Mechanical,thermomechanical, and morphology of PVC/PBMA blends and full IPNs

Blends and full IPNs' of poly(vinyl chloride) and polybutylmethacrylate (PBMA) have been synthesized and characterized with respect to their mechanical, thermomechanical, and morphological properties. Both the systems displayed a rise in the modulus and ultimate tensile strength and a consequent decreasing tendency of elongation at break and toughness are exhibited. The influence of crosslinking of the two polymers as has been done in case of full IPNs over the ordinary blends is quite well understood from these properties. The thermomechanical analysis revealed a substantial rise in stability with increasing methacrylate concentration in the system and this is quite apparent from the softening characteristics of the different samples under study. The biphasic cocontinuous systems as explicit from the morphological studies support phase mixing at the initial stages, with subsequent phasing out tendency with increasing percentage of PBMA incorporation. The thermomechanical parameters are in conformity to their mechanicals which have been further supported by their morphological studies. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci, 2006     

Particle size studies of supported metal catalysts: a comparative study by X-ray diffraction,EXAFS and electron microscopy

Supported ruthenium and iridium metal catalysts are studied by X-ray diffraction (XRD), EXAFS analysis and transmission electron microscopy (TEM). Estimates of the mean particle size from these techniques range from 20 to 25 Å from XRD, 13 to 16 Å from EXAFS and 25 to 32 Å from TEM. The strengths and weaknesses of these instrumental methods are discussed, as is the intrinsic comparability of these techniques. From a combination of these methods, the average particle size is estimated to be of the order of 20–30 Å.     

A novel pulse swallow based frequency divider circuit for a phase-locked loops

In this paper, an analysis of the memory effect in two amplifier-shared switched-capacitor integrators for a discrete-time sigma-delta (\(\varSigma \varDelta\)) modulator is presented. Interaction between the integrators is modeled by feeding an integrator output voltage to another integrator input and vice versa and multiplying by a coefficient depending on DC gain and input parasitic capacitance of the opamp. The model is applied to a second-order \(\varSigma \varDelta\) modulator to analyze how signal and noise transfer functions are altered. The analysis reveals that the magnitude response of the signal transfer function is minimally affected in the low-frequency signal band, whereas that of the noise transfer function can be increased significantly in the signal band, degrading the effectiveness of noise shaping. In relation to the parasitic capacitance at the opamp input, the DC gain required of the opamp is derived quantitatively for a given degradation of modulator dynamic range with respect to different oversampling ratios. Considering leaky integration, which is also caused by the finite opamp DC gain, the DC gain requirement imposed by the memory effect is proved to be more severe than that by leaky integration. Macromodel-based circuit simulation results confirm the accuracy of the proposed model and equations.     

Synthesis and Biological Evaluation of Ionizable Lipid Materials for the In Vivo Delivery of Messenger RNA to B Lymphocytes

B lymphocytes regulate several aspects of immunity including antibody production, cytokine secretion, and T‐cell activation; moreover, B cell misregulation is implicated in autoimmune disorders and cancers such as multiple sclerosis and non‐Hodgkin's lymphomas. The delivery of messenger RNA (mRNA) into B cells can be used to modulate and study these biological functions by means of inducing functional protein expression in a dose‐dependent and time‐controlled manner. However, current in vivo mRNA delivery systems fail to transfect B lymphocytes and instead primarily target hepatocytes and dendritic cells. Here, the design, synthesis, and biological evaluation of a lipid nanoparticle (LNP) system that can encapsulate mRNA, navigate to the spleen, transfect B lymphocytes, and induce more than 60 pg of protein expression per million B cells within the spleen is described. Importantly, this LNP induces more than 85% of total protein production in the spleen, despite LNPs being observed transiently in the liver and other organs. These results demonstrate that LNP composition alone can be used to modulate the site of protein induction in vivo, highlighting the critical importance of designing and synthesizing new nanomaterials for nucleic acid delivery.     

