Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks

In recent years, as a result of climate change as well as rainfall reduction in arid and semi‐arid regions, modelling qualitative and quantitative parameters belonging to aquifers has become crucially important. In Iran, as aquifers are treated as the most commonly used drinking water resources, modelling their qualitative and quantitative parameters is enormously important. In this paper, for the first time, values of salinity, total dissolved solids (TDS), groundwater level (GWL) and electrical conductivity (EC) of the Arak Plain, located in Markazi Province, Iran, are simulated by means of four modern artificial intelligence models including extreme learning machine (ELM), wavelet extreme learning machine (WELM), online sequential extreme learning machine (OSELM) and wavelet online sequential extreme learning machine (WOSELM) as well as the MODFLOW software for a 15‐year period monthly. To develop the hybrid artificial intelligence models, the wavelet is employed. First, the effective lags in estimating the qualitative and quantitative parameters of the groundwater are identified using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analysis. After that, four different models are developed by the selected input combinations and also the ACF and the PACF in the form of different lags for each of ELM, WAELM, OSELM and WOSELM methods. Then, the superior models in simulating the groundwater qualitative and qualitative parameters are detected by conducting a sensitivity analysis. To forecast the electrical conductivity (EC) by the best WOSELM model, the values of the Nash–Sutcliffe efficiency coefficient (NSC), Mean Absolute Error (MAE) and the scatter index (SI) are obtained to be 0.991, 18.005 and 4.28E‐03, respectively. In addition, the most effective lags in estimating these parameters are introduced. Subsequently, the results found by the MODFLOW model are compared with those of the artificial intelligence models and it is concluded that the latter are more accurate. For instance, the scatter index and Nash–Sutcliffe efficiency coefficient values calculated by WOSELM for TDS, respectively, are 5.34E‐03 and 0.991. Finally, an uncertainty analysis is conducted to evaluate the performance of different numerical models. For example, MODFLOW has an underestimated performance in simulating the salinity parameter.     

Development of a new analytical tool to design hierarchical truss beams for natural frequency

A set of recursive equations are introduced for optimum design of a wide range of hierarchical truss beams to satisfy a minimum required natural frequency. The design equations are based on a general analytical solution, which are derived based on a critical dimensional analysis. A practical example is examined in both 2D and 3D spaces, which promises substantial mass reduction, as much as 99%, when using high order hierarchical truss beams. The results are verified by numerical evaluation of three case studies. The approach looks also very promising to design nanostructures when it comes to the problems associated with vibration.     

A hybrid model of fuzzy goal programming and grey numbers in continuous project time,cost, and quality tradeoff

The purpose of this paper is to develop current mathematical models of cost, time, and quality tradeoffs in conditions that parameters of project activities are estimated uncertainly by grey numbers. In some projects like construction projects, activities can be done within a much shorter time by increasing in the resources, while project's cost may rise at the same time. In such situations, managers are usually required to determine the best combination of cost, time, and quality parameters of the activities, although their information regarding these parameters is limited and rather incomplete. The greyness of these parameters in the proposed method can aid managers to deal with these conditions. The most important aspect of the proposed model is that it considers uncertainty of the project planning data in the form of grey numbers. A combination of fuzzy goal programming and grey linear programming is also developed to solve the proposed model. Finally, this model will provide the managers with a stronger ability to face with uncertainty in project management and planning. The application of this model is examined in a numerical example. As its major finding, the model determines an optimal range in which the project managers can respond to intrinsic changes that may occur in the parameters during a project.     

Curcumin and Liver Disease: from Chemistry to Medicine

Curcumin, the natural yellow‐colored active principle, also called turmeric yellow, extracted from the perennial herb Curcuma longa L., has potent biological and pharmacological properties such as antioxidant, anti‐inflammatory, antifungal, antibacterial, anti‐ischemic, antitumor, and anticancer actions. The molecular mechanism of the hepatoprotective action of curcumin is due to its antioxidant properties and inhibitory activity against nuclear factor (NF)‐κB that regulates different proinflammatory and profibrotic cytokines. Overall, scientific reports demonstrate that curcumin has high therapeutic ability for treating hepatic disorders. Here is a systematic discussion of the hepatoprotective activity of curcumin and its possible mechanisms of actions.     

Preparation and functional properties of fish gelatin–chitosan blend edible films

With the goal of improving the physico-chemical performance of fish gelatin-based films, composite films were prepared with increasing concentrations of chitosan (Ch) (100G:0Ch, 80G:20Ch, 70G:30Ch, 60G:40Ch and 0G:100Ch, gelatin:Ch), and some of their main physical and functional properties were characterised. The results indicated that the addition of Ch caused significant increase (p < 0.05) in the tensile strength (TS) and elastic modulus, leading to stronger films as compared with gelatin film, but significantly (p < 0.05) decreased the elongation at break. Ch drastically reduced the water vapour permeability (WVP) and solubility of gelatin films, as this decline for the blend film with a 60:40 ratio has been of about 50% (p < 0.05). The light barrier measurements present low values of transparency at 600 nm of the gelatin–chitosan films, indicating that films are very transparent while they have excellent barrier properties against UV light. The structural properties investigated by FTIR and DSC showed a clear interaction between fish gelatin and Ch, forming a new material with enhanced mechanical properties.     

