Design of Smart Door Closer System with Image Classification over WLAN

Wireless Personal Communications - A dual purpose system is presented in this paper which serves not only as a door closer, but is equally effective for surveillance purposes. The currently...     

Ionic liquid-based deep eutectic solvents as novel solvent-cum-catalyst media for thermal dehydrogenation of chemical hydrides

The current work explores the usage of novel synthesized Deep Eutectic Solvent (DES) as a catalyst cum solvent media for the thermal dehydrogenation of chemical hydrides, namely Ammonia Borane (AB) and Ethylene diamine bisborane (EDAB). In the first instance, the quantum chemistry based COSMO-SAC (COnductor like Screening MOdel Segment Activity Coefficient) model was used for the selection of the pertinent solvent. 1-Butyl-3-methylimidazolium methanesulfonate: Imidazole ([BMIM][MeSO₃]:[Im]) turned out to be an ideal eutectic mixture with the highest predicted solubility with amine boranes. The DES was synthesized by combining the Hydrogen Bond Acceptor (HBA), namely 1-Butyl-3-methylimidazolium methanesulfonate and Imidazole as Hydrogen Bond Donor (HBD) at a molar ratio of 1:2 and T = 70 °C. The formation of DES was confirmed by recording the NMR spectra. Further, the thermal dehydrogenation study was performed at a vacuum of 4 × 10^?2 mbar (gauge pressure) of AB/DES and EDAB/DES systems at 105 °C, where a hydrogen equivalent of 1.40 and 2.55 was produced, respectively. The residual samples were further analyzed through ¹H NMR analysis for the reaction mechanism and to confirm the role of Ionic Liquid-based DES as catalyst cum solvent media.     

A metaheuristic analysis of heat transfer rates through porous fins of tapered and step profiles: a comparative study

Neural Computing and Applications - The advanced high-end gadgets and sophisticated machines require effective mechanism of transferring heat away from the sources. In a large number of...     

Impact of adhesive ingredients on adhesion between rubber and brass-plated steel wire in tire

Proficiency on underlying mechanism of rubber-metal adhesion has been increased significantly in the last few decades. Researchers have investigated the effect of various ingredients, such as hexamethoxymethyl melamine, resorcinol, cobalt stearate, and silica, on rubber-metal interface. The role of each ingredient on rubber-metal interfacial adhesion is still a subject of scrutiny. In this article, a typical belt skim compound of truck radial tire is selected and the effect of each adhesive ingredient on adhesion strength is explored. Out of these ingredients, the effect of cobalt stearate is found noteworthy. It has improved adhesion strength by 12% (without aging) and by 11% (humid-aged), respectively, over control compound. For detailed understanding of the effect of cobalt stearate on adhesion, scanning electron microscopy and energy dispersive spectroscopy are utilized to ascertain the rubber coverage and distribution of elements. X-ray photoelectron spectroscopy results helped us to understand the impact of Cu_XS layer depth on rubber-metal adhesion. The depth profile of the Cu_XS layer was found to be one of the dominant factors of rubber-metal adhesion retention. Thus, this study has made an attempt to find the impact of different adhesive ingredients on the formation of Cu_XS layer depth at rubber-metal interface and establish a correlation with adhesion strength simultaneously.     

Prevalence of hepatitis B and C virus infections among haemodialysis patients in Pune (western India)

Vascular injuries may occur as complications of elbow dislocation and usually involve the brachial artery. A case report is presented in which only the radial artery was compromised as a result of the dislocation.     

A new method to estimate parameters of linear compartmental models using artificial neural networks

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models.     

Calculation of shift of avalanche transit time phase delay due tooptically injected carriers in indium phosphide avalanche diodes

A computer method for the calculation of the phase shift due to optically injected carriers in an InP avalanche transit time diode has been suggested using the numerically simulated negative resistance profiles in the depletion layer of the diode. The results show that the phase shift due to hole injection is larger than that due to electron injection which explains the pronounced effect of photogenerated hole leakage current in modulating the microwave properties of InP diodes