Dual Drug Delivery Microcapsules via Layer-by-Layer Self-Assembly

The integration of hydrophobic and hydrophilic drugs in the polymer microcapsule offers the possibility of developing a new drug delivery system that combines the best features of these two distinct classes of material. Recently, we have reported the encapsulation of an uncharged water-insoluble drug in the polymer membrane. The hydrophobic drug is deposited using a layer-by-layer (LbL) technique, which is based on the sequential adsorption of oppositely charged polyelectrolytes onto a charged substrate. In this paper, we report the encapsulation of two different drugs, which are invariably different in structure and in their solubility in water. We have characterized these dual drug vehicular capsules by confocal laser scanning microscopy, atomic force microscopy, visible microscopy, and transmission electron microscopy. The growth of a thin film on a flat substrate by LbL was monitored by UV−vis spectra. The desorption kinetics of two drugs from the thin film was modeled by a second-order rate model.

Effect of static deflection on natural frequency of non-linear spring mass system by direct linearization method

An exact expression for the frequency of a non-linear cubic spring mass system is obtained considering the effect of static deflection. An alternative expression for the approximate frequency is also obtained by the direct linearization procedure; it is shown that this is very accurate as compared with the exact method. This approximate frequency equation is used to explain a “dual behaviour” of the frequency amplitude curves.

Comparison of Multiclass SVM Classification Methods to Use in a Supportive System for Distance Relay Coordination

This paper aims at evaluating the methods of multiclass support vector machines (SVMs) for effective use in distance relay coordination. Also, it describes a strategy of supportive systems to aid the conventional protection philosophy in combating situations where protection systems have maloperated and/or information is missing and provide selective and secure coordinations. SVMs have considerable potential as zone classifiers of distance relay coordination. This typically requires a multiclass SVM classifier to effectively analyze/build the underlying concept between reach of different zones and the apparent impedance trajectory during fault. Several methods have been proposed for multiclass classification where typically several binary SVM classifiers are combined together. Some authors have extended binary SVM classification to one-step single optimization operation considering all classes at once. In this paper, one-step multiclass classification, one-against-all, and one-against-one multiclass methods are compared for their performance with respect to accuracy, number of iterations, number of support vectors, training, and testing time. The performance analysis of these three methods is presented on three data sets belonging to training and testing patterns of three supportive systems for a region and part of a network, which is an equivalent 526-bus system of the practical Indian Western grid.

Dielectric Properties of Epoxy-Al2O3 Nanocomposite System for Packaging Applications

In recent times, there has been an ever-growing need for polymer-based multifunctional materials for electronic packaging applications. In this direction, epoxy-Al2O3 nanocomposites at low filler loadings can provide an excellent material option, especially from the point of view of their dielectric properties. This paper reports the dielectric characteristics for such a system, results of which are observed to be interesting, unique, and advantageous as compared to traditionally used microcomposite systems. Nanocomposites are found to display lower values of permittivity/tan delta over a wide frequency range as compared to that of unfilled epoxy. This surprising observation has been attributed to the interaction between the epoxy chains and the nanoparticles, and in this paper this phenomenon is analyzed using a dual layer interface model reported for polymer nanocomposites. As for the other dielectric properties associated with the nanocomposites, the nano-filler loading seems to have a significant effect. The dc resistivity and ac dielectric strength of the nanocomposites were observed to be lower than that of the unfilled epoxy system at the investigated filler loadings, whereas the electrical discharge resistant properties showed a significant enhancement. Further analysis of the results obtained in this paper shows that the morphology of the interface region and its characteristics decide the observed interesting dielectric behaviors.

