as a function of the protein concentration in solution, adsorbing surface contact angle, ionic strength, pH, and temperature of the carrying fluid, and the difference between pH and the isoelectric point of the protein. When applying the semiempirical relationships derived for benchmark proteins to two other "test" proteins with known PDB structure, i.e., ß-lactoglobulin and α-lactalbumin, the errors of this extrapolation were found to be in a linear relationship with the dissimilarity between the benchmark and the test proteins. The work presented here can be used for the estimation of operational parameters modulating protein adsorption for various applications such as diagnostic devices, pharmaceuticals, biomaterials, or the food industry.

Prediction of IOL decentration, tilt and axial position using anterior segment OCT data.

BACKGROUND: Intraocular lenses (IOLs) require proper positioning in the eye to provide good imaging performance. This is especially important for premium IOLs. The purpose of this study was to develop prediction models for estimating IOL decentration, tilt and the axial IOL equator position (IOLEQ) based on preoperative biometric and tomographic measures. METHODS: Based on a dataset (N = 250) containing preoperative IOLMaster 700 and pre-/postoperative Casia2 measurements from a cataractous population, we implemented shallow feedforward neural networks and multilinear regression models to predict the IOL decentration, tilt and IOLEQ from the preoperative biometric and tomography measures. After identifying the relevant predictors using a stepwise linear regression approach and training of the models (150 training and 50 validation data points), the performance was evaluated using an N = 50 subset of test data. RESULTS: In general, all models performed well. Prediction of IOL decentration shows the lowest performance, whereas prediction of IOL tilt and especially IOLEQ showed superior performance. According to the 95% confidence intervals, decentration/tilt/IOLEQ could be predicted within 0.3 mm/1.5°/0.3 mm. The neural network performed slightly better compared to the regression, but without significance for decentration and tilt. CONCLUSION: Neural network or linear regression-based prediction models for IOL decentration, tilt and axial lens position could be used for modern IOL power calculation schemes dealing with 'real' IOL positions and for indications for premium lenses, for which misplacement is known to induce photic effects and image distortion.

An improved successive projections algorithm version to variable selection in multiple linear regression.

The aim of the successive projections algorithm (SPA) is to enhance the accuracy of multiple linear regressions (MLR) by minimizing the impact of collinearity effects in the calibration data set. Combining SPA with MLR as a variable selection approach has resulted in the SPA-MLR method, which has been reported in literature to produce models with good prediction ability compared to conventional full-spectrum models obtained with partial-least-squares (PLS) in some cases. This paper proposes the addition of a filter step to the current version of the SPA algorithm to reduce the number of uninformative variables before the projection phase and assist the algorithm in selecting the best variables on subsequent steps. The proposed fSPA-MLR algorithm is evaluated in two case studies involving the near-infrared spectrometric analysis of pharmaceutical tablet and diesel/biodiesel mixture samples. Compared to PLS, the fSPA-MLR models demonstrate similar or better performance. Moreover, the fSPA-MLR models outperform the original SPA-MLR in both cross-validation and external prediction. The fSPA-MLR models deliver superior results regardless of the pre-processing algorithm tested, including first-derivative Savitzky-Golay (SG) and Standard Normal Variate (SNV), or even in raw spectra data.

Extreme learning machine for soil temperature prediction using only air temperature as input.

Soil temperature (TS) is a crucial parameter in many fields, especially agriculture. In developing countries like Algeria, the soil temperatures (TS) and the meteorological data are limited. This study investigates the use of Extreme Learning Machine (ELM) for the accurate prediction of daily ST at three different depths (30 cm, 60 cm, and 100 cm) using a minimal number of climatic inputs. The inputs used in this study include maximum and minimum air temperatures, relative humidity, and day of the year (DOY) as a representative of the temporal component. Five different combinations of inputs were used to develop ELM models and determine the best set of input variables. The ELM models were then compared with traditional methods such as multiple linear regression, artificial neural networks, and adaptive neuro-fuzzy inference system. Based on evaluation metrics such as R, RMSE, and MAPE, the ELM models with air temperatures and DOY as inputs (ELM-M0 and ELM-M3) demonstrated superior performance at all depths when compared to the other techniques. The most accurate predictions were found at a depth of 100 cm using the ELM-M3 model, which employed inputs of minimum and maximum air temperatures and DOY, with R value of 0.98, RMSE of 0.68 °C, and MAPE of 3.4%. The results demonstrate that the inclusion of DOY in the climatic dataset significantly enhances the performance and accuracy of machine learning models for ST prediction. The ELM was found to be a fast, simple, effective, and useful tool for TS prediction.

