Thermal modeling of a cylindrical LiFePO4/graphite lithium-ion battery

A lumped-parameter thermal model of a cylindrical LiFePO₄/graphite lithium-ion battery is developed. Heat transfer coefficients and heat capacity are determined from simultaneous measurements of the surface temperature and the internal temperature of the battery while applying 2 Hz current pulses of different magnitudes. For internal temperature measurements, a thermocouple is introduced into the battery under inert atmosphere. Heat transfer coefficients (thermal resistances in the model) inside and outside the battery are obtained from thermal steady state temperature measurements, whereas the heat capacity (thermal capacitance in the model) is determined from the transient part. The accuracy of the estimation of internal temperature from surface temperature measurements using the model is validated on current-pulse experiments and a complete charge/discharge of the battery and is within 1.5 °C. Furthermore, the model allows for simulating the internal temperature directly from the measured current and voltage of the battery. The model is simple enough to be implemented in battery management systems for electric vehicles.     

Advances in Rational Design and Materials of High‐Performance Stretchable Electromechanical Sensors

Stretchable and wearable sensor technology has attracted significant interests and created high technological impact on portable healthcare and smart human–machine interfaces. Wearable electromechanical systems are an important part of this technology that has recently witnessed tremendous progress toward high‐performance devices for commercialization. Over the past few years, great attention has been paid to simultaneously enhance the sensitivity and stretchability of the electromechanical sensors toward high sensitivity, ultra‐stretchability, low power consumption or self‐power functionalities, miniaturisation as well as simplicity in design and fabrication. This work presents state‐of‐the‐art advanced materials and rational designs of electromechanical sensors for wearable applications. Advances in various sensing concepts and structural designs for intrinsic stretchable conductive materials as well as advanced rational platforms are discussed. In addition, the practical applications and challenges in the development of stretchable electromechanical sensors are briefly mentioned and highlighted.     

Real-time and robust multiple-view gender classification using gait features in video surveillance

Pattern Analysis and Applications - It is common to view people in real applications walking in arbitrary directions, holding items, or wearing heavy coats. These factors are challenges in...     

Study on the preparation of ordered mesoporous carbon-based catalyst from waste microalgal biomass for the synthesis of biokerosene

Investigation on preparation of novel ordered mesoporous carbon-based catalyst (MCC catalyst) containing super-acid sites from waste microalgal biomass was established. The waste microalgal biomass was partially carbonized at 400 °C for 2 h obtaining biochar; then the obtained biochar was introduced to a sulfonation process using concentrated sulfuric acid. The sulfonation was established at 150 °C for 15 h producing sulfonated biochar. The MCC catalyst was prepared through condensation–evaporation method in an alkaline media containing cetyltrimethylammonium bromide (CTAB) as structure directing agent. The parameters of the condensation including time of evaporating and CTAB content were investigated while fixing temperature at 90 °C during the process. A special hydrothermal treatment was also applied to the preparation of the MCC catalyst in order to expand its mesopores for more convenient with large molecule diffusion. The catalyst activity was well demonstrated by converting linseed oil to biokerosene which could be used as biokerosene—component for blending with aviation fuels. The MCC catalysts were characterized by X-ray diffraction, infrared spectroscopy, energy dispersive X-ray, scanning electron microscopy, transmission electron microscopy, nitrogen adsorption–desorption method and ammonia-temperature programmed desorption techniques. The biokerosene composition was determined by gas chromatography–mass spectroscopy method.     

Chitosan-TPP nanoparticle as a release system of antisense oligonucleotide in the oral environment

Antisense oligonucleotide loaded chitosan nanoparticles were prepared and the release of oligonucleotide from chitosan-TPP/oligonucleotide nanoparticles was investigated. Morphological property, zeta potential and particle size of the prepared chitosan/oligonucleotide nanoparticles were investigated using Field Emission-Scanning Electron Microscope (FE-SEM) and particle size analyzer. The interaction between chitosan and oligonucleotide was confirmed by using capillary zone electrophoresis (CZE), and the released oligonucleotides were determined by spectrophotometric method. Oligonucleotides formed the complexes with chitosan with a unique morphological property. The release of oligonucleotides from nanoparticles was dependent on loading methods and pH conditions. Chitosan/oligomer-TPP nanoparticles, which was prepared by adding TPP after the formation of chitosan/oligonucleotide complex, showed the lowest release percent of oligonucleotides with 41.3% at pH 7.0 among the loading methods. The percent release of oligonucleotide from oligonucleotide loaded chitosan nanoparticle at pH 10 was higher than the one in acidic condition (pH 5.0). The released oligonucleotides from chitosan/oligonucleotide nanoparticles were stable enough for 12 h under the 20% saliva solution. Our results suggest that the sustained release of oligonucleotide from chitosan nanoparticles may be suitable for the local therapeutic application in periodontal diseases.     

Blind separation of instantaneous mixture of sources via anindependent component analysis

In this paper, we introduce a procedure for separating a multivariate distribution into nearly independent components based on minimizing a criterion defined in terms of the Kullback-Leibner distance. By replacing the unknown density with a kernel estimate, we derive useful forms of this criterion when only a sample from that distribution is available. We also compute the gradient and Hessian of our criteria for use in an iterative minimization. Setting this gradient to zero yields a set of separating functions similar to the ones considered in the source separation problem, except that here, these functions are adapted to the observed data. Finally, some simulations are given, illustrating the good performance of the method