A projection method for edge detection in images

An edge-detection method is proposed using the time-frequency domain method that expands the image intensity function in a series of Hermite functions. Test calculations and the application of the method to edge detection in real images demonstrate its noise tolerance. __________ Translated from Prikladnaya Matematika i Informatika, No. 23, pp. 118–133, 2006.     

Selective Complexation of S‐block Cations with Nanotubular Silk Type Cyclopeptides: A DFT Study

Density functional theory (DFT) was used to study the interaction of alkali metal cations (Li⁺, Na⁺ and K⁺) with cyclic peptides constructed from silk type macrocycles ( Silk1, Silk2, Silk3, Silk4, Silk5 and Silk6 ). The calculated binding energies were used as a base for investigating the selectivity of the cyclic peptides in biniding to considered metals ions. The highest cation selectivity for Li⁺ compared to the other alkali metal ions was observed. The orbital nature of different interactions between the metal cations and the cyclic peptides was analyzed using NBO analysis. The main types of driving force for host‐guest interactions was investigated and it was found that the electron‐donating O offers lone pair electrons to the contacting LP* of alkali metal cations     

Porous electrospun nanocomposite mats based on chitosan–cellulose nanocrystals for wound dressing: effect of surface characteristics of nanocrystals

Randomly oriented fiber mats of chitosan–polyethylene oxide matrix reinforced with cellulose nanocrystals (CNCs) were prepared by electrospinning technique. The cellulose nanocrystals used were isolated using hydrochloric acid (CNC_HCl) or sulphuric acid (\({\text{CNC}}_{{{\text{H}}_{ 2} {\text{SO}}_{ 4} }}\)) and the concentration of CNCs was 50 wt% in the electrospun mats. The surface characteristics of the nanocrystals were found to affect the dispersion, viscosity, conductivity and zeta-potential of the respective spinning solutions and resulted in better spinnability, homogeneity as well as crosslinking of CNC_HCl based nanocomposite fiber mats compared to \({\text{CNC}}_{{{\text{H}}_{ 2} {\text{SO}}_{ 4} }}\) ones. The microscopy studies showed that the diameter of the electrospun fibers decreased with the inclusion of both types of nanocrystals and that crosslinking decreased the porosity of the mats. The tensile strength and tensile modulus of the mats increased with the addition of nanocrystals and increased further for the CNC_HCl based mats (58 MPa, 3.1 GPa) after crosslinking. The as-spun CNC_HCl based mats had average pore diameters of 1.6 μm and porosity of 38 %. The water vapor permeability and the O₂/CO₂ transmission increased with the addition of CNC_HCl. The used nanocrystals as well as electrospun mats showed non-cytotoxic impact on adipose derived stem cells (ASCs), which was considered favorable for wound dressing.     

Using the QSPR Approach for Estimating the Density of Azole‐based Energetic Compounds

The relationship between density of energetic azole‐based compounds and their molecular structure is investigated through quantitative structure‐property relationship (QSPR) approach. The methodology of this work introduces a new model, which related density of azole‐based energetic compounds to the optimum elemental composition, the degree of unsaturation (DoU) of the compounds, presence of nitroimino group in the structural formula, as well as several non‐additive structural parameters. The presence of nitroimino functional group and also increasing the value of n_O/n_N in the formula of these compounds can enhance their density. The correlation is derived on the basis of experimental density values of 100 azole‐based energetic compounds with different molecular structure as training set. The determination coefficient of the new correlation is 0.923. Also, it has the root mean square deviation (RMSD) and the average absolute deviation (AAD) of 0.038 and 0.030 g · cm^–3, respectively. In addition, the correlation gives good predictions for further 25 azole‐based energetic compounds as test set (Q²_EXT = 0.901). The predictive ability of the correlation is checked using a cross validation method (Q²_LMO = 0.918). The proposed method can also apply for designing novel azole‐based energetic compounds.     

