Experimental dataset on adsorption of Arsenic from aqueous solution using Chitosan extracted from shrimp waste; optimization by response surface methodology with central composite design

The aim of data was to evaluate the efficiency of chitosan extracted from shrimp waste for Arsenic adsorption and optimization by response surface methodology (RSM) with central composite design (CCD). The data showed that, with increasing contact time, the amount of adsorption increased and the optimal contact time was about 60 min. With increasing pH decreased adsorption, although this reduction was not significant. The optimum pH was obtained at 4.41. The average amount of adsorbent capacity was also about 1.3 mg/g. Overall, chitosan extracted from shrimp waste could be considered as an efficient material for the adsorption of Arsenic from aqueous solution. Keywords: Chitosan, Arsenic, Aqueous solution, Adsorptio

Climatic zonation and land suitability determination for saffron in Khorasan-Razavi province using data mining algorithms

Yield prediction for agricultural crops plays an important role in export-import planning, purchase guarantees, pricing, secure profits and increasing in agricultural productivity. Crop yield is affected by several parameters especially climate. In this study, the saffron yield in the Khorasan-Razavi province was evaluated by different classification algorithms including artificial neural networks, regression models, local linear trees, decision trees, discriminant analysis, random forest, support vector machine and nearest neighbor analysis. These algorithms analyzed data for 20 years (1989-2009) including 11 climatological parameters. The results showed that a few numbers of climatological parameters affect the saffron yield. The minimum, mean and maximum of temperature, had the highest positive correlations and the relative humidity of 6.5h, sunny hours, relative humidity of 18.5h, evaporation, relative humidity of 12.5h and absolute humidity had the highest negative correlations with saffron cultivation areas, respectively. In addition, in classification of saffron cultivation areas, the discriminant analysis and support vector machine had higher accuracies. The correlation between saffron cultivation area and saffron yield values was relatively high (r=0.38). The nearest neighbor analysis had the best prediction accuracy for classification of cultivation areas. For this algorithm the coefficients of determination were 1 and 0.944 for training and testing stages, respectively. However, the algorithms accuracy for prediction of crop yield from climatological parameters was low (the average coefficients of determination equal to 0.48 and 0.05 for training and testing stages). The best algorithm i.e. nearest neighbor analysis had coefficients of determination equal to 1 and 0.177 for saffron yield prediction. Results showed that, using climatological parameters and data mining algorithms can classify cultivation areas. By this way it is possible to identify areas that have similar climate to prone areas and recognize suitable areas for cultivation

An Improved Harmony Search Algorithm for Proactive Routing Protocol in VANET

Vehicular ad-hoc network (VANET) is the direct application of mobile ad-hoc network (MANET) in which the nodes represent vehicles moving in a city or highway scenario. The deployment of VANET relies on routing protocols to transmit the information between the nodes. Different routing protocols that have been designed for MANET were proposed to be applied in VANET. However, the real-time implementation is still facing challenges to fulfill the quality of service (QoS) of VANET. Therefore, this study mainly focuses on the well-known MANET proactive optimized link state routing (OLSR) protocol. The OLSR in VANET gives a moderate performance; this is due to its necessity of maintaining an updated routing table for all possible routes. The performance of OLSR is highly dependent on its parameter. Thus, finding optimal parameter configurations that best fit VANET features and improve its quality of services is essential before its deployment. The harmony search (HS) is an emerging metaheuristic optimization algorithm with features of simplicity and exploration efficiency. Therefore, this paper aims to propose an improved harmony search optimization (EHSO) algorithm that considers the configuration of the OLSR parameters by coupling two stages, a procedure for optimization carried out by the EHSO algorithm based on embedding two popular selection methods in its memory, namely, roulette wheel selection and tournament selection. The experimental analysis shows that the proposed approach has achieved the QoS requirement, compared to the existing algorithms

Data on using macro invertebrates to investigate the biological integrity of permanent streams located in a semi-arid region

The aquatic ecosystems are continuously endangered due to variety of hazardous chemicals containing different toxic agents which can be emitted from anthropogenic sources. Besides the increasing of human population, various kinds of contaminants enter into the surface water resources. The aim of the present study was to investigate the abundance and diversity of macro invertebrates in two permanent streams located in the northern part of Tehran. The biological integrity of the streams was determined by manual sampling approach at five points. The distances between the sampling points were at least 2 km. The bio indicator organisms, organic pollution, and dissolved oxygen were measured. The different types of benthic invertebrates such as riffle beetle, midge and caddish fly larvae, dragon fly, may fly and stone fly nymph, riffle beetle adult, pyralid caterpillar, leech, and pouch snail were identified. It can be concluded that, the identified benthic macro invertebrates can be served as appropriate biological indicator in the studied area. Keywords: Biological integrity, Tehran, Macro invertebrate

MWCNT-Fe3O4 as a superior adsorbent for microcystins LR removal: Investigation on the magnetic adsorption separation, artificial neural network modeling, and genetic algorithm optimization

Magnetic multi-wall carbon nanotube (MMWCNT) was prepared by simple protocol and its structural features were characterized using SEM, TEM, and XRD analysis. The association between removal (%) and variables such as pH (3 − 11), adsorbent amounts (0.005, 0.1, 0.25, 0.5, 0.75, and 1 g/L), reaction time (5–180 min), and concentration of microcystins-LR (10, 25, 50, 75, and 125 μg/L) was investigated and optimized. The results of the isotherm study indicated that Langmuir offered high determination coefficients (R2 = 0.993, 0.996, and 0.998, for the three different working temperatures of 20 °C, 35 °C, and 50 °C respectively) and was the optimum isotherm to anticipate adsorption of MC-LR (microcystins-LR) by magnetic MWCNT adsorbent. The kinetic study revealed that the adsorption kinetics of MC-LR could be better defined using the pseudo-second-order model. A three-layer model of an artificial neural network was applied to forecast the MC-LR removal efficiency by magnetic MWCNTs over 66 runs. To forecast the MC-LR removal efficiency, the minimum mean squared error of 0.0011 and determination coefficient (R2) of 0.9813 were obtained. The use of the artificial neural network model achieved a good level of compatibility between the acquired and anticipated data