Lipase-catalyzed hydrolysis of canola oil in supercritical carbon dioxide

The effect of pressure, temperature, and CO₂ flow rale on the extent of conversion and the product composition in the enzyme-catalyzed hydrolysis of canola oil in supercritical carbon dioxide (SCCO₂) was investigated using lipase from Mucor miehei immobilized on macroporous anionic resin (Lipozyme IM). Reactions were carried out in a continuous flow reactor at 10, 24, and 38 MPa and 35 and 55°C. Supercritical fluid chromatography was used to analyze the reaction products. A conversion of 63–67% (triglyceride disappearance) was obtained at 24–38 MPa. Mono-and diglyceride production was minimum at 10 MPa and 35°C. Monoglyceride production was favored at 24 MPa. The amount of product obtained was higher at 24–38 MPa due to enhanced solubility in SCCO₂. Complete hydrolysis of oil should be possible by increasing the enzyme load and/or decreasing the quantity of the oil substrate. There was a drop in triglyceride conversion over a 24-h reaction time at 38 MPa and 55°C, which may be an indication of loss of enzyme activity. Pressure, temperature, and CO₂ flow rate are important parameters to be optimized in the enzyme-catalyzed hydrolysis of canola oil in SCCO₂ to maximize its conversion to high-value products.     

Wide-end fibers and their adhesion performance in biological attachment systems

Certain small creeping animals and insects generate the necessary adhesion force through the hairs of their feet. In addition to size, the shape of the fibrillar structures dramatically affects adhesion performance. One must develop a mathematical model to represent the relationship between the characteristics of fibers, surface roughness and adhesion quality. This paper provides a mathematical model and shows that wide-end fibrillar systems (WFS) are more compatible with variation in surface roughness. These systems adhere better than conventional systems with simple cylindrical fibers. The optimum geometry of fibers for maximum adhesion is discussed in this article. The obtained results are in good agreement with biological studies and are applicable to guide experimentation to achieve optimum artificial contacts.     

Region-based scalable self-recovery for salient-object images

Self-recovery is a tamper-detection and image recovery methods based on data hiding. It generates two types of data and embeds them into the original image: authentication data for tamper detection and reference data for image recovery. In this paper, a region-based scalable self-recovery (RSS) method is proposed for salient-object images. As the images consist of two main regions, the region of interest (ROI) and the region of non-interest (RONI), the proposed method is aimed at achieving higher reconstruction quality for the ROI. Moreover, tamper tolerability is improved by using scalable recovery. In the RSS method, separate reference data are generated for the ROI and RONI. Initially, two compressed bitstreams at different rates are generated using the embedded zero-block coding source encoder. Subsequently, each bitstream is divided into several parts, which are protected through various redundancy rates, using the Reed-Solomon channel encoder. The proposed method is tested on 10 000 salient-object images from the MSRA database. The results show that the RSS method, compared to related methods, improves reconstruction quality and tamper tolerability by approximately 30% and 15%, respectively.     

Modelling of LDMOS transistor using artificial neural networks: DC and RF performances

This article presents an accurate method based on artificial neural networks (ANNs) for DC and RF modelling of laterally diffused metal oxide semiconductor (LDMOS) transistors, under various temperature conditions. In LDMOS transistors, temperature is an effective factor, so the proposed models include this parameter. Two neural networks‐based procedures have been proposed for LDMOS transistor modelling, first for DC and second for RF modelling. In each case, two kinds of neural networks have been used, multilayer perceptron and radial basis function neural networks. Two models are compared to each other in terms of accuracy, and for both of them, an excellent agreement between modelled and measured data is obtained. The ANN model is developed and trained with the help of data obtained by simulation of a Si‐LDMOS transistor using ADS software.     

