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The molecularly imprinted polymers are artificial polymers that, during the synthesis, create specific sites for a definite purpose. These polymers due to their characteristics such as stability, easy of synthesis, reproducibility, reusability, high accuracy, and selectivity have many applications. However, the variety of the functional monomers, templates, solvents, and synthesis conditions like pH, temperature, the rate of stirring, and time, limit the selectivity of imprinting. The Practical optimization of the synthetic conditions has many drawbacks, including chemical compound usage, equipment requirements, and time costs. The use of machine learning (ML) for the prediction of the imprinting factor (IF), which indicates the quality of imprinting is a very interesting idea to overcome these problems. The ML has many advantages, for example a lack of human error, high accuracy, high repeatability, and prediction of a large amount of data in the minimum time. In this research, ML was used to predict the IF using non-linear regression algorithms, including classification and regression tree, support vector regression, and k-nearest neighbors, and ensemble algorithms, like gradient boosting (GB), random forest, and extra trees. The data sets were obtained practically in the laboratory, and inputs, included pH, the type of the template, the type of the monomer, solvent, the distribution coefficient of the MIP (K MIP ), and the distribution coefficient of the non-imprinted polymer (K NIP ). The mutual information feature selection method was used to select the important features affecting the IF. The results showed that the GB algorithm had the best performance in predicting the IF, and using this algorithm, the maximum R 2 value (R 2  = 0.871), and the minimum mean absolute error (MAE = − 0.982), and mean square error were obtained (MSE = − 2.303).

In this research, an electrochemical sensor based on molecularly imprinted polymer (MIP) nanoparticles for selective and sensitive determination of diazinon (DZN) pesticides was developed. The nanoparticles of diazinon imprinted polymer were synthesized by suspension polymerization and then used for modification of carbon paste electrode (CPE) composition in order to prepare the sensor. Cyclic voltammetry (CV) and square wave voltammetry (SWV) methods were applied for electrochemical measurements. The obtained results showed that the carbon paste electrode modified by MIP nanoparticles (nano-MIP-CP) has much higher adsorption ability for diazinon than the CPE based non-imprinted polymer nanoparticles (nano-NIP-CP). Under optimized extraction and analysis conditions, the proposed sensor exhibited excellent sensitivity (95.08 μA L μmol −1) for diazinon with two linear ranges of 2.5 × 10 −9 to 1.0 × 10 −7 mol L −1 ( R 2  = 0.9971) and 1.0 × 10 −7 to 2.0 × 10 −6 mol L −1 ( R 2  = 0.9832) and also a detection limit of 7.9 × 10 −10 mol.L −1 . The sensor was successfully applied for determination of diaznon in well water and apple fruit samples with recovery values in the range of 92.53–100.86 %. Graphical abstract Procedure for preparation of electrochemical sensor based on MIP nanoparticles for determination of diazinon

A novel method for preconcentration is described for chromium speciation at microgram per liter to sub-microgram per liter levels. It is based on selective complex formation of both Cr(VI) and Cr(III) followed by dispersive liquid–liquid microextraction and determination by microsample introduction-flame atomic absorption spectrometry. Effects influencing complex formation and extraction (such as pH, temperature, time, solvent, salinity and the amount of chelating agent) have been optimized. Enrichment factors up to 275 and 262 were obtained for Cr(VI) and total Cr, respectively. The calibration graph is linear from 0.3 to 20 µg L −1 , and detection limits are 0.07 and 0.08 µg L −1 for Cr(VI) and total Cr, respectively. Relative standard deviations (RSDs) were obtained to be 2.0% for Cr(VI) and 2.6% for total Cr ( n  = 7).

The application of the recently introduced dispersive liquid–liquid microextraction (DLLME) for the separation and determination of an inorganic selenite [Se(IV)] derivative by means of a gas chromatography–electron-capture detection system has been studied. The selenium derivative was extracted with the DLLME technique using a mixture of ethanol (disperser solvent) and chlorobenzene (extraction solvent). The influences of the various analytical parameters on the derivatization reaction and microextraction procedure have been evaluated and optimized. Under the optimum conditions, an enrichment factor of 122 was obtained for only 5.00 mL of the water sample. The calibration graph was linear in the range of 0.015–10 μg L −1 with a detection limit of 0.005 μg L −1 . The relative standard deviation for ten replicate measurements of 2 μg L −1 of selenium was 4.1%. The method was applied to the determination of selenium in environmental surface water samples with satisfactory recovery.