Prediction of Cell Migration in MDA-MB 231 and MCF-7 Human Breast Cancer Cells Treated with Albizia Lebbeck Methanolic Extract Using Multilinear Regression and Artificial Intelligence-Based Models.

Breast cancer is a common cancer affecting women worldwide, and it progresses from breast tissue to other parts of the body through a process called metastasis. Albizia lebbeck is a valuable plant with medicinal properties due to some active biological macromolecules, and it's cultivated in subtropical and tropical regions of the world. This study reports the phytochemical compositions, the cytotoxic, anti-proliferative and anti-migratory potential of A. lebbeck methanolic (ALM) extract on strongly and weakly metastatic MDA-MB 231 and MCF-7 human breast cancer cells, respectively. Furthermore, we employed and compared an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), and multilinear regression analysis (MLR) to predict cell migration on the treated cancer cells with various concentrations of the extract using our experimental data. Lower concentrations of the ALM extract (10, 5 & 2.5 µg/mL) showed no significant effect. Higher concentrations (25, 50, 100 & 200 µg/mL) revealed a significant effect on the cytotoxicity and proliferation of the cells when compared with the untreated group (p < 0.05; n ≥ 3). Furthermore, the extract revealed a significant decrease in the motility index of the cells with increased extract concentrations (p < 0.05; n ≥ 3). The comparative study of the models observed that both the classical linear MLR and AI-based models could predict metastasis in MDA-MB 231 and MCF-7 cells. Overall, various ALM extract concentrations showed promising an-metastatic potential in both cells, with increased concentration and incubation period. The outcomes of MLR and AI-based models on our data revealed the best performance. They will provide future development in assessing the anti-migratory efficacies of medicinal plants in breast cancer metastasis.

Analysis of Unconfined Compressive Strength of Rammed Earth Mixes Based on Artificial Neural Network and Statistical Analysis.

Earth materials have been used in construction as safe, healthy and environmentally sustainable. It is often challenging to develop an optimum soil mix because of the significant variations in soil properties from one soil to another. The current study analyzed the soil properties, including the grain size distribution, Atterberg limits, compaction characteristics, etc., using multilinear regression (MLR) and artificial neural networks (ANN). Data collected from previous studies (i.e., 488 cases) for stabilized (with either cement or lime) and unstabilized soils were considered and analyzed. Missing data were estimated by correlations reported in previous studies. Then, different ANNs were designed (trained and validated) using Levenberg-Marquardt (L-M) algorithms. Using the MLR, several models were developed to estimate the compressive strength of both unstabilized and stabilized soils with a Pearson Coefficient of Correlation (R2) equal to 0.2227 and 0.766, respectively. On the other hand, developed ANNs gave a higher value for R2 than MLR (with the highest value achieved at 0.9883). Thereafter, an experimental program was carried out to validate the results achieved in this study. Finally, a sensitivity analysis was carried out using the resulting networks to assess the effect of different soil properties on the unconfined compressive strength (UCS). Moreover, suitable recommendations for earth materials mixes were presented.

Can we predict the humerus stem component size required to achieve rotational stability in metaphyseal stability concept?

