Two modeling techniques [artificial neural network-genetic algorithm (ANN-GA) and stepwise regression analysis] were used to predict the effect of medium macro-nutrients on in vitro performance of pear rootstocks (OHF and Pyrodwarf). The ANN-GA described associations between investigating eight macronutrients (NO3-, NH4+, Ca2+, K+, Mg2+, PO42-, SO42-, and Cl−) and explant growth parameters [proliferation rate (PR), shoot length (SL), shoot tip necrosis (STN), chlorosis (Chl), and vitrification (Vitri)]. ANN-GA revealed a substantially higher accuracy of prediction than for regression models. According to the ANN-GA results, among the input variables concentrations (mM), NH4+ (301.7), and NO3-, NH4+ (64), SO42- (54.1), K+ (40.4), and NO3- (35.1) in OHF and Ca2+ (23.7), NH4+ (10.7), NO3- (9.1), NH4+ (317.6), and NH4+ (79.6) in Pyrodwarf had the highest values of VSR in data set, respectively, for PR, SL, STN, Chl, and Vitri. The ANN-GA showed that media containing (mM) 62.5 NO3-, 5.7 NH4+, 2.7 Ca2+, 31.5 K+, 3.3 Mg2+, 2.6 PO42-, 5.6 SO42-, and 3.5 Cl− could lead to optimal PR for OHF and optimal PR for Pyrodwarf may be obtained with media containing 25.6 NO3-, 13.1 NH4+, 5.5 Ca2+, 35.7 K+, 1.5 Mg2+, 2.1 PO42-, 3.6 SO42-, and 3 Cl−.

One of the major obstacles to the micropropagation of Prunus rootstocks has, up until now, been the lack of a suitable tissue culture medium. Therefore, reformulation of culture media or modification of the mineral content might be a breakthrough to improve in vitro multiplication of G × N15 (garnem). We found artificial neural network in combination of genetic algorithm (ANN-GA) as a very precise and powerful modeling system for optimizing the culture medium, So that modeling the effects of MS mineral salts (NH4+, NO3-, PO42-, Ca2+, K+, SO42-, Mg2+, and Cl−) on in vitro multiplication parameters (the number of microshoots per explant, average length of microshoots, weight of calluses derived from the base of stem explants, and quality index of plantlets) of G × N15. Showed high R2 correlation values of 87, 91, 87, and 74 between observed and predicted values were found for these four growth parameters, respectively. According to the ANN-GA results, among the input variables, NH4+ and NO3- had the highest values of VSR in data set for the parameters studied. The ANN-GA showed that the best proliferation rate was obtained from medium containing (mM) 27.5 NO3-, 14 NH4+, 5 Ca2+, 25.9 K+, 0.7 Mg2+, 1.1 PO42-, 4.7 SO42-, and 0.96 Cl−. The performance of the medium optimized by ANN-GA, denoted as YAS (Yadollahi, Arab and Shojaeiyan), was compared to that of standard growth media for all Prunus rootstock, including the Murashige and Skoog (MS) medium, (specific media) EM, Quoirin and Lepoivre (QL) medium, and woody plant medium (WPM) Prunus. With respect to shoot length, shoot number per cultured explant and productivity (number of microshoots × length of microshoots), YAS was found to be superior to other media for in vitro multiplication of G × N15 rootstocks. In addition, our results indicated that by using ANN-GA, we were able to determine a suitable culture medium formulation to achieve the best in vitro productivity.

The main aim of the present investigation is modeling and optimization of a new culture medium for in vitro rooting of G×N15 rootstock using an artificial neural network-genetic algorithm (ANN-GA). Six experiments for assessing different media culture, various concentrations of Indole – 3- butyric acid, different concentrations of Thiamine and Fe-EDDHA were designed. The effects of five ionic macronutrients (NH4+, NO3−, Ca2+, K+ and Cl−) on five growth parameters [root number (RN), root length (RL), root percentage (R%), fresh (FW) and dry weight (DW)] were evaluated using the ANN-GA method. The R2 correlation values of 0.88, 0.88, 0.98, 0.94 and 0.87 between observed and predicted values were acquired for all five growth parameters, respectively. The ANN-GA results indicated that among the input variables, K+ (7.6) and NH4+ (4.4), K+ (7.7) and Ca2+ (2.8), K+ (36.7) and NH4+ (4.3), K+ (14.7) and NH4+ (4.4) and K+ (7.6) and NH4+ (4.3) had the highest values of variable sensitivity ratio (VSR) in the data set, for RN, RL, R%, FW and DW, respectively. ANN-GA optimized LS medium for G×N15 rooting contained optimized amounts of 1 mg L−1 IBA, 100, 150, or 200 mg L−1 Fe-EDDHA and 1.6 mg L−1 Thiamine. The efficiency of the optimized culture media was compared to other standard media for Prunus rooting and the results indicated that the optimized medium is more efficient than the others.

