Investigation of swelling and network parameters of poly (ethylene glycol)-crosslinked poly (methyl vinyl ether-co-maleic acid) hydrogels

he influence of poly(ethylene glycol) (PEG) plasticiser content and molecular weight on the physicochemical properties of films cast from aqueous blends of poly(methyl vinyl ether-co-maleic acid) was investigated using thermal analysis, swelling studies, scanning electron microscopy (SEM) and attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy. FTIR spectroscopy revealed a shift of the CO peak from 1708 to 1731 cm-1, indicating that an esterification reaction had occurred upon heating, thus producing crosslinked films. Higher molecular weight PEGs (10,000 and 1000 Da, respectively), having greater chain length, producing hydrogel networks with lower crosslink densities and higher average molecular weight between two consecutive crosslinks. Accordingly, such materials exhibited higher swelling rates. Hydrogels crosslinked with a low molecular weight PEG (PEG 200) showed rigid networks with high crosslink densities and, therefore, lower swelling rates. Polymer:plasticizer ratio alteration did not yield any discernable patterns, regardless of the method of analysis. The polymer–water interaction parameter (?) increased with increases in the crosslink density. SEM studies showed that porosity of the crosslinked films increased with increasing PEG MW, confirming what had been observed with swelling studies and thermal analysis, that the crosslink density must be decreased as the Mw of the crosslinker is increased. Hydrogels containing PMVE/MA/PEG 10,000 could be used for rapid delivery of drug, due to their low crosslink density. Moderately crosslinked PMVE/MA/PEG 1000 hydrogels or highly crosslinked PMVE/MA/PEG 200 systems could then be used in controlling the drug delivery rates. We are currently evaluating these systems, both alone and in combination, for use in sustained release drug delivery devices.

Investigation of swelling and network parameters of poly(ethylene glycol)-crosslinked poly(methyl vinyl ether-co-maleic acid) hydrogels

The influence of poly(ethylene glycol) (PEG) plasticiser content and molecular weight on the physicochemical properties of films cast from aqueous blends of poly(methyl vinyl ether-co-maleic acid) was investigated using thermal analysis, swelling studies, scanning electron microscopy (SEM) and attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy. FTIR spectroscopy revealed a shift of the C{double bond, long}O peak from 1708 to 1731 cm, indicating that an esterification reaction had occurred upon heating, thus producing crosslinked films. Higher molecular weight PEGs (10,000 and 1000 Da, respectively), having greater chain length, producing hydrogel networks with lower crosslink densities and higher average molecular weight between two consecutive crosslinks. Accordingly, such materials exhibited higher swelling rates. Hydrogels crosslinked with a low molecular weight PEG (PEG 200) showed rigid networks with high crosslink densities and, therefore, lower swelling rates. Polymer:plasticizer ratio alteration did not yield any discernable patterns, regardless of the method of analysis. The polymer-water interaction parameter (?) increased with increases in the crosslink density. SEM studies showed that porosity of the crosslinked films increased with increasing PEG MW, confirming what had been observed with swelling studies and thermal analysis, that the crosslink density must be decreased as the M of the crosslinker is increased. Hydrogels containing PMVE/MA/PEG 10,000 could be used for rapid delivery of drug, due to their low crosslink density. Moderately crosslinked PMVE/MA/PEG 1000 hydrogels or highly crosslinked PMVE/MA/PEG 200 systems could then be used in controlling the drug delivery rates. We are currently evaluating these systems, both alone and in combination, for use in sustained release drug delivery devices. © 2008 Elsevier Ltd. All rights reserved.

Influence of a pore-forming agent on swelling, network parameters, and permeability of poly(ethylene glycol)-crosslinked poly(methyl vinyl ether-co-maleic acid) hydrogels:Application in transdermal delivery systems