Background: Implant manufacturers typically offer several sizes of a humeral stem for shoulder arthroplasty so that time zero fixation can be achieved with the optimal size. Stem size can be templated preoperatively but is definitively determined intraoperatively. The purpose of this study was to determine if preoperatively acquired parameters, including patient demographics and imaging, could be used to reliably predict intraoperative humeral stem size. Methods: A cohort of 290 patients that underwent shoulder arthroplasty (116 anatomic and 174 reverse) was analyzed to create a regression formula to predict intraoperative stem size. The initial cohort was separated into train and test groups (randomly selected 80% and 20%, respectively). Patient demographics, anatomical measurements, and statistical shape model parameters determined from a preoperative shoulder arthroplasty planning software program were used for multilinear regression. The implant used for all cases was a short-stemmed metaphyseal-fit prosthesis. Results: Metaphyseal bone density, humeral statistical shape model parameters, and humeral intramedullary canal diameter were identified as highly predictive of intraoperative final humeral prosthesis size. On the train group, a coefficient of determination R2 of 0.63 was obtained for the multilinear regression equation combining these parameters. When analyzing the cohort for the prediction of stem size in the test group, 95% were within plus or minus one size of that used during surgery. Conclusion: Preoperative criteria such as humeral geometry and proximal humeral bone density can be combined in a single multilinear equation to predict intraoperative humeral stem size within one size variation. Embedding the surgeon's decision-making process into an automated algorithm potentially allows this process to be applied across the surgical community. Predicting intraoperative decisions such as humeral stem size also has potential implications for the management of implant stocks for both manufacturers and health-care facilities.

Explanation of major determinants of poverty using multivariate statistical approach and spatial technology: a case study on Birbhum district, West Bengal, India.

Poverty is not only the focal issue that has drawn worldwide attention but is also an essential issue in people's livelihoods. This research examines the primary factors of poverty in the Birbhum district. Multivariate statistical techniques have been used to identify the primary determinants. Ten parameters have been identified as significant drivers of poverty, six of which are physical, viz. slope, elevation, drainage density, pond frequency, soil texture, and rainfall. The remaining four sociocultural and economic parameters are literacy, major market center, population growth, and road density. A linear relationship has been established between the explanatory and response variables where the R-square or coefficient of determination value is 0.741, and this relationship explains more than 74% of the variables. The P-value of multi-linear regression is 0.000, which validates the model and permits the data for factor analysis to extract the major determinants. Factor analysis indicates that five essential factors have been found based on their eigenvalue viz., agro-climatic factor, infrastructural and educational factors, hydrological factor, demographic factor, and pedological factors. All the p-values of the correlation matrix are < 0.05, meaning all the relationships are valid and significant. This research also demonstrates the spatial analysis of data using GIS technology. The western part of the study area has been affected by the high influence of all factors due to the presence of plateau fringe and associated low productivity. The outcomes of the research are scientifically significant and this study helps the planners, higher authorities, and social workers to eradicate poverty from this region through formulating better policies and management.

Mapping the spatial variability of rainfall from a physiographic-based multilinear regression: model development and application to the Southwestern Iberian Peninsula.

A physiographic-based multilinear regression model supported by GIS was developed to estimate spatial rainfall variability in the Southwest Iberian Peninsula. The area study includes a wide diversity of landscape features and comprises four Portuguese regions and one Spanish province (totalizing 28,860 km2). The region suffers a very strong Mediterranean influence, with a major cleavage between winter and summer seasons. Thus, the analysis was carried out separately for the wet (October to March) and dry (April to September) semesters. From an initial set of 10 explanatory physiographic variables, five were selected to be used in the multilinear regression, as they allowed generating models by map algebra that fitted well with the last 40 years of monthly rainfall data records. These records were obtained from 163 weather stations, filtered from an initial set of 230 (142 stations in Portugal and 88 in Spain). The correlation between the physiographic-based multilinear regression model and a model obtained by interpolation from rainfall historical data showed to be good or very good in approximately 75% of the area under study. Results show that physiographic-based models can be effectively used to estimate rainfall where there is a lack of rain gauges, or to densify spatial resolution of rainfall between rain gauges.

Flow Equations for Free-Flowable Particle Fractions of Sorbitol for Direct Compression: An Exploratory Multiple Regression Analysis of Particle and Orifice Size Influence.