Paclitaxel is the top-selling anticancer medicine in the world. In vitro culture of Corylus avellana has been made known as a promising and inexpensive strategy for producing paclitaxel. Fungal elicitors have been named as the most efficient strategy for enhancing the biosynthesis of secondary metabolites in plant cell culture. In this study, endophytic fungal strain HEF 17 was isolated from C. avellana and identified as Camarosporomyces flavigenus. C. avellana cell suspension culture (CSC) elicited with cell extract (CE) and culture filtrate (CF) derived from strain HEF 17 , either individually or combined treatment, in mid and late log phase was processed for modeling and optimizing growth and paclitaxel biosynthesis regarding CE and CF concentration levels, elicitor adding day, and CSC harvesting time using multilayer perceptron-genetic algorithm (MLP-GA). The results displayed higher accuracy of MLP-GA models (0.89-0.95) than regression models (0.56-0.85). The great accordance between the predicted and observed values of output variables (dry weight, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) for both training and testing subsets supported the excellent performance of developed MLP-GA models. MLP-GA method presented a promising tool for selecting the optimal conditions for maximum paclitaxel biosynthesis. An Excel ® estimator, HCC-paclitaxel, was designed based on MLP-GA model as an easyto-use tool for predicting paclitaxel biosynthesis in C. avellana CSC responding to fungal elicitors.

Mobile technology opens the door for next generation and let the learning occurs in anytime, anywhere and to be influence in a variety of learning contexts. The study was conducted in 329 teachers from 2352 secondary school teachers of Mathematics from 19 districts of Tehran using descriptive-field method during 2012-2013 academic years. A researcher-made Likhert-type questionnaire was developed to identify the teachers' viewpoint of the effect of Mlearning in different aspects of Mathematics learning. Twenty six questions measured the effect of different functional capabilities of mobile technology on increased motivation of learning Mathematics. Thirty seven questions measured the effect of different aspects of mobile learning on diversity of training methods of learning Mathematics. Thirty one questions measured the effect of different functional capabilities of mobile learning on students' participation in learning Mathematics. The reliability of the questionnaire using Chronbach's Alpha was 92%. One sample T test was used to examine significance of difference among the variables supporting the effect of M-leaning on different aspects of Mathematics learning. ANOVA was used to examine the effect of teachers' educational level and teaching experience on the effect of M-leaning on Mathematics learning. The results revealed that in teachers' viewpoint, mobile learning has a positive effect on motivating the students towards Mathematics. Also there is a positive and significant relation between using mobile learning and students' participation in Mathematics. Moreover, the relation between mobile learning and diversity of training methods of teachers is positive and significant. The findings of this survey show that teachers of Mathematics are interested in using the mobile technology in Mathematics learning. In their view this technology could increase students' motivation and participation in Mathematics learning and provide the opportunity of diversity of training methods of Mathematics.

The existing literature is inconsistent with respect to optimal dietary concentrations of glycine (Gly) and serine (Ser) in broiler feed. Therefore, we conducted a meta-analysis to investigate the response of broilers to dietary levels of Gly using a full quadratic model based on mixed model methodology. Response was measured as ADG (g/d), ADFI (g/d), and G:F (g/g). In addition, the influence of other dietary constituents was evaluated. This meta-analysis was based on a data set comprising a total of 9,626 broilers in 10 peer-reviewed papers that investigated the response of broilers to different dietary concentrations of Gly, achieved by addition of free Gly. The fitted quadratic model, with either Gly+Ser or the calculated glycine equivalent (Glyequi) of both amino acids as the independent variable, revealed that all model terms were significant (P ≤ 0.05), and hence proved a curvilinear relationship between these independent variables and response traits. The R(2) value and root MS error confirmed a strong relationship between observed and predicted traits. A comparison of the influence of Gly+Ser and Glyequi on response traits revealed that both approaches produced similar results. Because Glyequi should meet the physiological values of a diet better than Gly+Ser, models with 2 independent variables were conducted using Glyequi. The second independent variables were methionine (Met):TSAA ratio and the concentrations of cysteine (Cys) and CP. In models with one or 2 independent variables, the impact of dietary Gly on ADFI was low. By contrast, G:F was markedly influenced by dietary Gly; this effect intensified at lower Met:TSAA ratios and higher Cys and CP levels. ADG was also a function of Glyequi and the second independent variables. For ADG, an optimal Met:TSAA ratio of 0.655 and Cys concentration of 0.302% was calculated. Following the nonlinear nature of relationship, generally applicable replacement values could not be calculated. However, it was concluded that consideration of dietary Cys can diminish the requirement for Glyequi, and therefore, enable a reduction in the CP of broiler diets without limiting growth performance.