Structural,morphological, optical and gas sensing properties of pure and Ce doped SnO<Subscript>2</Subscript> thin films prepared by jet nebulizer spray pyrolysis (JNSP) technique

Pure and cerium (Ce) doped tin oxide (SnO₂) thin films are prepared on glass substrates by jet nebulizer spray pyrolysis technique at 450 °C. The synthesized films are characterized by X-ray diffraction (XRD), scanning electron microscopy, energy dispersive analysis X-ray, ultra violet visible spectrometer (UV–Vis) and stylus profilometer. Crystalline structure, crystallite size, lattice parameters, texture coefficient and stacking fault of the SnO₂ thin films have been determined using X-ray diffractometer. The XRD results indicate that the films are grown with (110) plane preferred orientation. The surface morphology, elemental analysis and film thickness of the SnO₂ films are analyzed and discussed. Optical band gap energy are calculated with transmittance data obtained from UV–Visible spectra. Optical characterization reveals that the band gap energy is found decreased from 3.49 to 2.68 eV. Pure and Ce doped SnO₂ thin film gas sensors are fabricated and their gas sensing properties are tested for various gases maintained at different temperature between 150 and 250 °C. The 10 wt% Ce doped SnO₂ sensor shows good selectivity towards ethanol (at operating temperature 250 °C). The influence of Ce concentration and operating temperature on the sensor performance is discussed. The better sensing ability for ethanol is observed compared with methanol, acetone, ammonia, and 2-methoxy ethanol gases.     

Exergy efficiency improvement in hydrogen production process by recovery of chemical energy versus thermal energy

Exergy analysis is recently being employed as one of the preferred methods to improve the design performance of a system and to achieve overall sustainability. Exergy is mainly composed of physical or thermo-mechanical and chemical components and a single stream can possess one or more forms of exergy. Where there is exergy lost in unused chemical streams or wasted energy, the recovery of exergy would reduce losses and increase the second law efficiency of the process. In many chemical process plants such as hydrogen (H₂), ammonia, nitric acid, etc., there is a potential to recover waste or excess heat by process heat exchange or by generating utilities. For a process like steam–methane (CH₄) reforming (SMR), exergy efficiency can be improved by recovering the available excess heat partially or fully in the form of chemical energy or thermal energy. This paper presents the generalised system analysis to show that the recovery of exergy in the form of chemical energy is better than in thermal energy form due to fewer losses and higher efficiency. The concept is illustrated with the example of a simple combustion system with excess heat in which saving fuel proves to be more exergy efficient than generating utility. The approach is applied to an industrial case study of H₂-producing SMR plant with two modified cases of steam generation and recycling portion of unconverted CH₄ as feed. In the case study, heat exchanger network is treated as a separate process component and a simple methodology is proposed to calculate the exergy losses for the same. The results of the case study prove that the recovery of chemical energy is more efficient than that of thermal energy from an exergy perspective.     

An investigation on the degradation of different features extracted from the compressed American English speech using narrowband and wideband codecs

