Soft chemistry preparation of layered Al0.3V2O5·5H2O

A novel layered compound Al_0.3V₂O₅·5H₂O was successfully prepared and characterized using powder X-ray diffraction, Thermal analysis, scanning and transmission electron microscopes. The new layered Al_0.3V₂O₅·5H₂O has like-nanowires shape and the layered structure was stable until 400 °C. At higher temperature (500 °C), it decomposes to orthorhombic Al_xV₂O₅ and triclinic AlVO₄.     

A crystal-tolerant fully integrated CMOS low-IF dual-band GPS receiver

This paper presents the design of a dual-band L1/L2 GPS receiver, that can be easily integrated in portable devices (mainly GSM mobile phones). For the ease of integration with GSM wireless systems the receiver can tolerate most of the common GSM crystals, besides the GPS crystals, this will eliminate the need to use another crystal on board. A new frequency plan is presented to satisfy this requirement. A low-IF receiver architecture is used for dual-band operation with analog on-chip image rejection. The receiver is composed of a narrow-band LNA for each band, dual down-conversion mixers, a variable-gain channel filter, a 2-bit analog-to-digital converter, and a fully integrated frequency synthesizer including an on-chip VCO and loop filter. The complex filter can accept IF frequency variation of 10% around 4.092 MHz which allows the use of the commonly used 10/13/26 MHz GSM crystals and all the GPS crystals. The synthesizer generates the LO signals for both L1/L2 bands with an average phase noise of −95 dBc/Hz. The receiver exhibits maximum gain of 112 and 115 dB, noise figures of 4 and 3.6 dB, and input compression points of −76 and −79 dBm for L1 and L2, respectively. An on-chip variable-gain channel filter provides IF image rejection greater than 25 dB and gain control range over 80 dB. The receiver is designed in 0.13 μm CMOS technology and consumes 18 mW from a 1.2-V supply.     

Rosemary (<Emphasis Type="Italic">Rosmarinus officinalis</Emphasis>) oil: composition and functionality of the cold-pressed extract

The aim of this work was to investigate cold-pressed rosemary oil (RO) for its lipid classes, subclasses, fatty acid composition, tocochromanols and total phenolics amount. Antiradical activity against 1,1-diphenyl-2-picrylhydrazyl (DPPH^?) and galvinoxyl radicals, antioxidant activity, as measured by the Rancimat test, as well as antimicrobial activity against food-borne bacteria, and dermatophytic fungi of RO were evaluated. In RO, the amount of neutral lipids was highest (ca. 86%), followed by phospholipids (0.92%) and glycolipids (0.88%). The percentages of monounsaturated, polyunsaturated, and saturated fatty acids were 41.7, 42.3, and 15.8%, respectively. Linoleic acid (41.7%) and oleic acid (41.2%) were the major fatty acids while linolenic acid accounted for 1.3% of total fatty acids. The following tocochromanols were detected: α-, β-, γ- and δ-tocopherols, which accounted for 291, 22, 1145, and 41 mg/100 g oil, respectively, as well as α-, β-, γ- and δ-tocotrienols, which accounted for 18, 12, 29, and 158 mg/100 g oil, respectively. RO also contained high levels of phenolic compounds (7.2 mg GAE/g). After 60 min of reaction with free radicals, 67% of DPPH^? and 55% of galvinoxyl radicals were quenched by RO. Rancimat test showed that blending RO with sunflower oil increased the induction period (IP) for blends. The IP of the RO: sunflower oil blend (1:9, v/v) was 390 min, and RO: sunflower oil blend (2:8, v/v) was longer (540 min). RO exhibited high antimicrobial potential against food-borne pathogenic bacteria (E. coli, S. enteritidis, and L. monocytogenes) and high antifungal potential against dermatophyte fungi (T. mentagrophytes, and T. rubrum). RO had unique high level of γ-tocopherol, which is a scavenger of reactive nitrogen species making it a promising material in the food, cosmetic and pharmaceutical applications.     

Induction Motors Broken Rotor Bars Diagnosis Through the Discrete Wavelet Transform of the Instantaneous Reactive Power Signal under Time-varying Load Conditions

Abstract—In this article, a method based on the application of the discrete wavelet transform to the instantaneous reactive power signal, for diagnosing the occurrence of broken rotor bars in induction motors operating under time-varying load conditions, is presented. This method is based on the decomposition of the instantaneous reactive power signal, from which wavelet approximation and detail coefficients are extracted. The energy evaluation of known bandwidths permits to de?ne a fault severity factor. This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. These simulation results are complemented by experimental tests conducted on an induction motor with several faulty rotors that can be interchanged and both simulation and experimental results have shown the effectiveness of the proposed method for broken rotor bars diagnosis in induction motors even under time-varying load conditions.     

