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.     

Flexural and Charpy impact behaviour of epoxy/glass fabric treated by nano-SiO2 and silane blend

A sizing formulation, containing compatible and incompatible silane coupling agents with epoxy resin in conjunction with nanoscale colloidal silica, was used to modify the surface of glass fabric. The modified glass fabric/epoxy resin composite panels were fabricated and characterised by flexural test, Charpy impact test and scanning electron microscope (SEM). By combining nano silica with silane blend in the fabric sizing, more energy was consumed under bending and impacting, which resulted in an improvement of the toughness in composites. The flexural strength, bending stain and Charpy impact strength of the epoxy composite/glass fabric treated with 1?wt-% nano silica and silane blend were ～42, ～22 and 35%, respectively, higher than those of silane blend coated glass fabric-reinforced composites (without nano silica). Furthermore, the change of the brittle fracture of the composite into ductile fracture was investigated by SEM micrographs. A possible toughening mechanism was also proposed.     

On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model

Advanced automatic data acquisition is now widely adopted in manufacturing industries and it is common to monitor several correlated quality variables simultaneously. Most of multivariate quality control charts are effective in detecting out-of-control signals based upon an overall statistics in multivariate manufacturing processes. The main problem of such charts is that they can detect an out-of-control event but do not directly determine which variable or group of variables has caused the out-of-control signal and what is the magnitude of out of control. This study presents a hybrid learning-based model for on-line analysis of out-of-control signals in multivariate manufacturing processes. This model consists of two modules. In the first module using a support vector machine-classifier, type of unnatural pattern can be recognized. Then by using three neural networks for shift mean, trend and cycle it can be recognized magnitude of mean shift, slope of trend and cycle amplitude for each variable simultaneously in the second module. The performance of the proposed approach has been evaluated using two examples. The output generated by trained hybrid model is strongly correlated with the corresponding actual target value for each quality characteristic. The main contributions of this work are recognizing the type of unnatural pattern and classification major parameters for shift, trend and cycle and for each variable simultaneously by proposed hybrid model.     

Three-dimensional free vibration of variable thickness thick circular and annular isotropic and functionally graded plates on Pasternak foundation

This paper describes a study of three-dimensional free vibration analysis of thick circular and annular isotropic and functionally graded (FG) plates with variable thickness along the radial direction, resting on Pasternak foundation. The formulation is based on the linear, small strain and exact elasticity theory. Plates with different boundary conditions are considered and the material properties of the FG plate are assumed to vary continuously through the thickness according to power law. The kinematic and the potential energy of the plate-foundation system are formulated and the polynomial-Ritz method is used to solve the eigenvalue problem. Convergence and comparison studies are done to demonstrate the correctness and accuracy of the present method. With respect to geometric parameters, elastic coefficients of foundation and different boundary conditions some new results are reported which may be used as benchmark solutions for future researches.     

Active fault detection and isolation of discrete-time linear time-varying systems: a set-membership approach

Active fault detection and isolation (AFDI) is used for detection and isolation of faults that are hidden in the normal operation because of a low excitation signal or due to the regulatory actions of the controller. In this paper, a new AFDI method based on set-membership approaches is proposed. In set-membership approaches, instead of a point-wise estimation of the states, a set-valued estimation of them is computed. If this set becomes empty the given model of the system is not consistent with the measurements. Therefore, the model is falsified. When more than one model of the system remains un-falsified, the AFDI method is used to generate an auxiliary signal that is injected into the system for detection and isolation of faults that remain otherwise hidden or non-isolated using passive FDI (PFDI) methods. Having the set-valued estimation of the states for each model, the proposed AFDI method finds an optimal input signal that guarantees FDI in a finite time horizon. The input signal is updated at each iteration in a decreasing receding horizon manner based on the set-valued estimation of the current states and un-falsified models at the current sample time. The problem is solved by a number of linear and quadratic programming problems, which result in a computationally efficient algorithm. The method is tested on a numerical example as well as on the pitch actuator of a benchmark wind turbine.     