Two Reliable Simple Relationships between Flash Points of Hydrocarbon Kerosene Fuels and Their Molecular Structures

This study presents two new reliable simple correlations for predicting flash point of kerosene hydrocarbons using multiple linear regression method. The methodology assumes that the flash point of kerosene fuels can be expressed as a function of elemental composition and several structural parameters. The proposed correlations have determination coefficients of 0.910 and 0.977. Also, the first model has root mean square deviation (RMSD) and the average absolute deviations (AAD) of 10.6 and 8.2 K, respectively, for 111 kerosene fuels with different molecular structures as training set. The RMSD and AAD for the second improved model are 5.39 and 4.33 K, respectively. The predictive power of two correlations is checked using a cross validation method. (R² = 0.977, Q²_Ext = 0.975, and Q²_LMO = 0.979). Also, these correlations give good predictions for further 25 kerosene fuels as test set. The proposed model can also be applied for designing novel kerosene fuels.     

Size‐based proteins separation using polymer‐entrapped colloidal self‐assembled nanoparticles on‐chip

We report on a facile method to stabilize colloidal self‐assembled (CSA) nanoparticles packed in microchannels for high speed size‐based separation of proteins. Silica nanoparticles, self‐assembled in a network of microfluidic channels, were stabilized with a methacrylate polymer prepared in situ through photopolymerization. The entrapment conditions were investigated to minimize the effect of the polymer matrix on the structure of the packing and the separation properties of the CSA beds. SEM shows that the methacrylate matrix links the nanoparticles at specific sphere–sphere contact points, improving the stability of the CSA structure at high electric fields (up to at least 1800 V/cm), allowing fast and efficient separation. The %RSD of the protein migration times varied between 0.3 and 0.5% (n = 4, in 1 day) and <0.83% over a period of 7 days (n = 28 runs) in a single device, at high field strength. The overall %RSD of protein migration times from chip‐to‐chip across a single fabrication run was 4.3% (n = 3) and between fabrication runs was 11% (n = 35), with 87% fabrication yield, demonstrating reproducible packing and entrapment behavior. The optimized entrapped CSA beds demonstrated better separation performance (plate height, H ～ 200 nm) than similarly prepared on‐chip CSA beds without the polymer entrapment. Polymer‐entrapped CSA beds also exhibited superior protein resolving power: the minimum resolvable molecular weight difference of proteins in the polymer‐entrapped CSA bed is 0.6 kDa versus ～9 kDa for the native silica CSA bed (i.e. without polymer entrapment).     

Piston scuffing fault and its identification in an IC engine by vibration analysis

In this paper, the effects of piston scuffing fault on engine performance and vibrations are investigated. A procedure based on vibration analysis is also presented to identify piston scuffing fault. To this end, an internal combustion (IC) engine ran under a specific test procedure. The engine parameters and vibration signals were measured during the experiments. To produce piston scuffing fault, three-body abrasive wear mechanism was employed. The experimental results showed that piston scuffing fault caused the engine performance to reduce significantly. The vibration signals were analyzed in time-domain, frequency-domain and time–frequency domain. Continuous wavelet transform (CWT) was used to obtain time–frequency representations. “dmey” wavelet was selected as the optimum wavelet type for this research among different wavelet types using the three criteria of energy, Shannon entropy and energy to Shannon entropy ratio. The results of CWT analysis by “dmey” wavelet showed that piston scuffing fault excited the frequency band of 2.4–4.7 kHz in which the frequency of 3.7 kHz was affected more. Finally, seven different features were extracted from the engine vibration signals related to the frequency band of 2.4–4.7 kHz. The results indicated that maximum, mean, RMS, skewness, kurtosis and impulse factor of the engine vibration related to the found frequency band increased significantly due to piston scuffing fault. The obtained results showed that the proposed method identified piston scuffing fault and discovered the vibration characteristics of this fault like frequency band. The results also demonstrated the possibility of using engine vibrations in piston scuffing fault identification.