Copolymer-Templated Nickel Oxide for High-Efficiency Mesoscopic Perovskite Solar Cells in Inverted Architecture

Despite the outstanding role of mesoscopic structures on the efficiency and stability of perovskite solar cells (PSCs) in the regular (n–i–p) architecture, mesoscopic PSCs in inverted (p–i–n) architecture have rarely been reported. Herein, an efficient and stable mesoscopic NiO_x (mp-NiO_x) scaffold formed via a simple and low-cost triblock copolymer template-assisted strategy is employed, and this mp-NiO_x film is utilized as a hole transport layer (HTL) in PSCs, for the first time. Promisingly, this approach allows the fabrication of homogenous, crack-free, and robust 150 nm thick mp-NiO_x HTLs through a facile chemical approach. Such a high-quality templated mp-NiO_x structure promotes the growth of the perovskite film yielding better surface coverage and enlarged grains. These desired structural and morphological features effectively translate into improved charge extraction, accelerated charge transportation, and suppressed trap-assisted recombination. Ultimately, a considerable efficiency of 20.2% is achieved with negligible hysteresis which is among the highest efficiencies for mp-NiO_x based inverted PSCs so far. Moreover, mesoscopic devices indicate higher long-term stability under ambient conditions compared to planar devices. Overall, these results may set new benchmarks in terms of performance for mesoscopic inverted PSCs employing templated mp-NiO_x films as highly efficient, stable, and easy fabricated HTLs.     

An Adaptive Multitaper-SVD Spectrum Sensing Method for OFDM-Based Cognitive Radio Systems

Cognitive radio (CR) has been proposed and widely investigated as an approach for increasing the spectrum efficiency. CR devices exploit so called white spaces in the spectrum allocated to the primary users (PU) by a process commonly referred to as the spectrum sensing. In this work, we use the singular value decomposition (SVD) for spectrum sensing in orthogonal frequency division multiplexing-based (OFDM) CR systems. A single input multiple output channel is assumed between the PU and the secondary user equipped with multiple antennas. At the CR side, the multitaper method (MTM) is used for the spectrum sensing in each antenna. As a first contribution, we aim at reducing the time necessary to perform spectrum sensing. To this end, we propose an adaptive MTM–SVD spectrum sensing method that decreases the sensing time. As a second contribution, we formulate a three dimensional SVD (referred to as 3-D SVD) scheme that efficiently processes signals and quantities related to multiple antenna traffic, OFDM multiple blocks and different tapers, simultaneously. Simulation results indicate that the proposed adaptive MTM–SVD decreases the sensing time by about 61–69 % for various proposed adaptive algorithms, compared to the conventional MTM–SVD method. Besides, performance improvement in probability of detection is achieved from 2–13 % for a predefined probability of false alarm by using adaptive MTM–SVD. In addition to further reduction of the sensing time, the proposed 3D-MTM–SVD outperforms conventional methods for the low probability of the false alarm.     

Simultaneous vector-valued image segmentation and intensity nonuniformity correction using variational level set combined with Markov random field modeling

Object quantification requires an image segmentation to make measurements about size, material composition and morphology of the object. In vector-valued or multispectral images, each image channel has its signal characteristics and provides special information that may improve the results of image segmentation method. This paper presents a region-based active contour model for vector-valued image segmentation with a variational level set formulation. In this model, the local image intensities are characterized using Gaussian distributions with different means and variances. Furthermore, by utilizing Markov random field, the spatial correlation between neighboring pixels and voxels is modeled. With incorporation of intensity nonuniformity model, our method is able to deal with brain tissue segmentation from multispectral magnetic resonance (MR) images. Our experiments on synthetic images and multispectral cerebral MR images with different noise and bias level show the advantages of the proposed method.     

Kinetics of sulfuric acid leaching of cadmium from Cd-Ni zinc plant residues

Cd-Ni filtercakes are produced continuously at the third purification step in the electrolytic production of zinc in the National Iranian Lead and Zinc Company (NILZ) in northwestern Iran. In this research, the dissolution kinetics of cadmium from Cd-Ni residues produced in NILZ plant has been investigated. Hence, the effects of temperature, sulfuric acid concentration, particle size and stirring speed on the kinetics of cadmium dissolution in sulfuric acid were studied. The dissolution kinetics at 25-55 degrees C and t相似文献