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

This study analyzes the linear relationship between climate variables and milk components in Iran by applying bootstrapping to include and assess the uncertainty. The climate parameters, Temperature Humidity Index (THI) and Equivalent Temperature Index (ETI) are computed from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis (2002–2010). Milk data for fat, protein (measured on fresh matter bases), and milk yield are taken from 936,227 milk records for the same period, using cows fed by natural pasture from April to September. Confidence intervals for the regression model are calculated using the bootstrap technique. This method is applied to the original times series, generating statistically equivalent surrogate samples. As a result, despite the short time data and the related uncertainties, an interesting behavior of the relationships between milk compound and the climate parameters is visible. During spring only, a weak dependency of milk yield and climate variations is obvious, while fat and protein concentrations show reasonable correlations. In summer, milk yield shows a similar level of relationship with ETI, but not with temperature and THI. We suggest this methodology for studies in the field of the impacts of climate change and agriculture, also environment and food with short-term data.

In this paper, we propose a methodology for the step-by-step solution of problems, which can be incorporated into a computer algebra system. Our main aim is to show all the intermediate evaluation steps of mathematical expressions from the start to the end of the solution. The first stage of the methodology covers the development of a formal grammar that describes the syntax and semantics of mathematical expressions. Using a compiler generation tool, the second stage produces a parser from the grammar description. The parser is used to convert a particular mathematical expression into an Abstract Syntax Tree (AST), which is evaluated in the third stage by traversing al its nodes. After every evaluation of some nodes, which corresponds to an intermediate solution step of the related expression, the resulting AST is transformed into the corresponding mathematical expression and then displayed. Many other algebra-related issues such as simplification, factorization, distribution and substitution can be covered by the solution methodology. We currently focuses on the solutions of various problems associated with the subject of derivative, equations, single variable polynomials, and operations on functions. However, it can easily be extended to cover the other subjects of general mathematics.

There are situations in which one needs to write various kinds of mathematical expressions, such as practicing tests and school exams. There is a variety of methods to produce such expressions, but they are usually based on a database. This paper addresses the production of new expressions using the template ones that can be derived from the evaluation process or entered by users. With special limitations on the values of parameters, some templates can be dynamically constructed for the automatic generation of mathematical expressions and represented in the form of classes. For this purpose, a new type of grammar is proposed. This grammar is similar to Context-Free Grammar, but it empowers the producer to gain control over the generation of rules for different expressions. Our work mainly focuses on generating mathematical expressions in a user-oriented way, using a predefined set of templates of production rules. The production of expressions is not completely random, and is based on the defined subject.

Mother's and infant exposure to cigarette smoke is one of the most important public health problems. There is no study in Iran evaluating the impact of cigarette smoke on infant growth and development. The purpose of this study was to determine the effects of cigarette. This prospective cohort study was conducted on 51 cigarette smoke-exposed infants (exposed group) and 51 non-exposed infants (non-exposed group). They were evaluated for weight, height and head circumference three times; five to seven days, two months and four months after birth. Urine samples were also collected in each turn. Exposure to secondhand smoke was assessed through questionnaires and urinary cotinine levels. The analysis was performed using an independent t-test, Mann-Whitney U test, chi-square and Fisher's exact and Kappa tests. Mean urinary cotinine level in the exposed group was 38.57±2.85 ng/mg creatinine at baseline, 86.95±1.16 at two months and 63.32±2.08 at four months of age. These indicated a gradual reduction of exposure from two to four months. The weight and height of the exposed group were significantly lower than the non-exposed group (P< 0.001) at two and four months after birth. The results of the present study showed that the exposure to secondhand smoke during infancy may lead to weight and height growth reduction in the first four months of life.

The main purpose of this study is to assess the relationship between four bioclimatic indices for cattle (environmental stress, heat load, modified heat load, and respiratory rate predictor indices) and three main milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when the cows use the natural pasture. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty information in the confidence intervals. The main results identify an interesting relationship between the milk compounds and climate indices under all climate conditions. During spring, there are reasonably high correlations between the fat and protein concentrations vs. the climate indices, whereas there are insignificant dependencies between the milk yield and climate indices. During summer, the correlation between the fat and protein concentrations with the climate indices decreased in comparison with the spring results, whereas the correlation for the milk yield increased. This methodology is suggested for studies investigating the impacts of climate variability/change on food and agriculture using short term data considering uncertainty.