Speech coding facilitates speech compression without perceptual loss that results in the elimination or deterioration of both speech and speaker specific features used for a wide range of applications like automatic speaker and speech recognition, biometric authentication, prosody evaluations etc. The present work investigates the effect of speech coding in the quality of features which include Mel Frequency Cepstral Coefficients, Gammatone Frequency Cepstral Coefficients, Power-Normalized Cepstral Coefficients, Perceptual Linear Prediction Cepstral Coefficients, Rasta-Perceptual Linear Prediction Cepstral Coefficients, Residue Cepstrum Coefficients and Linear Predictive Coding-derived cepstral coefficients extracted from codec compressed speech. The codecs selected for this study are G.711, G.729, G.722.2, Enhanced Voice Services, Mixed Excitation Linear Prediction and also three codecs based on compressive sensing frame work. The analysis also includes the variation in the quality of extracted features with various bit-rates supported by Enhanced Voice Services, G.722.2 and compressive sensing codecs. The quality analysis of extracted epochs, fundamental frequency and formants estimated from codec compressed speech was also performed here. In the case of various features extracted from the output of selected codecs, the variation introduced by Mixed Excitation Linear Prediction codec is the least due to its unique method for the representation of excitation. In the case of compressive sensing based codecs, there is a drastic improvement in the quality of extracted features with the augmentation of bit rate due to the waveform type coding used in compressive sensing based codecs. For the most popular Code Excited Linear Prediction codec based on Analysis-by-Synthesis coding paradigm, the impact of Linear Predictive Coding order in feature extraction is investigated. There is an improvement in the quality of extracted features with the order of linear prediction and the optimum performance is obtained for Linear Predictive Coding order between 20 and 30, and this varies with gender and statistical characteristics of speech. Even though the basic motive of a codec is to compress single voice source, the performance of codecs in multi speaker environment is also studied, which is the most common environment in majority of the speech processing applications. Here, the multi speaker environment with two speakers is considered and there is an augmentation in the quality of individual speeches with increase in diversity of mixtures that are passed through codecs. The perceptual quality of individual speeches extracted from the codec compressed speech is almost same for both Mixed Excitation Linear Prediction and Enhanced Voice Services codecs but regarding the preservation of features, the Mixed Excitation Linear Prediction codec has shown a superior performance over Enhanced Voice Services codec.     

Robust Boosted Parameter Based Combined Classifier for Rotation Invariant Texture Classification

Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. This article presents a robust hybrid combination technique to build a combined classifier that is able to tackle the problem of classification of rotation-invariant 2D textures. Diversity in the components of the combined classifier is enforced through variation of the parameters related to both architecture design and training stages of a neural network classifier. The boosting algorithm is used to make perturbation of the training set using Multi-Layer Perceptron (MLP) as the base classifier. The final decision of the proposed combined classifier is based on the majority voting. Experiments’ results on a standard benchmark database of rotated textures show that the proposed hybrid combination method is very robust, and it presents an excellent texture discrimination for all considered classes, overcoming traditional texture modification methods.     

Reliable pose estimation of underwater dock using single camera: a scene invariant approach

It is well known that docking of Autonomous Underwater Vehicle (AUV) provides scope to perform long duration deep-sea exploration. A large amount of literature is available on vision-based docking which exploit mechanical design, colored markers to estimate the pose of a docking station. In this work, we propose a method to estimate the relative pose of a circular-shaped docking station (arranged with LED lights on periphery) up to five degrees of freedom (5-DOF, neglecting roll effect). Generally, extraction of light markers from underwater images is based on fixed/adaptive choice of threshold, followed by mass moment-based computation of individual markers as well as center of the dock. Novelty of our work is the proposed highly effective scene invariant histogram-based adaptive thresholding scheme (HATS) which reliably extracts positions of light sources seen in active marker images. As the perspective projection of a circle features a family of ellipses, we then fit an appropriate ellipse for the markers and subsequently use the ellipse parameters to estimate the pose of a circular docking station with the help of a well-known method in Safaee-Rad et al. (IEEE Trans Robot Autom 8(5):624–640, 1992). We analyze the effectiveness of HATS as well as proposed approach through simulations and experimentation. We also compare performance of targeted curvature-based pose estimation with a non-iterative efficient perspective-n-point (EPnP) method. The paper ends with a few interesting remarks on vantages with ellipse fitting for markers and utility of proposed method in case of non-detection of all the light markers.     

Two-dimensional Laplacianfaces method for face recognition

In this paper we propose a two-dimensional (2D) Laplacianfaces method for face recognition. The new algorithm is developed based on two techniques, i.e., locality preserved embedding and image based projection. The 2D Laplacianfaces method is not only computationally more efficient but also more accurate than the one-dimensional (1D) Laplacianfaces method in extracting the facial features for human face authentication. Extensive experiments are performed to test and evaluate the new algorithm using the FERET and the AR face databases. The experimental results indicate that the 2D Laplacianfaces method significantly outperforms the existing 2D Eigenfaces, the 2D Fisherfaces and the 1D Laplacianfaces methods under various experimental conditions.