Melt‐blowing thermoplastic polyurethane and polyether‐block‐amide elastomers: Effect of processing conditions and crystallization on web properties

Melt‐blown webs from ester and ether thermoplastic polyurethanes and polyether‐block‐amide (PEBA) elastomers were produced at different die‐to‐collector distances (DCD) to study the correlation between the polymer type and hardness, melt‐blowing process conditions, and web properties. An experimental set up was built to measure the air temperature and velocity profiles below and across the melt‐blowing die to correlate the fiber formation process and polymer crystallization behavior to process conditions and web properties. It was shown that air temperature and velocity profiles follow similar trends with increasing distance below the melt‐blowing die: both drop rapidly until reaching a plateau region approximately 5–6 cm below the die. Thereafter, they remain relatively constant with further increasing distance. It was found that crystallization onset and peak temperatures of all block copolymers in this study fall within this region of rapid velocity and temperature drop. This suggests that the polymers have already started to crystallize and solidify before reaching the collector, the extent of which depends on the crystallization kinetics of the polymer. The strong influence of the crystallization kinetics on web strength was clearly demonstrated in the PEBA series. In particular, the hardest grade produced the lowest web strength mainly because of its high crystallization rate and crystallization onset temperature. It is concluded that the melt‐blown web strength is strongly dependent on the degree of fiber‐to‐fiber adhesion within the web, which is determined by the amount of fiber solidification that occurs prior to the collector. The crystallization kinetics of the polymer and the distances traveled between the die and collector or the exposure time of the polymer melt to process and ambient air were shown to be critical in the amount of fiber solidification attained. POLYM. ENG. SCI., 2009. © 2009 Society of Plastics Engineers     

A new FMEA method by integrating fuzzy belief structure and TOPSIS to improve risk evaluation process

Failure mode and effect analysis (FMEA) model is a technique used to evaluate the risk. This paper aimed to propose a new FMEA model combining technique for order of preference by similarity to ideal solution (TOPSIS) and belief structure to overcome the shortcomings of the traditional index of FMEA. In this paper, the fuzzy belief TOPSIS method is combined with FMEA to introduce a belief structure FMEA to describe the expert knowledge by a number of linguists as a grammatical phenomenon. Moreover, the weights of components in FMEA index can be different from each other. Therefore, the flexibility of assigning weight to each factor in this method is more compatible to the real decision-making situation. In other word, TOPSIS method is applied to determine the preference of alternatives versus risk criteria. Using linguistic terms in the fuzzy belief approach, the risk factors described a more meaningful value and decision-makers’ judgment is assigned with belief degrees through evaluation of factors. Finally, a numerical case study about the preference of cause failures of steel production process is provided to illustrate the process of proposed method, and then result and discussion is performed for each case.     

Verification of two machine learning approaches for cloud masking based on reflectance of channel IR3.9 using Meteosat Second Generation over Middle East maritime

ABSTRACT

Most cold channels of Meteosat Second Generation (MSG) satellites can distinguish between the sea and ice cloud tops, except for the IR3.9 channel because of the close reflectance and radiance values of the IR3.9 channel for maritime, low-level cloud and ice cloud tops. In this article, we introduce and evaluate two machine learning methods for cloud masking of Spinning Enhanced Visible and Infrared Imager (SEVIRI) images in the day and night that use the reflectance value of the IR3.9 channel. We reached a good correlation by comparing the results of the modelled cloud masking of Meteosat satellite images with MODIS (Moderate Resolution Imaging Spectroradiometer) and CLM (Cloud Mask product of EUMETSAT) images in a way that the coefficient of determination (R²) value was 92.34%, 89.91% and 83.69%, 78.23% in the cold season and 90.17%, 87.09% and 80.37%, 76.48% in the warm season, respectively, using the CHAID (chi-squared automatic interaction detection) decision tree and RBF (radial basis function) neural network approaches.     

Effects of Fe/SDS and Au nanoparticles on P. aeruginosa bacterial growth and biosurfactant production

The aim of this study was to evaluate the effects of iron (Fe)/SDS and gold (Au) nanoparticles on growth and biosurfactant production of Pseudomonas aeruginosa PBCC5. The concentrations of the nanoparticles used were 1, 500 and 1000 mg/l. In this research, the surface tension of biosurfactant, dry weight of biosurfactant and biomass, emulsification indexes (E₂₄) were measured and transmission electron microscopy analysis was used to monitor the nanoparticles. The test results showed that the effect of nanoparticles on the bacterial growth and biosurfactant production varied corresponding to the type and concentration of nanoparticles. Fe/SDS nanoparticles showed no bacterial toxicity when the concentration of nanoparticles was 1 mg/ml and increased the growth and biosurfactant production, 23.21 and 20.73%, respectively. While at higher concentrations (500, 1000 mg/l), the nanoparticles suppressed bacterial growth as well as biosurfactant production. Similarly, Au nanoparticles had no bacterial toxicity and also increased bacterial growth and biosurfactant production. The surface tensions of all samples decreased from 72 of distiled water to 32–35 mN/m.Inspec keywords: nanoparticles, iron, gold, nanofabrication, nanomedicine, surfactants, biomedical materials, surface tension, renewable materials, transmission electron microscopy, microorganismsOther keywords: Au nanoparticles, P. aeruginosa bacterial growth, biosurfactant production, Pseudomonas aeruginosa PBCC5, surface tension, biomass, emulsification indexes, dry weight, transmission electron microscopy, Fe‐SDS nanoparticles, distiled water, Fe, Au     