Defining the pathway to insulin-like growth factor system targeting in cancer

The insulin-like growth factors (IGEs; IGF-1 and IGF-2) play central roles in cell growth, differentiation, survival, transformation and metastasis. The biologic effects of the IGFs are mediated by the IGF-1 receptor (IGF-1R), a receptor tyrosine kinase with homology to the insulin receptor (IR). Dysregulation of the ICE system is well recognized as a key contributor to the progression of multiple cancers, with IGF-1R activation increasing the tumorigenic potential of breast, prostate, lung, colon and head and neck squamous cell carcinoma (HNSCC). Despite this relationship, targeting the IGF-1R has only recently undergone development as a molecular cancer therapeutic. As it has taken hold, we are witnessing a robust increase and interest in targeting the inhibition of IGF-1R signaling. This is accentuated by the list of over 30 drugs, including monoclonal antibodies (mAbs) and tyrosine kinase inhibitors (TKIs) that are under evaluation as single agents or in combination therapies 1]. The ICE-binding proteins (IGFBPs) represent the third component of the ICE system consisting of a class of six soluble secretory proteins. They represent a unique class of naturally occurring ICE-antagonists that bind to and sequester IGF-1 and IGF-2, inhibiting their access to the IGF-1R. Due to their dual targeting of the IGFs without affecting insulin action, the IGFBPs are an untapped ``third'' class of IGF-1R inhibitors. in this commentary, we highlight some of the significant aspects of and prospects for targeting the IGF-1R and describe what the future may hold. (C) 2010 Elsevier Inc. All rights reserved.

Estimation of BT Interference in OFDM Sub Carriers with Training Signals

While wireless LAN (WLAN) is very popular now a days, its performance deteriorates in the presence of other signals like Bluetooth (BT) signal that operate in the same band as WLAN. Present interference mitigation techniques in WLAN due to BT cancel interference in WLAN sub carrier where BT has hopped but do not cancel interference in the adjacent sub carriers. In this paper BT interference signal in all the OFDM sub carriers is estimated. That is, leakage of BT in other sub carriers including the sub carriers in which it has hopped is also measured. BT signals are estimated using the training signals of OFDM system. Simulation results in AWGN noise show that proposed algorithm agrees closely with theoretical results.

Classical screw theory: A fresh look using dual eigenvalue approach

This paper presents a novel algebraic formulation of the central problem of screw theory, namely the determination of the principal screws of a given system. Using the algebra of dual numbers, it shows that the principal screws can be determined via the solution of a generalised eigenproblem of two real, symmetric matrices. This approach allows the study of the principal screws of the general screw systems associated with a manipulator of arbitrary geometry in terms of closed-form expressions of its architecture and configuration parameters. The formulation is illustrated with examples of practical manipulators.

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static VAr compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static Var compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system.

On-Chip Memory Architecture Exploration Framework for DSP Processor-Based Embedded System on Chip

Today's SoCs are complex designs with multiple embedded processors, memory subsystems, and application specific peripherals. The memory architecture of embedded SoCs strongly influences the power and performance of the entire system. Further, the memory subsystem constitutes a major part (typically up to 70%) of the silicon area for the current day SoC. In this article, we address the on-chip memory architecture exploration for DSP processors which are organized as multiple memory banks, where banks can be single/dual ported with non-uniform bank sizes. In this paper we propose two different methods for physical memory architecture exploration and identify the strengths and applicability of these methods in a systematic way. Both methods address the memory architecture exploration for a given target application by considering the application's data access characteristics and generates a set of Pareto-optimal design points that are interesting from a power, performance and VLSI area perspective. To the best of our knowledge, this is the first comprehensive work on memory space exploration at physical memory level that integrates data layout and memory exploration to address the system objectives from both hardware design and application software development perspective. Further we propose an automatic framework that explores the design space identifying 100's of Pareto-optimal design points within a few hours of running on a standard desktop configuration.

A sequential dual method for structural SVMs

In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to compu- tational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms. We consider the problem of structured classifi- cation. In the last few years, large margin classifiers like sup-port vector machines (SVMs) have shown much promise for structured output learning. The related optimization prob -lem is a convex quadratic program (QP) with a large num-ber of constraints, which makes the problem intractable for large data sets. This paper proposes a fast sequential dual method (SDM) for structural SVMs. The method makes re-peated passes over the training set and optimizes the dual variables associated with one example at a time. The use of additional heuristics makes the proposed method more efficient. We present an extensive empirical evaluation of the proposed method on several sequence learning problems.Our experiments on large data sets demonstrate that the proposed method is an order of magnitude faster than state of the art methods like cutting-plane method and stochastic gradient descent method (SGD). Further, SDM reaches steady state generalization performance faster than the SGD method. The proposed SDM is thus a useful alternative for large scale structured output learning.