Preparation of a novel potassium ion imprinted polymeric nanoparticles based on dicyclohexyl 18C6 for selective determination of K+ ion in different water samples

This work reports the first application of the ion imprinting technology for determination of potassium ion by precipitation polymerization method. Ion imprinted polymeric (IIP) nanoparticles were prepared by using dicyclohexyl 18C6 (DC18C6) as a K⁺ ion selective crown ether, in the acetonitrile–dimethylsulfoxide (3:1; v/v) mixture as porogen. The imprint potassium ion was removed from the polymeric matrix using 0.5 M HNO₃. The scanning electron microscopy (SEM) micrographs showed colloidal nanoparticles of 60–90 nm in diameter and slightly irregular in shape. The obtained ion-imprinted particles for K⁺ showed selective recognition with rapid adsorption and desorption processes. It was found that imprinting results in increased affinity of the material toward K⁺ ion over other competitor metal ions with the same charge and/or close ionic radius. The synthesized IIP nanobeads were shown to be promising for solid-phase extraction coupled with flame photometry for determination of trace K⁺ ion in different water samples.     

Low voltage low noise open loop automatic amplitude control for voltage-controlled oscillators

This paper presents a low voltage low noise open loop automatic amplitude control method for voltage-controlled oscillators (VCO’s). In this method a feedback mechanism keeps the VCO at its optimum amplitude over temperature and process variations and then the loop is broken to avoid noise injection form the control circuitry to the VCO. The loop does not add extra noise to the VCO. Based on the proposed method, a low voltage low noise LC-VCO was designed for a low phase noise application in TSMC 0.18 micron RFCMOS technology. Simulations show considerable improvement in the phase noise with the application of the proposed method.     

Composition and biological activities of essential oils from four Heracleum species

The composition of essential oils from aerial parts of Heracleum persicum, a widely used medicinal plant, and three other Heracleum species growing wild in Iran were analysed by GC and GC–MS. Myristicin (53.6%), (E)-anethole (25.0%), hexyl butanoate (29.7%) and elemicin (41.1%) were the major compounds of Heracleum pastinacifolium, H. persicum, Heracleum rechingeri and Heracleum transcaucasicum, respectively. Cytotoxic activity assessed on three human cancer cell lines (HeLa, LS180 and Raji), showed that essential oils from H. transcaucasicum (IC₅₀ values; 0.362–0.594 mg/ml) followed by H. pastinacifolium (0.497–1.398 mg/ml) had moderate antitumoral activities. In the DPPH radical scavenging assay, H. pastinacifolium and H. persicum oils showed the highest activities with IC₅₀ values of 7.3 and 7.4 mg/ml, respectively. Antioxidant activity correlated well with the total phenolic content of the oils. None of the essential oils showed significant antimicrobial activities.     

A Convenient Method for Preparation of Amphiphilic Monomethoxypoly (Ethylene Glycol)–Polystyrene Diblock Copolymer by NMRP Technique

Amphiphilic block copolymers are macromolecular compounds of great importance from both fundamental scientific and many technological point of views for a large variety of applications. Amphiphilic diblock copolymer containing segments of monomethoxypoly(ethylene glycol) and polystyrene (MPEG-b-PS) was synthesised by a convenient method for preparation of macroinitiator MPEG-TEMPO for ‘living’ free radical polymerization (NMRP technique). Initially, derivative of MPEG with chlorine function has been prepared in an one-step reaction with thionyl chloride. 1-hydroxy-2,2,6,6-tetramethyl-piperidine (TEMPO-OH) obtained by reduction of 2,2,6,6-tetramethyl-piperidinyl-1-oxy (TEMPO) with sodium ascorbate was coupled with chlorinated MPEG to yield the macroinitiator MPEG terminated with a TEMPO unit (MPEG-TEMPO), which was further used to prepare the diblock copolymer MPEG-b-PS of styrene. The product was purified and identified by ¹H NMR, GPC and, FT-IR.