New Approaches for Estimation of Monthly Rainfall Based on GEP-ARCH and ANN-ARCH Hybrid Models

Accurate estimation of rainfall has an important role in the optimal water resources management, as well as hydrological and climatological studies. In the present study, two novel types of hybrid models, namely gene expression programming-autoregressive conditional heteroscedasticity (GEP-ARCH) and artificial neural networks-autoregressive conditional heteroscedasticity (ANN-ARCH) are introduced to estimate monthly rainfall time series. To fulfill this purpose, five stations with various climatic conditions were selected in Iran. The lagged monthly rainfall data was utilized to develop the different GEP and ANN scenarios. The performance of proposed hybrid models was compared to the GEP and ANN models using root mean square error (RMSE) and coefficient of determination (R²). The results show that the proposed GEP-ARCH and ANN-ARCH models give a much better performance than the GEP and ANN in all of the studied stations with various climates. Furthermore, the ANN-ARCH model generally presents better performance in comparison with the GEP-ARCH model.     

Drought Monitoring Using the Multivariate Standardized Precipitation Index (MSPI)

One major characteristic of the Standardized Precipitation Index (SPI) is its flexibility to be calculated in a variety of time scales and hence being aware of different types of droughts. However, various time scales may result in confusion of the water resources’ researchers, decision makers and users in identifying and specifying drought periods in a certain region. To solve this problem in this article, a multivariate approach has been utilized having the ability to aggregate a variety of the SPI time series into a new time series called the Multivariate Standardized Precipitation Index (MSPI). The MSPI is based on the principal components analysis (PCA) of the SPI time series in a certain location. Having specified the first principal component’s (PC₁’s) scores, the MSPI would be simply attained by dividing PC₁’s values by the monthly standard deviation. In this article, MSPI’s capability in depicting the variability of the SPI time series (in five ranges of time scales, including 3–6, 6–12, 3–12, 12–24, and 24–48 months) was studied at four weather stations representing the four different climates in Iran from the driest to the wettest climates. The results showed that the PC₁ is able to justify more than 74 % of the variability for the selected sets of the SPI time scales in the studied climates. Also, it became clear that the drought and wet severity classes monitored by MSPIs matched very satisfyingly to those of the five sets of the SPI time scales. Therefore, in cases where the aggregation time scales for calculating the SPI are not previously known, this study recommends the researchers use the MSPI.     

The Addiction-Stroop test: Theoretical considerations and procedural recommendations.

Decisions about using addictive substances are influenced by distractions by addiction-related stimuli, of which the user might be unaware. The addiction-Stroop task is a paradigm used to assess this distraction. The empirical evidence for the addiction-Stroop effect is critically reviewed, and meta-analyses of alcohol-related and smoking-related studies are presented. Studies finding the strongest effects were those in which participants had strong current concerns about an addictive substance or such concerns were highlighted through experimental manipulations, especially those depriving participants of the substance. Theories to account for addiction-related attentional bias are discussed, of which the motivational theory of current concerns appears to provide the most complete account of the phenomenon. Recommendations are made for maximizing the precision of the addiction-Stroop test in future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Using class-based feature selection for the classification of hyperspectral data

The rapid advances in hyperspectral sensing technology have made it possible to collect remote-sensing data in hundreds of bands. However, the data-analysis methods that have been successfully applied to multispectral data are often limited in achieving satisfactory results for hyperspectral data. The major problem is the high dimensionality, which deteriorates the classification due to the Hughes Phenomenon. In order to avoid this problem, a large number of algorithms have been proposed, so far, for feature reduction. Based on the concept of multiple classifiers, we propose a new schema for the feature selection procedure. In this framework, instead of using feature selection for whole classes, we adopt feature selection for each class separately. Thus different subsets of features are selected at the first step. Once the feature subsets are selected, a Bayesian classifier is trained on each of these feature subsets. Finally, a combination mechanism is used to combine the outputs of these classifiers. Experiments are carried out on an Airborne Visible/Infrared Imaging Spectroradiometer (AVIRIS) data set. Encouraging results have been obtained in terms of classification accuracy, suggesting the effectiveness of the proposed algorithms.     

Morphology, rheology and dynamic mechanical properties of PP/EVA/clay nanocomposites

This paper reports on morphology, rheology and dynamic mechanical properties of polypropylene (PP)/ethylene vinyl acetate (EVA) copolymer/clay nanocomposite system prepared via a single step melt compounding process using a twin screw micro-compounder. Scanning electron microscopic (SEM) investigations revealed that the dispersed phase droplet size was reduced with incorporation of an organo-modified montmorillonite (OMMT). This reduction was more significant in presence of a maleated PP (PP-g-MAH) used as compatibilizer. Phase inversion in the compatibilized blends caused a further decrease in PP droplet size. The OMMT gallery spacing was higher in nanocomposites with EVA as matrix which could be attributed to higher tendency of OMMT nanoparticles towards EVA rather than PP. This enhanced tendency was confirmed by rheological analysis too. Transmission electron microscopy (TEM) results also showed that the majority of OMMT nanoparticles were localized on the interface and within EVA droplets. According to dynamic mechanical analysis, the compatibilized nanocomposites showed higher storage and loss moduli due to better dispersion of OMMT layers. The modulus enhancement of nanocomposites as a function of OMMT volume fraction was modeled by Halpin-Tsai’s-Nielsen expression of modulus for nanocomposites. The results of modeling suggested that the aspect ratio of the intercalated OMMT, in the form of Einstein coefficient (K _E), plays a determining role in the modulus enhancement of nanocomposites.     