We characterized hydrogels, prepared from aqueous blends of poly(methyl vinyl ether-co-maleic acid) (PMVE/MA) and poly(ethylene glycol) (PEG 10,000 Daltons) containing a pore-forming agent (sodium bicarbonate, NaHCO ). Increase in NaHCO content increased the equilibrium water content (EWC) and average molecular weight between crosslinks (M ) of hydrogels. For example, the %EWC was 731, 860, 1109, and 7536% and the M was 8.26, 31.64, 30.04, and 3010.00 × 10 g/mol for hydrogels prepared from aqueous blends containing 0, 1, 2, and 5% w/w of NaHCO , respectively. Increase in NaHCO content also resulted in increased permeation of insulin. After 24 h, percentage permeation was 0.94, 3.68, and 25.71% across hydrogel membranes prepared from aqueous blends containing 0, 2, and 5% w/w of NaHCO , respectively. Hydrogels containing the pore-forming agent were fabricated into microneedles (MNs) for transdermal drug delivery applications by integrating the MNs with insulin-loaded patches. It was observed that the mean amount of insulin permeating across neonatal porcine skin in vitro was 20.62% and 52.48% from hydrogel MNs prepared from aqueous blends containing 0 and 5% w/w of NaHCO . We believe that these pore-forming hydrogels are likely to prove extremely useful for applications in transdermal drug delivery of biomolecules. © 2012 Wiley Periodicals, Inc.

Cognitive Single Carrier Systems: Joint Impact of Multiple Licensed Transceivers

In this paper, the impact of interference from multiple licensed transceivers on cognitive underlay single carrier systems is examined. Specifically, the situation is considered in which the secondary network is limited by three key parameters: 1) maximum transmit power at the secondary transmitter, 2) peak interference power at the primary receivers, and 3) interference power from the primary transmitters. For this cognitive underlay single carrier system, the signal-to-interference ratio (SIR) of the secondary network is obtained for transmission over frequency selective fading channels. Based on this, a new closedform expression for the cumulative distribution function of the SIR is evaluated, from which the outage probability and the ergodic capacity are derived. Further insights are established by analyzing the asymptotic outage probability and the asymptotic ergodic capacity in the high transmission power regime. In particular, it is corroborated that the asymptotic outage diversity gain is equal to the multipath gain of the frequency selective channel in the secondary network. The asymptotic ergodic capacity also gives new insight into the additional power cost for different network parameters while maintaining a specified target ergodic capacity. Illustrative numerical examples are presented to validate the outage probability and ergodic capacity under different interference power profiles.

Cognitive amplify-and-forward relay networks with beamforming under primary user power constraint over Nakagami-m fading channels

In this paper, we analyze the performance of cognitive amplify-and-forward (AF) relay networks with beamforming under the peak interference power constraint of the primary user (PU). We focus on the scenario that beamforming is applied at the multi-antenna secondary transmitter and receiver. Also, the secondary relay network operates in channel state information-assisted AF mode, and the signals undergo independent Nakagami-m fading. In particular, closed-form expressions for the outage probability and symbol error rate (SER) of the considered network over Nakagami-m fading are presented. More importantly, asymptotic closed-form expressions for the outage probability and SER are derived. These tractable closed-form expressions for the network performance readily enable us to evaluate and examine the impact of network parameters on the system performance. Specifically, the impact of the number of antennas, the fading severity parameters, the channel mean powers, and the peak interference power is addressed. The asymptotic analysis manifests that the peak interference power constraint imposed on the secondary relay network has no effect on the diversity gain. However, the coding gain is affected by the fading parameters of the links from the primary receiver to the secondary relay network

Improving parameter learning of Bayesian nets from incomplete data

This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The task is usually tackled by running the Expectation-Maximization (EM) algorithm several times in order to obtain a high log-likelihood estimate. We argue that choosing the maximum log-likelihood estimate (as well as the maximum penalized log-likelihood and the maximum a posteriori estimate) has severe drawbacks, being affected both by overfitting and model uncertainty. Two ideas are discussed to overcome these issues: a maximum entropy approach and a Bayesian model averaging approach. Both ideas can be easily applied on top of EM, while the entropy idea can be also implemented in a more sophisticated way, through a dedicated non-linear solver. A vast set of experiments shows that these ideas produce significantly better estimates and inferences than the traditional and widely used maximum (penalized) log-likelihood and maximum a posteriori estimates. In particular, if EM is adopted as optimization engine, the model averaging approach is the best performing one; its performance is matched by the entropy approach when implemented using the non-linear solver. The results suggest that the applicability of these ideas is immediate (they are easy to implement and to integrate in currently available inference engines) and that they constitute a better way to learn Bayesian network parameters.