In making general recommendations for amino acids, researchers might survey various published data on the responses of poultry to amino acids. In this way, the use of appropriate mathematical and statistical approaches may help researchers draw appropriate conclusions. The purpose of this study was to develop artificial neural network (ANN)-based models to analyze data on the responses of broiler chickens [ADG and feed conversion ratio (FCR)] to protein and threonine from 21 to 42 d of age. A data set containing 92 dose-response treatments was extracted from the literature, compiled, and entered into the training and testing sets of the ANN models. The constructed models were subjected to a process of sensitivity analysis to evaluate the relative importance of the effects of dietary protein and threonine on ADG and FCR. Optimal values for the input variables (protein and threonine requirements) to maximize ADG and minimize FCR in birds were obtained by using the ANN models with an optimization algorithm. Based on the calculated goodness of fit criteria, it appeared that the platform of ANN-based models with the sensitivity analysis and optimization algorithms was an efficient tool for integrating published data on the responses of broiler chickens to threonine. The analyses of ANN models for ADG and FCR based on the compiled data set suggested that the dietary protein concentration was more important than the threonine concentration. The optimization algorithm revealed that diets containing 18.69% protein and 0.73% threonine could lead to optimal ADG, whereas the optimal FCR could be achieved with diets containing 18.71% protein and 0.75% threonine.

Central composite design (CCD; 5 levels and 4 factors), response surface methodology (RSM), and artificial neural network-genetic algorithm (ANN-GA) were used to evaluate the response of broiler chicks [ADG and feed conversion ratio (FCR)] to dietary standardized ileal digestible protein (dP), lysine (dLys), total sulfur amino acids (dTSAA), and threonine (dThr). A total of 84 battery brooder units of 5 birds each were assigned to 28 diets of CCD containing 5 levels of dP (18-22%), dLys (1.06-1.30%), dTSAA (0.81-1.01%), and dThr (0.66-0.86%) from 11 to 17 d of age. The experimental results of CCD were fitted with the quadratic and artificial neural network models. A ridge analysis (for RSM models) and a genetic algorithm (for ANN-GA models) were used to compute the optimal response for ADG and FCR. For both ADG and FCR, the goodness of fit in terms of R(2) and MS error corresponding to ANN-GA and RSM models showed a substantially higher accuracy of prediction for ANN models (ADG model: R(2) = 0.99; FCR model: R(2) = 0.97) compared with RSM models (ADG model: R(2) = 0.70; FCR model: R(2) = 0.71). The ridge maximum analysis on ADG and minimum analysis on FCR models revealed that the maximum ADG may be obtained with 18.5, 1.10, 0.89, and 0.73% dP, dLys, dTSAA, and dThr, respectively, in diet, and minimum FCR may be obtained with 19.44, 1.18, 0.90, and 0.75% of dP, dLys, dTSAA, and dThr, respectively, in diet. The optimization results of ANN-GA models showed the maximum ADG may be achieved with 19.93, 1.06, 0.90, and 0.76% of dP, dLys, dTSAA, and dThr, respectively, in diet, and minimum FCR may be achieved with 18.63, 1.26, 0.84, and 0.69% of dP, dLys, dTSAA, and dThr, respectively, in diet. The results of this study revealed that the platform of CCD (for conducting growth trials with minimum treatments), RSM model, and ANN-GA (for experimental data modeling and optimization) may be used to describe the relationship between dietary nutrient concentrations and broiler performance to achieve the optimal target.