Flowability is among the most important properties of powders, especially when fine particle size fractions need to be processed. In this study, our goal was to find a possibly simple but accurate mathematical model for predicting the mass flow rate for different fractions of the pharmaceutical excipient sorbitol for direct compression. Various regression models derived from the Jones-Pilpel equation for the prediction of the mass flow rate were investigated. Using validation with experimental data for various particle and hopper orifice sizes, we focused on the prediction accuracy of the respective models, i.e., on the relative difference between measured and model-predicted values. Classical indicators of regression quality from statistics were addressed as well, but we consider high prediction accuracy to be particularly important for industrial processing in practice. For individual particle size fractions, the best results (an average prediction accuracy of 3.8%) were obtained using simple regression on orifice size. However, for higher accuracy (3.1%) in a unifying model, valid in the broad particle size range 0.100-0.346 mm, a fully quadratic model, incorporating interaction between particle and orifice size, appears to be most appropriate.

The Impact of Green Practices in Value Chain on Firm Performance in the Context of a Developing Country.

Background: Companies need to go green to remain relevant. Previous studies have confirmed that going green leads to superior performance for companies. However, research of green practices in a value chain requires further attention, especially in identifying the green value chain activities that lead to superior performance. A value chain analysis focuses on identifying competitive advantages of firms through five primary and four support activities. Methods: This study extends from Ong et al. (2019), who developed and validated the instrument for the nine green value chain activities, to also examine their effect on firm performance. The 207 valid responses in this study are collected through a questionnaire survey of the sampling frame consisting of companies in Bursa Malaysia and the Federation of Malaysian Manufacturers Directory. Results: The findings reveal that the companies' green practices in primary value chain activities are higher than in the supporting value chain activities. Technological development is the activity with the lowest green attention among the nine value chain activities. Our multiple regression analysis shows that 25% of the variation in firm performance can be significantly explained by the nine green value chain activities. In terms of the individual green value chain activities, green technology development is the only activity that can positively and significantly explain firm performance. Conclusions: The findings of the study suggest that companies intending to build their green core competence need to engage in green technology development. Companies that go green for the purpose of complying to regulations and fulfilling minimum customers' demands can still embed green practices into their green value chain without compromising their performance.

Oral health-related quality of life following peri-implantitis surgery: A prospective study.

AIM: The aim of this prospective study was to describe long-term patient-reported outcomes following surgical treatment of peri-implantitis. METHODS: Oral health-related quality of life (OHRQoL) of 43 patients diagnosed with peri-implantitis was recorded using the short form of the Oral Health Impact Profile (OHIP-14), where low scores indicate low impact. A Norwegian version of the OHIP-14 form was filled out 1 week before and 6-, 18- and 36 months after the peri-implant surgery. The mean and median OHIP-14 scores were calculated for its seven domains (i.e., Functional limitation, Physical pain, Psychological discomfort, Physical disability, Psychological disability, Social disability, and Handicap) across four different time points. The dataset was analyzed to find correlations between independent variables and the OHIP-scores. RESULTS: The OHIP-14 scores were at a low level from baseline to 36 months post-surgery. The mean scores at specific time points were at baseline 7.2 (SD 7.3), 6 months post-surgery 6.0 (SD 6.9), 18 months post-surgery 6.8 (SD 9.7), and 3 years post-surgery 7.0 (SD 9.4). None of these changes were statistically significant. Specific domains of OHRQoL did not significantly differ across different time points (pre- and post-surgery) in males (except for domain "Handicap") or females (except for domain "Functional limitation"). CONCLUSIONS: The reported OHIP-14 measures were initially low and stayed low up to 3 years after peri-implant surgery. This may indicate that neither the disease nor the treatment deteriorated or improved the OHRQoL.

New Pharmacokinetic and Microbiological Prediction Equations to Be Used as Models for the Search of Antibacterial Drugs.

Currently, the development of resistance of Enterobacteriaceae bacteria is one of the most important health problems worldwide. Consequently, there is a growing urge for finding new compounds with antibacterial activity. Furthermore, it is very important to find antibacterial compounds with a good pharmacokinetic profile too, which will lead to more efficient and safer drugs. In this work, we have mathematically described a series of antibacterial quinolones by means of molecular topology. We have used molecular descriptors and related them to various pharmacological properties by using multilinear regression (MLR) analysis. The regression functions selected by presenting the best combination of a number of quality and validation metrics allowed for the reliable prediction of clearance (CL), and minimum inhibitory concentration 50 against Enterobacter aerogenes (MIC50Ea) and Proteus mirabilis (MIC50Pm). The obtained results clearly reveal that the combination of molecular topology methods and MLR provides an excellent tool for the prediction of pharmacokinetic properties and microbiological activities in both new and existing compounds with different pharmacological activities.