Room temperature synthesis of Mn2+ doped ZnS d-dots and observation of tunable dual emission: Effects of doping concentration, temperature, and ultraviolet light illumination

Mn2+ doped (0-50.0 molar %) ZnS d-dots have been synthesized in water medium by using an environment friendly low cost chemical technique. Tunable dual emission in UV and yellow-orange regions is achieved by tailoring the Mn2+ doping concentration in the host ZnS nanocrystal. The optimum doping concentration for achieving efficient photoluminescence (PL) emission is determined to be similar to 1.10 (at. %) corresponding to 40.0 (molar %) of Mn2+ doping concentration used during synthesis. The mechanism of charge transfer from the host to the dopant leading to the intensity modulated tunable (594-610 nm) yellow-orange PL emission is straightforwardly understood as no capping agent is used. The temperature dependent PL emission measurements are carried out, viz., in 1.10 at. % Mn2+ doped sample and the experimental results are explained by using a theoretical PL emission model. It is found that the ratio of non-radiative to radiative recombination rates is temperature dependent and this phenomenon has not been reported, so far, in Mn2+ doped ZnS system. The colour tuning of the emitted light from the samples are evident from the calculated chromaticity coordinates. UV light irradiation for 150 min in 40.0 (molar %) Mn2+ doped sample shows an enhancement of 33% in PL emission intensity. (C) 2013 American Institute of Physics. http://dx.doi.org/10.1063/1.4795779]

Channel training signal design for reciprocal multiple antenna systems with beamforming

Fast and efficient channel estimation is key to achieving high data rate performance in mobile and vehicular communication systems, where the channel is fast time-varying. To this end, this work proposes and optimizes channel-dependent training schemes for reciprocal Multiple-Input Multiple-Output (MIMO) channels with beamforming (BF) at the transmitter and receiver. First, assuming that Channel State Information (CSI) is available at the receiver, a channel-dependent Reverse Channel Training (RCT) signal is proposed that enables efficient estimation of the BF vector at the transmitter with a minimum training duration of only one symbol. In contrast, conventional orthogonal training requires a minimum training duration equal to the number of receive antennas. A tight approximation to the capacity lower bound on the system is derived, which is used as a performance metric to optimize the parameters of the RCT. Next, assuming that CSI is available at the transmitter, a channel-dependent forward-link training signal is proposed and its power and duration are optimized with respect to an approximate capacity lower bound. Monte Carlo simulations illustrate the significant performance improvement offered by the proposed channel-dependent training schemes over the existing channel-agnostic orthogonal training schemes.

Data Fusion of Dual Foot-Mounted INS to Reduce the Systematic Heading Drift

In this report, we investigate the problem of applying a range constraint in order to reduce the systematic heading drift in a foot-mounted inertial navigation system (INS) (motion-tracking). We make use of two foot-mounted INS, one on each foot, which are aided with zero-velocity detectors. A novel algorithm is proposed in order to reduce the systematic heading drift. The proposed algorithm is based on the idea that the separation between the two feet at any given instance must always lie within a sphere of radius equal to the maximum possible spatial separation between the two feet. A Kalman filter, getting one measurement update and two observation updates is used in this algorithm.

Channel estimation at the transmitter in a reciprocal MIMO spatial multiplexing system

This paper considers the problem of channel estimation at the transmitter in a spatial multiplexing-based Time Division Duplex (TDD) Multiple Input Multiple Output (MIMO) system with perfect CSIR. A novel channel-dependent Reverse Channel Training (RCT) sequence is proposed, using which the transmitter estimates the beamforming vectors for forward link data transmission. This training sequence is designed based on the following two metrics: (i) a capacity lower bound, and (ii) the mean square error in the estimate. The performance of the proposed training scheme is analyzed and is shown to significantly outperform the conventional orthogonal RCT sequence. Also, in the case where the transmitter uses water-filling power allocation for data transmission, a novel RCT sequence is proposed and optimized with respect to the MSE in estimating the transmit covariance matrix.