Film Blowing of Layered Silicate Nanocomposites

Bubble formation and stability in the film blowing processing of in situ polymerized and melt-compounded polyamide 6-based layered silicate nanocomposites (LSNs) are correlated to their underlying rheology, structure, and crystallization behavior. The layered silicates enhance melt elasticity, induce γ -form crystallinity, and increase crystallization rates without having any significant effect on the extent of crystallinity. A bubble stability quantification method employed to assess the level of instability during the film blowing process finds the in situ polymerized LSNs to be more stable than PA6, while melt-compounded LSNs do not display such an improved processability. All of the LSN films produced by film blowing possess superior mechanical properties compared to neat nylon 6, despite their relatively rougher film surfaces.     

Subchannel Allocation and Power Control for Uplink Femtocell Radio Networks with Imperfect Channel State Information

Wireless Personal Communications - Femtocell technology is emerging as a key solution for mobile operators for its advantage in coverage and capacity enhancement along with its cost effectiveness....     

A Monte Carlo simulation study of the mechanical and conformational properties of networks of helical polymers. I. General concepts

We study the mechanical and conformational properties of networks of helical polymers with a combination of Monte Carlo simulations based on the Wang-Landau algorithm and the three-chain model. We find that the stress-strain behavior of these networks has novel features not observed in typical networks made of synthetic polymers. In particular, we find that as these networks are stretched they first strengthen, then soften and finally strengthen again. This non-monotonic behavior of the stress correlates with the one of the helical content and is rationalized by the elongation-induced formation and melting of the helical structure of the polymer. We complement these results with a study of other conformational properties of the polymer strands that clarify the molecular mechanisms behind the mechanical behavior of these networks. Finally, we present a qualitative comparison of some of our results with the theoretical ones recently reported by Kutter and Terentjev.     

Machining strategy development and parameter selection in 5-axis milling based on process simulations

Fused deposition modeling (FDM) is one of the most popular additive manufacturing technologies for fabricating prototypes with complex geometry and different materials. However, current commercial FDM machines have the limitations in process reliability and product quality. In order to overcome these limitations and increase the levels of machine intelligence and automation, machine conditions need to be monitored more closely as in closed-loop control systems. In this study, a new method for in situ monitoring of FDM machine conditions is proposed, where acoustic emission (AE) technique is applied. The proposed method allows for the identification of both normal and abnormal states of the machine conditions. The time-domain features of AE hits are used as the indicators. Support vector machines with the radial basis function kernel are applied for state identification. Experimental results show that this new method can potentially serve as a non-intrusive diagnostic and prognostic tool for FDM machine maintenance and process control.     

Preparation of Yttria-Stabilized Zirconia Thin Films on Strontium-Doped LaMnO3 Cathode Substrates via Electrophoretic Deposition for Solid Oxide Fuel Cells

A yttria-stabilized zirconia (YSZ) thin film on an La_0.8Sr_0.2MnO₃ porous cathode substrate was prepared, using electrophoretic deposition (EPD) to fabricate a solid oxide fuel cell (SOFC). The electrical conductivity of an La_0.8Sr_0.2MnO₃ substrate is satisfactorily high at room temperature; therefore, YSZ powder could be deposited electrophoretically onto an La_0.8Sr_0.2MnO₃ substrate without any extra surface treatment, such as a metal coating. Successive repetition of EPD and sintering was required to obtain a film without gas leakage, because of the thermal expansion coefficient mismatch between the YSZ and the La_0.8Sr_0.2MnO₃ substrate. On the other hand, the electromotive force of the oxygen concentration in the cell that used YSZ film prepared via EPD increased and attained the theoretical value when the number of deposition and calcination cycles was increased. Six or more successive repetitions were required to obtain a YSZ film without gas leakage. A planar-type SOFC was fabricated, using nickel as the anode and YSZ film (∼10 μm thick) that had been deposited onto the La_0.8Sr_0.2MnO₃ substrate as the electrolyte and cathode. The cell exhibited an open circuit voltage of 1.0 V and a maximum power density of 1.5 W/cm². Thus, the EPD method could be used as a colloidal process to prepare YSZ thin-film electrolytes for SOFCs.     

CHEMICAL CHARACTERISTICS OF SUPERCRITICAL TOLUENE EXTRACTS FROM TWO TURKISH LIGNITES

ABSTRACT

Samples from two major deposits of Turkish lignites were subjected to supercritical toluene extraction at 325°C and 14.1 MPa in an autoclave. The resultant extracts were separated into pre-asphaltene, asphaltene, saturates, aromatics and resin fractions by column chromatography. The fractions were characterized by ¹H-NMR, ¹³C-NMR, IR. and elemental analysis in order to compare the chemical composition of extracis from the two coal samples. The results show that the extracts from the Tungbiiek (T-coal) deposits contain aliphatic constituent which is composed of mostly long chain unbranched alkyl compounds while the extracts from the Soma Mcrkeg (S-coal) deposits contain a significant amount of branched paraffinic compounds. The resin fractions from both samples were found to contain a significant amount of single ring aromatic compounds.     

Cost-conscious comparison of supervised learning algorithms over multiple data sets

In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi²Test, a generalization of our previous work, for ordering multiple learning algorithms on multiple data sets from “best” to “worst” where our goodness measure is composed of a prior cost term additional to generalization error. Our simulations show that Multi²Test generates orderings using pairwise tests on error and different types of cost using time and space complexity of the learning algorithms.     

Eigenclassifiers for combining correlated classifiers

In practice, classifiers in an ensemble are not independent. This paper is the continuation of our previous work on ensemble subset selection [A. Ula?, M. Semerci, O.T. Y?ld?z, E. Alpayd?n, Incremental construction of classifier and discriminant ensembles, Information Sciences, 179 (9) (2009) 1298–1318] and has two parts: first, we investigate the effect of four factors on correlation: (i) algorithms used for training, (ii) hyperparameters of the algorithms, (iii) resampled training sets, (iv) input feature subsets. Simulations using 14 classifiers on 38 data sets indicate that hyperparameters and overlapping training sets have higher effect on positive correlation than features and algorithms. Second, we propose postprocessing before fusing using principal component analysis (PCA) to form uncorrelated eigenclassifiers from a set of correlated experts. Combining the information from all classifiers may be better than subset selection where some base classifiers are pruned before combination, because using all allows redundancy.