Synthesis and enzymatic degradation of epichlorohydrin cross-linked pectins

The water solubility of pectin was successfully decreased by cross-linking with increasing amounts of epichlorohydrin in the reaction media. The initial molar ratios of epichlorohydrin/ galacturonic acid monomer in the reaction mixtures were 0, 0.37, 0.56, 0.74, 1.00, 1.47, and 2.44. The resulting epichlorohydrin cross-linked pectins were thus referred to as C-LP0, C-LP37, C-LP56, C-LP75, C-LP100, C-LP150, and C-LP250, respectively. Methoxylation degrees ranged from 60.5 ± 0.9% to 68.0 ± 0.6%, and the effective cross-linking degrees, determined by quantification of the hydroxyl anions consumed during the reaction, were 0, 17.8, 26.0, 38.3, 46.5, 53.5, and 58.7%, respectively. After incubating the different cross-linked pectins (0.5% w/v) in 25 mL of 0.05 M acetate-phosphate buffer (pH 4.5), containing 50 µL of Pectinex® Ultra SP-L (pectinolytic enzymes), between 60 and 80% of the pectin osidic bounds were broken in less than 1 hr. Moreover, increasing the cross-linking degree only resulted in a weak slowing on the enzymatic degradation velocity.     

Decision Analysis Applied to Tunnel Exploration Planning. II: Consideration of Uncertainty

Tunnel exploration is a classic problem of decision making under uncertainty. This paper, Part II, dealing with the exploration problem, discusses the sources of uncertainty in tunnel exploration, and provides techniques to formally incorporate uncertainties into the exploration planning. Part I described the state of uncertainty in geology and exploration reliability using assigned probabilities. These probabilities are, however, uncertain. Construction costs are also uncertain. This paper presents tools with which the decision maker is able to assess the effects of these uncertainties on exploration decisions. Decisions are therefore made based on the value of collecting new, uncertain information through “virtual” exploration prior to actually exploring.     

Noise Figure Reduction in Externally Modulated Analog Fiber-Optic Links

An octave-bandwidth microwave photonic link with a third-order limited spurious-free dynamic range of 121 dB in a 1-Hz bandwidth has been demonstrated. The link noise figure of 9 dB at a modulation frequency of 2 GHz was achieved by using a bias-shifted Mach-Zehnder modulator with an optical input power of 500 mW. This level of performance was realized without electronic or optical linearization     

Everything over IP — an overview of the strategic change in voice and data networks

This paper investigates the changes that are required in networking technology for ‘everything over IP’ to become a reality. Initially the changes that are taking place in the telecommunications industry are reviewed. This review ranges from a discussion about the companies installing massive global IP networks to the emergence of novel routeing technologies, e.g. multi-protocol label switching (MPLS) and terabit router technologies. The role of existing telecommunications operators is then discussed, along with the reasons why they are developing interworking and intelligence layers based on distributed computing principles to support all their networks — mobile, fixed, broadband and IP.     

Design of 2D FIR and IIR Digital Filters with Canonical Signed Digit Coefficients Using Singular Value Decomposition and Genetic Algorithms

In this paper a design approach for two-dimensional finite impulse and infinite impulse response digital filters with canonic signed digit (CSD) coefficients based on singular value decomposition and genetic algorithms (GAs) is presented. The proposed technique uses a new chromosome coding scheme to alleviate the problem encountered by many GAs during crossover and mutation which destroys the CSD property. Examples are given to demonstrate the usefulness of the proposed technique. A comparative study carried out with some of the existing techniques indicates the high throughput property of the proposed technique.     

Exploiting symmetries for scaling loopy belief propagation and relational training

Judging by the increasing impact of machine learning on large-scale data analysis in the last decade, one can anticipate a substantial growth in diversity of the machine learning applications for “big data” over the next decade. This exciting new opportunity, however, also raises many challenges. One of them is scaling inference within and training of graphical models. Typical ways to address this scaling issue are inference by approximate message passing, stochastic gradients, and MapReduce, among others. Often, we encounter inference and training problems with symmetries and redundancies in the graph structure. A prominent example are relational models that capture complexity. Exploiting these symmetries, however, has not been considered for scaling yet. In this paper, we show that inference and training can indeed benefit from exploiting symmetries. Specifically, we show that (loopy) belief propagation (BP) can be lifted. That is, a model is compressed by grouping nodes together that send and receive identical messages so that a modified BP running on the lifted graph yields the same marginals as BP on the original one, but often in a fraction of time. By establishing a link between lifting and radix sort, we show that lifting is MapReduce-able. Still, in many if not most situations training relational models will not benefit from this (scalable) lifting: symmetries within models easily break since variables become correlated by virtue of depending asymmetrically on evidence. An appealing idea for such situations is to train and recombine local models. This breaks long-range dependencies and allows to exploit lifting within and across the local training tasks. Moreover, it naturally paves the way for the first scalable lifted training approaches based on stochastic gradients, both in an online and a MapReduced fashion. On several datasets, the online training, for instance, converges to the same quality solution over an order of magnitude faster, simply because it starts optimizing long before having seen the entire mega-example even once.     

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.     

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).     

Robustification of input redundant feedback systems using robust actuator weighting in the control allocation problem

In this article, a new methodology for robust actuator weighting in the control allocation (CA) problem of input redundant feedback systems is addressed. The methodology is based on the control structural properties of the plant which were previously used for control configuration selection. Robust performance (RP) measures including H _∞ norm and structured singular value of the closed-loop system are used in this article. The capability of the approach is proven with application to lateral dynamics control of the vehicle over-actuated with front and rear steering systems. Employing the RP measures, it is concluded that the vehicle feedback control with front steering angles gives superior RP properties in comparison with the feedback loop of the rear steering angles. Based on these results, the penalty weightings in the cost function of the CA unit are determined. Simulation results based on nonlinear seven degrees of freedom vehicle handling model show that the selection of penalty weightings in the CA unit based on the RP properties of the control inputs (front and rear steering angles) improves the RP of the closed-loop.