Analysis of field-driven clocking for molecular quantum-dot cellular automata based circuits

Molecular quantum-dot cellular automaton (QCA) offers an alternative paradigm for computing at the nano-scale. QCA circuits require an external clock which can be generated using a network of submerged electrodes to synchronize information flow and provide the required power to drive the computation. In this paper, the effect of electrode separation and applied potential on the likelihood of different QCA cell states of molecular cells located above and in between two adjacent electrodes is analyzed. Using this analysis, estimates of operational ranges are developed for the placement, applied potential, and relative phase between adjacent clocking electrodes to ensure that only those states that are used in the computation are energetically favorable. Conclusions on the trade-off between cell size, cell-to-cell distance, and applied clocking potential are drawn and the temperature dependence of the operation of fundamental QCA building blocks is considered.     

Biodegradation of natural and synthetic estrogens in moving bed bioreactor

Estrogen hormones as a group of endocrine disruptive compounds(EDC) can interfere with endocrine system in humans and animals. The goal of this study was to investigate the elimination rate of Estrone(El), 17β-estradiol(E2) and 17α-ethinyl estradiol(EE2) in Moving Bed Bioreactor(MBBR). These analytes extracted by Dispersive Liquid-Liquid Microextraction(DLLME) technique, followed by derivatization, and detected by GC/MS. Estrogen removal efficiency in MBBR improved at high solid retention times(SRTs), which notion is owing to development of nitrification. Estrogen specific removal rate was between 0.22-1.45 μg·(g VSS)~(-1)·d~(-1) for natural and synthetic hormones. The adsorption rate was 0.9%-3.2%, 0-1.3%, and 0.7%-5.7% for El, E2, and EE2, respectively. In addition, the biodegradation rates were more than 95% for these compounds. These results illustrated that in MBBR,the biodegradation and the adsorption to biomass are considered as two significant routes for elimination of estrogenic compounds. As a whole, the deterioration rate of estrogens enhanced by MBBR compared to other biological wastewater treatment processes such as conventional activated sludge.     

Comparative study of methods for producing gum arabic powder and the impact of DIC treatment (instant controlled pressure drop) on the properties of the product

In this study, gum arabic powder was produced according to four methods by inserting a restructuring stage by DIC treatment (instant controlled pressure drop) in the classic process of grinding and spray drying. Properties of the final product were compared, and the results show that DIC treatment enables to control and improve the properties of the powder. The DIC treatment can cause a controlled increase in the tapped bulk density, filling rate, compressibility, porosity; and a reduction in interstitial air volume and the loose bulk density. It also facilitated the subsequent grinding and intensified the drying kinetics. The impacts of pressure and DIC treatment time were examined. Pressure was the strongest factor influencing the properties of the gum arabic powder. Selecting an optimal pressure and treatment time plays a decisive role in controlling the properties of powders.     

A sliding mode observer for uncertain nonlinear systems based on multiple models approach

This paper presents a method of state estimation for uncertain nonlinear systems described by multiple models approach. The uncertainties, supposed as norm bounded type, are caused by some parameters’ variations of the nonlinear system. Linear matrix inequalities (LMIs) have been established in order to ensure the stability conditions of the multiple observer which lead to determine the estimation gains. A sliding mode gain has been added in order to compensate the uncertainties. Numerical simulations through a state space model of a real process have been realized to show the robustness of the synthesized observer.     

Thin‐film encapsulated white organic light top‐emitting diodes using a WO3/Ag/WO3 cathode to enhance light out‐coupling

An alternative design of a semitransparent cathode for top‐emission white‐fluorescent organic light‐emitting diodes (OLEDs) has been investigated. The scope of this study was to improve the luminance of OLEDs used for displays while keeping the current density versus voltage characteristic unchanged for addressing purposes. The use of an optical simulation tool allowed the optimization of the tri‐layer cathode WO₃/Ag/WO₃ to increase the light out‐coupling coefficient of the device leading to an increased white emission compared with a reference device with a Ca/Ag cathode. An increase of ~40% in luminance has been calculated by simulation and experimentally confirmed. The p‐i‐n OLED structure underneath the tri‐layer cathode allowed an efficient injection of electrons independently from the work function of WO₃. The WO₃/Ag/WO₃ cathode has been also confirmed to be compatible with the atomic layer deposition technique for thin film encapsulation. Finally, lifetime measurements up to 600 h have been carried out to quantify the enhancements induced by the new cathode compared with the control device. It has been found that lifetimes of both cathode architectures are similar on this time scale, while the WO₃/Ag/WO₃ cathode shows a lower voltage drift versus aging.     