Prediction of CW chord as a measure for the eye's orientation axis after cataract surgery from preoperative IOLMaster 700 measurement data.

BACKGROUND: The angles alpha and kappa are widely discussed for centring refractive procedures, but they cannot be determined with ophthalmic instruments. The purpose of this study is to investigate the Chang-Waring chord (position of the Purkinje reflex PI relative to the corneal centre) derived from an optical biometer before and after cataract surgery and to study the changes resulting from cataract surgery. METHODS: The analysis was based on a large dataset of 1587 complete sets of preoperative and postoperative IOMaster 700 biometry measurements from two clinical centres, each containing: valid data for pupil and corneal centre position, the position of the Purkinje reflex PI originated from a coaxial fixation target, keratometry (K), axial length (AL), anterior chamber depth (ACD), lens thickness (LT), central corneal thickness CCT, and horizontal corneal diameter W2W. The Chang-Waring chord CW was derived from pupil centre and Purkinje reflex PI analysed preoperatively and postoperatively, and a multilinear regression model together with a feedforward neural network algorithm was set up to predict postoperative CW chord from preoperative CW chord, K and biometric distances of the eye. RESULTS: The Y component of CW chord shows a slight shift in the inferior direction in both left and right eyes, before and after cataract surgery. The X component shows some shift in the temporal direction, which is more pronounced preoperatively and slightly reduced postoperatively but with a larger variation. The change in CW chord from preoperative to postoperative shows a slight shift in the superior and nasal directions. Our algorithms for prediction of postoperative CW chord using preoperative CW chord, keratometry and biometry as input data performed with a multilinear regression and a feedforward neural network approach were able to reduce the variance, but could not properly predict the postoperative CW chord X and Y components. CONCLUSION: The CW chord as the position of the Purkinje reflex PI with respect to the pupil centre can be directly measured with any biometer, topographer or tomographer with a coaxial fixation light. The mean Y component does not differ between right and left eyes or preoperatively and postoperatively, but the mean temporal shift of the X component preoperatively is slightly reduced postoperatively, but with a larger scatter of the values.

QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches.

Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved PEC50 value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor.

Estimation of Mycophenolic Acid Exposure in Heart Transplant Recipients by Population Pharmacokinetic and Limited Sampling Strategies.

Purpose: The aim of this study is i) to establish a strategy to estimate the area under the curve of the dosing interval (AUC0-12h) of mycophenolic acid (MPA) in the heart transplant recipients and ii) to find the covariates that significantly affect the pharmacokinetics of MPA exposure. Methods: This single-center, prospective, open-label, observational study was conducted in 91 adult heart transplant recipients orally taking mycophenolate mofetil dispersible tablets. Samples collected intensively and sparsely were analyzed by the enzyme-multiplied immunoassay technique, and all the data were used in PPK modeling. Potential covariates were tested stepwise. The goodness-of-fit plots, the normalized prediction distribution error, and prediction-corrected visual predictive check were used for model evaluation. Optimal sampling times by ED-optimal strategy and multilinear regression (MLR) were analyzed based on the simulated data by the final PPK model. Moreover, using intensive data from 14 patients, the accuracy of AUC0-12h estimation was evaluated by Passing-Bablok regression analysis and Bland-Alman plots for both the PPK model and MLR equation. Results: A two-compartment model with first-order absorption and elimination with a lag time was chosen as the structure model. Co-medication of proton pump inhibitors (PPIs), estimated glomerular filtration rate (eGFR), and albumin (ALB) were found to significantly affect bioavailability (F), clearance of central compartment (CL/F), and the distribution volume of the central compartment (V2/F), respectively. Co-medication of PPIs decreased F by 27.6%. When eGFR decreased by 30 ml/min/1.73 m2, CL/F decreased by 23.7%. However, the impact of ALB on V2/F was limited to MPA exposure. The final model showed an adequate fitness of the data. The optimal sampling design was pre-dose and 1 and 4 h post-dose for pharmacokinetic estimation. The best-fit linear equation was finally established as follows: AUC0-12h = 3.539 × C0 + 0.288 × C0.5 + 1.349 × C1 + 6.773 × C4.5. Conclusion: A PPK model was established with three covariates in heart transplant patients. Co-medication of PPIs and eGFR had a remarkable impact on AUC0-12h of MPA. A linear equation was also concluded with four time points as an alternative way to estimate AUC0-12h for MPA.