Practical Spectrum Aggregation for Secondary Networks with Imperfect Sensing

We investigate a collision-sensitive secondary network that intends to opportunistically aggregate and utilize spectrum of a primary network to achieve higher data rates. In opportunistic spectrum access with imperfect sensing of idle primary spectrum, secondary transmission can collide with primary transmission. When the secondary network aggregates more channels in the presence of the imperfect sensing, collisions could occur more often, limiting the performance obtained by spectrum aggregation. In this context, we aim to address a fundamental query, that is, how much spectrum aggregation is worthy with imperfect sensing. For collision occurrence, we focus on two different types of collision: one is imposed by asynchronous transmission; and the other by imperfect spectrum sensing. The collision probability expression has been derived in closed-form with various secondary network parameters: primary traffic load, secondary user transmission parameters, spectrum sensing errors, and the number of aggregated sub-channels. In addition, the impact of spectrum aggregation on data rate is analysed under the constraint of collision probability. Then, we solve an optimal spectrum aggregation problem and propose the dynamic spectrum aggregation approach to increase the data rate subject to practical collision constraints. Our simulation results show clearly that the proposed approach outperforms the benchmark that passively aggregates sub-channels with lack of collision awareness.

Dynamic Modeling and Interaction of Hybrid Natural Gas and Electricity Supply System in Microgrid

Natural gas (NG) network and electric network are becoming tightly integrated by microturbines in the microgrid. Interactions between these two networks are not well captured by the traditional microturbine (MT) models. To address this issue, two improved models for single-shaft MT and split-shaft MT are proposed in this paper. In addition, dynamic models of the hybrid natural gas and electricity system (HGES) are developed for the analysis of their interactions. Dynamic behaviors of natural gas in pipes are described by partial differential equations (PDEs), while the electric network is described by differential algebraic equations (DAEs). So the overall network is a typical two-time scale dynamic system. Numerical studies indicate that the two-time scale algorithm is faster and can capture the interactions between the two networks. The results also show the HGES with a single-shaft MT is a weakly coupled system in which disturbances in the two networks mainly influence the dc link voltage of the MT, while the split-shaft MT is a strongly coupled system where the impact of an event will affect both networks.

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance. 

Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network

An artificial neural network (ANN) model is developed for the analysis and simulation of the correlation between the properties of maraging steels and composition, processing and working conditions. The input parameters of the model consist of alloy composition, processing parameters (including cold deformation degree, ageing temperature, and ageing time), and working temperature. The outputs of the ANN model include property parameters namely: ultimate tensile strength, yield strength, elongation, reduction in area, hardness, notched tensile strength, Charpy impact energy, fracture toughness, and martensitic transformation start temperature. Good performance of the ANN model is achieved. The model can be used to calculate properties of maraging steels as functions of alloy composition, processing parameters, and working condition. The combined influence of Co and Mo on the properties of maraging steels is simulated using the model. The results are in agreement with experimental data. Explanation of the calculated results from the metallurgical point of view is attempted. The model can be used as a guide for further alloy development.

Neural Network Modeling of Rheological Parameters of Grouts Containing Viscosity-Modifying Agent

The development of artificial neural network (ANN) models to predict the rheological behavior of grouts is described is this paper and the sensitivity of such parameters to the variation in mixture ingredients is also evaluated. The input parameters of the neural network were the mixture ingredients influencing the rheological behavior of grouts, namely the cement content, fly ash, ground-granulated blast-furnace slag, limestone powder, silica fume, water-binder ratio (w/b), high-range water-reducing admixture, and viscosity-modifying agent (welan gum). The six outputs of the ANN models were the mini-slump, the apparent viscosity at low shear, and the yield stress and plastic viscosity values of the Bingham and modified Bingham models, respectively. The model is based on a multi-layer feed-forward neural network. The details of the proposed ANN with its architecture, training, and validation are presented in this paper. A database of 186 mixtures from eight different studies was developed to train and test the ANN model. The effectiveness of the trained ANN model is evaluated by comparing its responses with the experimental data that were used in the training process. The results show that the ANN model can accurately predict the mini-slump, the apparent viscosity at low shear, the yield stress, and the plastic viscosity values of the Bingham and modified Bingham models of the pseudo-plastic grouts used in the training process. The results can also predict these properties of new mixtures within the practical range of the input variables used in the training with an absolute error of 2%, 0.5%, 8%, 4%, 2%, and 1.6%, respectively. The sensitivity of the ANN model showed that the trend data obtained by the models were in good agreement with the actual experimental results, demonstrating the effect of mixture ingredients on fluidity and the rheological parameters with both the Bingham and modified Bingham models.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.