Matrix based method for synthesis of main intensified and integrated distillation sequences

The objective of many studies in this area has involved access to a column-sequencing algorithm enabling designers and researchers alike to generate a wide range of sequences in a broad search space, and be as mathematically and as automated as possible for programing purposes and with good generality. In the present work an algorithm previously developed by the authors, called the matrix method, has been developed much further. The new version of the algorithm includes thermally coupled, thermodynamically equivalent, intensified, simultaneous heat and mass integrated and divided-wall column sequences which are of gross application and provide vast saving potential both on capital investment, operating costs and energy usage in industrial applications. To demonstrate the much wider searchable space now accessible, a three component separation has been thoroughly examined as a case study, always resulting in an integrated sequence being proposed as the optimum.     

A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System

This paper presents a new and accurate algorithm for locating faults in a combined overhead transmission line with underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses 10 ANFIS networks and consists of 3 stages, including fault type classification, faulty section detection and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., fundamental component of three phase currents and zero sequence current. Another ANFIS network is used to detect the faulty section, whether the fault is on the overhead line or on the underground cable. Other eight ANFIS networks are utilized to pinpoint the faults (two for each fault type). Four inputs, i.e., the dc component of the current, fundamental frequency of the voltage and current and the angle between them, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on each part of the combined line. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances. Simulation results confirm that the proposed method can be used as an efficient means for accurate fault location on the combined transmission lines.     

A low‐power,area‐efficient multichannel receiver for micro MRI

A new integrated, low‐noise, low‐power, and area‐efficient multichannel receiver for magnetic resonance imaging (MRI) is described. The proposed receiver presents an alternative technique to overcome the use of multiple receiver front‐ends in parallel MRI. The receiver consists of three main stages: low‐noise pre‐amplifier, quadrature down‐converter, and a band pass filter (BPF). These components are used to receive the nuclear magnetic resonance signals from a 3 × 3 array of micro coils. These signals are combined using frequency domain multiplexing (FDM) method in the pre‐amplifier and BPF stages, then amplified and filtered to remove any out‐of‐band noise before providing it to an analog‐to‐digital converter at the low intermediate frequency stage. The receiver is designed using a 90 nm CMOS technology to operate at the main B₀ magnetic field of 9.4 T, which corresponds to 400 MHz. The receiver has an input referred noise voltage of 1.1 nV/√Hz, a total voltage gain of 87 dB, a power consumption of 69 mA from a 1 V supply voltage, and an area of 305 µm × 530 µm including the reference current and bias voltage circuits. Copyright © 2013 John Wiley & Sons, Ltd.     

A wide range delay locked loop for low power and low jitter applications

In this paper, a design of analog delay locked loop is introduced in which new techniques are applied to eventually increase operating frequency range and reduce jitter considerably. In this design, all blocks of a delay locked loop including a voltage controlled delay line, charge pump, and loop filter are accurately designed. A new delay cell is proposed with wide delay range, in which increase of delay range results in using fewer cells, and consequently the power consumption will decrease. Current mirror techniques and feedback in the proposed charge pump also cause higher current matching and better jitter performance. This delay locked loop, which is designed with TSMC 0.18‐μm CMOS technology, has a wide frequency range from 217 to 800 MHz. It consumes maximum 3.4‐mW and minimum 2.6‐mW power dissipation in source voltage of 1.8 V, which is suitable for low power applications. It also has an appropriate lock time that is at least equal to 3 clock cycles at 217 MHz and at most 25 clock cycles at 800 MHz. Jitter performance in this delay locked loop is improved significantly: RMS jitter is 0.65 ps at 800 MHz and 2.54 ps at 217 MHz. Moreover, its maximum peak‐to‐peak jitter is equal to 5.17 ps, and its minimum peak‐to‐peak jitter is equal to 1.39 ps at 217 and 800 MHz, respectively.     

Adaptive neural dynamic surface control of MIMO stochastic nonlinear systems with unknown control directions

In this paper, an adaptive neural output‐feedback control approach is considered for a class of uncertain multi‐input and multi‐output (MIMO) stochastic nonlinear systems with unknown control directions. Neural networks (NNs) are applied to approximate unknown nonlinearities, and K‐filter observer is designed to estimate unavailable system's states. Due to utilization of Nussbaum gain function technique in the proposed approach, the singularity problem and requirement to prior knowledge about signs of high‐frequency gains are removed, simultaneously. Razumikhin functional method is employed to deal with unknown state time‐varying delays, so that the offered control approach is free of common assumptions on derivative of time‐varying delays. Also, an adaptive neural dynamic surface control is developed; hence, explosion of complexity in conventional backstepping method is eliminated, effectively. The boundedness of all the resulting closed‐loop signals is guaranteed in probability; meanwhile, convergence of the tracking errors to adjustable compact set in the sense of mean quartic value is also proved. Finally, simulation results are shown to verify and clarify efficiency of the offered approach. Copyright © 2016 John Wiley & Sons, Ltd.