Using bottom surface reflectance to map coastal marine areas: a new application method for Lyzenga's model

Remote sensing is widely used in coastal management. Lyzenga's model has been traditionally used to explain the relationship between bottom surface reflectance and the radiance level measured by satellite. Due to its central assumption, this model lacks accuracy compared with the other radiative transfer models. Nonetheless, it enables, with a single and simple equation, representation of the multiple optical processes taking place in coastal areas. Mapping processes associated with this model may include radiometric correction, a technique previously pointed out as a major driver of mapping accuracy. Radiometric correction is generally based on a depth-invariant index, efficient for clear waters (Jerlov water type I to II) but largely unsuitable when transparency decreases (Jerlov water type II to III). In order to overcome this problem, we developed a new index for radiometric correction, which combines bathymetry data with attenuation coefficients. The improved efficiency of our model with regard to the traditional depth invariant index was demonstrated through two case studies: Funakoshi Bay (Japan; Jerlov water type II) and the Gabes Gulf part located off Mahares (Tunisia; Jerlov water type II to III).     

A novel adaptive fuzzy predictive control for hybrid systems with mixed inputs

This paper proposes a new adaptive nonlinear model predictive control (NMPC) methodology for a class of hybrid systems with mixed inputs. For this purpose, an online fuzzy identification approach is presented to recursively estimate an evolving Takagi–Sugeno (eTS) model for the hybrid systems based on a potential clustering scheme. A receding horizon adaptive NMPC is then devised on the basis of the online identified eTS fuzzy model. The nonlinear MPC optimization problem is solved by a genetic algorithm (GA). Diverse sets of test scenarios have been conducted to comparatively demonstrate the robust performance of the proposed adaptive NMPC methodology on the challenging start-up operation of a hybrid continuous stirred tank reactor (CSTR) benchmark problem.     

Stabilization of gas-lift oil wells by a nonlinear model predictive control scheme based on adaptive neural network models

Producing oil from gas-lift wells are often faced with severe producing oscillatory flow regimes. A major source of the oscillations is recognized as casing–heading instability which is caused by dynamic interaction between injection gas and multiphase fluid. This phenomenon poses strict production-related challenges in terms of lower average production and strain on downstream equipment. In this paper, an effective solution is proposed based on integration of an online interpretation dynamic model and a nonlinear model predictive control (NMPC) scheme. The paper uses adaptive growing and pruning radial basis function (GAP-RBF) neural networks (NNs) to recursively capture the essential dynamics of casing–heading instability in a nonlinear model structure. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are comparatively investigated to adaptively train modified GAP-RBF NNs. NMPC methodology is developed on the basis of the identified nonlinear NN model for real-time stabilization of casing–heading instability in an oil reservoir equipped with a gas-lift production well. A set of test studies has been conducted to explore the superior performance of the proposed adaptive NMPC controller under different scenarios for an oil reservoir simulated in ECLIPSE and linked to a complementary gas-lifted oil well simulated in programming environment.     

Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems

The issue of fault detection and diagnosis (FDD) has gained widespread industrial interest in process condition monitoring applications. An innovative data-driven FDD methodology has been presented in this paper on the basis of a distributed configuration of three adaptive neuro-fuzzy inference system (ANFIS) classifiers for an industrial 440 MW power plant steam turbine with once-through Benson type boiler. Each ANFIS classifier has been developed for a dedicated category of four steam turbine faults. A preliminary set of conceptual and experimental studies has been conducted to realize such fault categorization scheme. A proper selection of four measured variables has been configured to feed each ANFIS classifier with the most influential diagnostic information. This consequently leads to a simple distributed FDD system, facilitating the training and testing phases and yet prevents operational deficiency due to possible cross-correlated measured data effects. A diverse set of test scenarios has been carried out to illustrate the successful diagnostic performances of the proposed FDD system against 12 major faults under challenging noise corrupted measurements and data deformation corresponding to a specific fault time history pattern.