Insights into the Pollutant Removal Performance of Stormwater Green Infrastructures: A Case Study of Detention Basins and Retention Ponds.

The quality of water has deteriorated due to urbanization and the occurrence of urban stormwater runoff. To solve this problem, this study investigated the pollutant reduction effects from the geometric and hydrological factors of green infrastructures (GIs) to more accurately design GI models, and evaluated the factors that are required for such a design. Among several GIs, detention basins and retention ponds were evaluated. This study chose the inflow, outflow, total suspended solids (TSS), total phosphorus (TP), watershed area, GI area (bottom area in detention basins and permanent pool surface area in retention ponds), and GI volume (in both detention basins and retention ponds) for analysis and applied both ordinary least squares (OLS) regression and multiple linear regression (MLR). The geometric factors do not vary within each GI, but there may be a bias due to the number of stormwater events. To solve this problem, three methods that involved randomly extracting data with a certain range and excluding outliers were applied to the models. The accuracies of these OLS and MLR models were analyzed through the percentage bias (PBIAS), Nash-Sutcliffe efficiency (NSE), and RMSE-observations standard deviation ratio (RSR). The results of this study suggest that models which consider the influent concentration combined with the hydrological and GI geometric parameters have better correlations than models that consider only a single parameter.

PM1.0-Nitrite Heterogeneous Formation Demonstrated via a Modified Versatile Aerosol Concentration Enrichment System Coupled with Ion Chromatography.

Particulate nitrite is a critical source of hydroxyl radicals; however, it lacks high-resolution methods due to its low abundance and stability to explore its formation mechanism. In this study, a modified versatile aerosol concentration enrichment system (VACES) coupled with ion chromatography (IC) was used to measure particulate NO2- hourly online and achieve a lowered detection limit of 10-3 µg m-3. VACES-IC was used to observe a high- and low-concentration events of PM1.0-NO2- in Shanghai, corresponding to the ambient-level concentrations of 0.34 and 0.05 µg m-3, respectively. The morning peak concentrations of NO2- even exceeded 3σ (standard deviation) in the high-concentration event due to the reduction of NO2 by aerosol SO32- based on kinetics and regression analysis. This implies that controlling SO2 emissions would be an effective strategy to decrease morning NO2- concentrations, correspondingly reducing the kinetic formation of SO42- by 20.8-34.8%. However, after sunrise, NO2- formation was primarily attributed to NO2 hydrolysis at pH 4.97-6.14. In the low-concentration event, NO2 hydrolysis also accounted for an overwhelming proportion (∼90%) of NO2- formation. This work estimates the contribution of different paths to particulate NO2- formation based on newly established high-resolution measurements.

WinMLR program for the determination of sorbic and benzoic acids in food samples.

The program WinMLR has been developed to quantify sorbic and benzoic acids in soft drinks, fruit juices, and soy sauce by making a multilinear regression treatment of experimental data to a linear combination of standard signals. The spectra of sorbic and benzoic acid and samples were obtained from a conventional spectrophotometer, which has been saved in an ASCII file to be applied with the WinMLR program. Before to determine sorbic and benzoic acids in samples, the wavelength validation and calibration parameters were studied. Standard solutions of sorbic and benzoic acids were used for the calibration parameters to measure the individual analyte. If the sample has simultaneously both compounds, it is better to choose the synthetic mixture for the calibration parameters. This technique provides a good recovery in the range of 80.4-104.8% without a complicated and expensive instrument.