Low molecular weight serine protease from the viscera of sardinelle (Sardinella aurita) with collagenolytic activity: Purification and characterisation

A new low molecular weight (LMW) serine-protease from sardinelle (Sardinella aurita) viscera was purified using ammonium sulphate precipitation and Sephadex G-100 gel filtration, with a 3.82-fold increase in specific activity. The molecular weight of the enzyme was estimated to be 14.2 kDa by SDS-PAGE. The optimum pH and temperature for the enzyme activity were around pH 8.0 and 60 °C, respectively. The purified protease was strongly inhibited by phenylmethylsulphonyl fluoride, a serine-protease inhibitor, and soybean trypsin inhibitor. The N-terminal amino acid sequence of the first 10 amino acids of the purified protease was APVQPCVVVI. This sequence showed low homology with several peptidases, suggesting that the enzyme is a new protease. Interestingly, the protease was found to cleave collagen type I and hydrolyze succinyl-L-Ala-L-Ala-L-Pro-L-Phe-p-nitroanilide (sAAPFpna), an amide substrate of chymotrypsin. Our findings indicate that the S. aurita protease is a new LMW enzyme with collagenolytic activity.     

The role of porous media in modeling flow and heat transfer in biological tissues

Flow and heat transfer in biological tissues are analyzed in this investigation. Pertinent works are reviewed in order to show how transport theories in porous media advance the progress in biology. The main concepts studied in this review are transport in porous media using mass diffusion and different convective flow models such as Darcy and the Brinkman models. Energy transport in tissues is also analyzed. Progress in development of the bioheat equation (heat transfer equation in biological tissues) and evaluation of the applications associated with the bioheat equation are analyzed. Prominent examples of diffusive applications and momentum transport by convection are discussed in this work. The theory of porous media for heat transfer in biological tissues is found to be most appropriate since it contains fewer assumptions as compared to different bioheat models. A concept that is related to flow instabilities caused by swimming of microorganisms is also discussed. This concept named bioconvection is different from blood convection inside vessels. The works that consider the possibility of reducing these flow instabilities using porous media are reviewed.     

Cognitive-Mathematical Approaches for Evaluating Architectural Contextual Fit

The main goal of this study is to apply a scientific quantitative approach to the investigation of contextual fit. This is approached mathematically within the framework of cognitive science and research on categorization and prototypes. Two experiments investigated two leading mathematical-cognitive approaches for explaining people’s judgment of contextual fit of a new building with an architectural/urban context: prototype approach and feature frequency approach. The basic concept is that people represent the built environment via architectural prototypes and/or frequencies of encountered architectural features. In the first experiment, a group of twelve participants performed rank order tasks on artificially created architectural patterns, for the purpose of psychological scaling. Perceptual distances among all patterns were mathematically determined. In the second experiment, three groups of architectural patterns were constructed to represent assumed architectural contexts. The prototype of each context was mathematically determined according to prototype cognitive model, and based on the distances calculated in the first experiment. Fifty-six students participated in the main experiment, in which they rank ordered a group of fifteen architectural patterns in terms of contextual fit to each of the three architectural contexts. Participants’ rank order data of the fifteen patterns were regressed on both the perceptual distances from prototypes, and numbers of features shared with each architectural context. Results indicated that both prototype and feature frequency approaches significantly accounted for important portions of participants’ judgments. However, participants tended to prefer one approach to the other according to context composition. Results have implications for both research on utilizing cognitive-mathematical models in architectural research and on urban design guidelines and control.     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

     

Speech emotion recognition using optimized genetic algorithm-extreme learning machine

Multimedia Tools and Applications - Automatic Emotion Speech Recognition (ESR) is considered as an active research field in the Human-Computer Interface (HCI). Typically, the ESR system is...     

Subcarrier grouping with environmental sensing for MIMO‐OFDM systems over correlated double‐selective fading channels

Multiple‐Input, Multiple‐Output (MIMO)‐orthogonal frequency division multiplexing (OFDM) is a promising technique in 5G wireless communications. In high‐mobility scenarios, the transmission environments are time‐varying and/or the relative moving velocity between the transmitter and receiver is also time‐varying. In the literature, most of previous works mainly focused on fixed subcarrier group size and precoded the MIMO signals with unitary channel state information. In this way, the subcarrier grouping may naturally lead to big loss of channel capacity in high‐mobility scenarios because of the channel state information difference on the subcarriers in each group. To employ the MIMO‐OFDM technique, adaptive subcarrier grouping scheme may be an efficient way. In this paper, we first consider MIMO‐OFDM systems over double‐selective i.i.d. Rayleigh channels and investigate the quantitative relation between subcarrier group size and capacity loss theoretically. With developed theoretical results, we also propose an adaptive subcarrier grouping scheme to satisfy the preset capacity loss threshold by adjusting grouping size with the sensed environmental information and mobile velocity. Theoretical analysis and simulation results show that to achieve a better system capacity, a sparse scattering, lower signal‐to‐noise ratio, and lower velocity as well as properly large antenna number are matched with larger subcarrier group size. One important observation is that if the antenna number is too large and higher than a threshold, which will not bring any additional gain to the subcarrier grouping. That is, the system capacity loss will converge to a lower bound expeditiously with respect to antenna number, which is given in theory also. Copyright © 2016 John Wiley & Sons, Ltd.