Electropolymerization and characterization of polyaniline, poly(2-anilinoethanol) and poly(aniline-co-2-anilinoethanol)

Applications of conducting polymers to biosensors have recently aroused much interest. This is because these molecular electronic materials offer control of different parameters such as polymer layer thickness, electrical properties and bio-reagent loading, diversity, ease of fabrication and potentially low cost, etc. Polyaniline, poly(2-anilinoethanol) and poly(aniline-co-2-anilinoethanol) preparations are performed with electrochemical (CV) method at room temperature, in a standard three-electrode cell. Homopolymer and the copolymers of aniline and 2-anilinoethanol films were deposited from 1?M acidic aqueous media containing 0.2?M aniline, 2-anilinoethanol by voltammetric sweep between ?0.1 and 1 V Ag/AgCl, at 20?mV/s^?1. The sweep was stopped after 30 cycles at ?0.1?V Ag/AgCl and the working electrode was covered by homopolymer and copolymer of aniline and 2-anilinoethanol. Characterizations of the products were carried out by cyclic voltammograms, UV?Cvisible, differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR) and electrochemical impedance (EIS) was employed to examine the water absorption of the synthesized polymers to be used in biosensor application. Electrochemical properties of polyaniline, poly(2-anilinoethanol) and poly(aniline-co-2-anilinoethanol) were studied and it is shown that with increased (2-anilinoethanol) content in the copolymer, its electroactivity, conductivity and resistance are reduced, though the processability and adhesion properties improve. The hydrophilicity of polymer film obtained has increased with increasing (2-anilinoethanol) content which leads to salt moving to the surface of steel.     

Spontaneous Formation of Liquid Crystals in Ultralarge Graphene Oxide Dispersions

A novel process is developed to synthesize graphene oxide sheets with an ultralarge size based on a solution‐phase method involving pre‐exfoliation of graphite flakes. Spontaneous formation of lyotropic nematic liquid crystals is identified upon the addition of the ultralarge graphene oxide sheets in water above a critical concentration of about 0.1 wt%. It is the lowest filler content ever reported for the formation of liquid crystals from any colloid, arising mainly from the ultrahigh aspect ratio of the graphene oxide sheets of over 30 000. It is proposed that the self‐assembled brick‐like graphene oxide nanostructure can be applied in many areas, such as energy‐storage devices and nanocomposites with a high degree of orientation.     

The effect of pH,salts and sugars on the rheological properties of cress seed (Lepidium sativum) gum

Effect of shear rate (15–600 s^?1), gum concentration (1–2%), pH (3–9), sucrose (10–40%), lactose (5–15%), NaCl (100–300 mm ) and CaCl₂ (5–50 mm ) was evaluated on apparent viscosity (η_a), flow behaviour index (n), consistency coefficient (K) and yield stress (τ₀) indices of cress seed gum (CSG) solutions. Different rheological models were used to fit the experimental data, although the Herschel–Bulkley model was found the best model. An increase in gum concentration led to an increase in τ₀, η_a, and k and a decrease in n values. The addition of salts lowered the k value; however, the n value showed slight significant change. The presence of sugar resulted in the enhancement of n, k, τ₀ and η_ap values. The existence of yield stress and pseudoplastic behaviour of CSG, its stability against salts, wide range of pH and synergic effect of sugar make it a good thickener and stabiliser in food formulations.     

Improving Rainfall Forecasting Efficiency Using Modified Adaptive Neuro-Fuzzy Inference System (MANFIS)

Rainfall is one of the most complicated effective hydrologic processes in runoff prediction and water management. The adaptive neuro-fuzzy inference system (ANFIS) has been widely used for modeling different kinds of nonlinear systems including rainfall forecasting. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) combines the capabilities of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) to solve different kinds of problems, especially efficient in rainfall prediction. This paper after reconsidering conventional ANFIS architecture brings up a modified ANFlS (MANFlS) structure developed with attention to making ANFIS technique more efficient regarding to Root Mean Square Error (RMSE), Correlation Coefficient (R ²), Root Mean Absolute Error (RMAE), Signal to Noise Ratio (SNR) and computing epoch. The modified ANFIS (MANFIS) architecture is simpler than conventional ANFIS with nearly the same performance for modeling nonlinear systems. In this study, two scenarios were introduced; in the first scenario, monthly rainfall was used solely as an input in different time delays from the time (t) to the time (t-4) to conventional ANFIS, second scenario used the modified ANFIS to improve the rainfall forecasting efficiency. The result showed that the model based Modified ANFIS performed higher rainfall forecasting accuracy; low errors and lower computational complexity (total number of fitting parameters and convergence epochs) compared with the conventional ANFIS model.