A comparative study of CO<Subscript>2</Subscript> and CH<Subscript>4</Subscript> adsorption using activated carbon prepared from pine cone by phosphoric acid activation

Adsorption of pure carbon dioxide and methane was examined on activated carbon prepared from pine cone by chemical activation with H₃PO₄ to determine the potential for the separation of CO₂ from CH₄. The prepared adsorbent was characterized by N₂ adsorption-desorption, elemental analysis, FTIR, SEM and TEM. The equilibrium adsorption of CO₂ and CH₄ on AC was determined at 298, 308 and 318 K and pressure range of 1–16 bar. The experimental data of both gases were analyzed using Langmuir and Freundlich models. For CO₂, the Langmuir isotherm presented a perfect fit, whereas the isotherm of CH₄ was well described by Freundlich model. The selectivity of CO₂ over CH₄ by AC (CO₂: CH₄=50: 50, 298K, 5 bar), predicted by ideal adsorbed solution theory (IAST) model, was achieved at 1.68. These data demonstrated that pine cone-based AC prepared in this study can be successfully used in separation of CO₂ from CH₄.     

Thin natural gelatin/chitosan nanofibrous scaffolds for retinal pigment epithelium cells

Damage of the retinal pigmented epithelial cells causes various diseases such as age-related macular degeneration in retinal tissue. Nowadays, scientists are attempting to replace lost retinal cells with healthy and efficient cells that provide better conditions for recovering and preventing blindness. In this study, gelatin/chitosan nanofibrous scaffolds with mean diameters of 180?nm were fabricated for subretinal space through electrospinning. Thickness and morphology of the gelatin–chitosan scaffolds were analyzed by scanning electron microscopy (SEM). The results showed that the high rate of degradation, i.e., 90% damage was obtained after 1 month. The cell viability of gelatin/chitosan nanofibers were measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. The SEM results of cultured RPE on gelatin/chitosan scaffolds showed the appropriate adhesion of cells on the substrate. The results of the identity of RPE cells cultured on the scaffolds indicated that a large number of cells strongly expressed RPE65 and cytokeratin 8/18.     

Zein nanoparticle‐embedded electrospun PVA nanofibers as wound dressing for topical delivery of anti‐inflammatory diclofenac

Bioactive wound dressings from poly(vinyl alcohol) (PVA) and zein nanoparticles (NPs) loaded with diclofenac (DLF) were prepared successfully by the single jet electrospinning method. DLF‐loaded zein NPs with an average diameter of ～228 nm were prepared using anti‐solvent precipitation method. The formulation of zein:DLF 1:1 exhibited optimum encapsulation efficiency of 47.80%. The NPs were characterized by dynamic light scattering, zeta‐potential measurement, and differential scanning calorimetry. In vitro, drug release profiles of the DLF‐loaded zein NPs, and PVA–zein NPs were also studied within 120 h and showed the release efficiency of nearly 80% from zein NPs. A more controlled release of DLF was achieved by embedding the zein NPs in the PVA nanofibers. Fourier transform infrared spectroscopy was used to analyze possible interactions between different components of the fabricated dressings. The mechanical properties of the developed dressings were also evaluated using uniaxial tensile testing. Young's modulus (E) of the dressings decreased after inclusion of zein NPs within the PVA nanofibers. Moreover, fibroblast culturing experiments proved that the composite dressings supported better cell attachment and proliferation compared to PVA nanofibers, by exhibiting moderate hydrophilicity. The results suggested that the electrospun composite dressing of PVA nanofibers and zein NPs is a promising topical drug‐delivery system and have a great potential for wound healing application. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46643.     

Fuzzy kernel K-medoids clustering algorithm for uncertain data objects

Pattern Analysis and Applications - Most data mining algorithms are designed for traditional type of data objects which are referred to as certain data objects. Certain data objects contain no...     

Towards improved parallelism through order reduction of accessing data in nD matrices

This paper encompasses the presentation of an enhanced approach with the capacity to reduce the time complexity of accessing nodes in m-dimensional matrices from \(O(n^m)\) to \(O(n\log n)\) . The accomplishment of this process is attained by the serialization of nD (nD) matrices to single-dimensional arrays followed by the access of nodes accordingly. Linear representation of nD matrix data structure induces a superior parallelism of matrix calculations over dense, parallel core micro-architecture computers, including NVIDIA GPGPU Supercomputing and Intel Xeon Phi processing boards. This approach is feasibly implemented as the core of matrix data representation in Math software such as Matlab, Mathematica and Maple, in IDEs for more optimized code generation and in Parallel Computing Libraries such as CUBLAS and Magma.     

Strength evaluation of granite block samples with different predictive models

Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (V_p) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R²) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.