Dynamic light scattering measurements on the polystyrene/ethyl acetate system at semi-dilute concentrations as a function of temperature

Summary Measurements of autocorrelation functions extending over a broad time range are reported for a sample of polystyrene in ethyl acetate as a function of temperature between –44°C (-temperature) and 70°C. The corresponding spectra of decay times are obtained by two mathematical methods. The existence of three dynamic processes is shown and their temperature and angular behaviour is studied.     

Fast internal dynamics in polyelectrolyte gels measured by dynamic light scattering

Summary Dynamic light scattering was used to investigate the dynamics of sodium poly(styrene sulfonate) and fully neutralized poly(acrylic acid) gels as a function of the degree of swelling and weight ratio of cross-linking agent. It was shown that the collective diffusion coefficient increases with increasing degree of swelling and that the diffusion coefficient shows stronger concentration dependence than predicted by scaling arguments. For gel samples measured at the swelling equilibrium, the diffusion coefficient increases with increasing gel concentration for both gel systems.     

The costs and benefits of tonal centers for chord processing.

Harmonic priming studies have shown that a musical context, with its established tonal center, influences target chord processing. This study investigated costs and benefits of priming tonal centers for target processing by adding a baseline condition (sequences without a specific tonal center). Results confirmed harmonic priming, with faster processing for related than for unrelated and less related targets (tonic chord, out-of-key chord, subdominant chord). Comparing targets in baseline contexts with targets in sequences with well-established tonal centers revealed a benefit of processing for related targets but a cost of processing for unrelated and less related targets. Findings are discussed in terms of tonal knowledge activation and suggest that an activated tonal center gives rise to strong expectations for the tonic. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Relative viscosity models and their application to capillary flow data of highly filled hard‐metal carbide powder compounds

The rheological behavior of highly filled polymer systems used in powder injection molding (PIM) technology strongly influences the final properties of the products. In this study, the capillary flow data of multi‐component polymer binders—based on polyethylene, paraffin, ethylene‐based copolymers, and polyethylene glycol—compounded with three various hard‐metal carbide powders were employed. The rheology of such highly filled (up to 50 vol%) multiphase systems is necessarily a complex phenomenon characterized by strain dependent, non‐Newtonian properties complicated by flow instabilities and yield. Over 15 mathematical models proposed for highly filled systems were tested, some of them calculating the maximum filler loading. Due to the complex structure of the filler (irregular shape, particle size distribution) and a multi‐component character of the binder, the applicability of these models varied with the powder‐binder systems studied. However, the particular values of maximum loadings are in good accordance with the predictions based on powder characteristics. Simple modification of Frankel‐Acrivos model to the systems containing unimodal hard‐metal carbide powders with particles of an irregular shape and broad particle size distribution gave precise agreement between experimental data and model prediction. POLYM. COMPOS., 26:29–36, 2005. © 2004 Society of Plastics Engineers.     

Complexes of Silver(I) Ions and Silver Phosphate Nanoparticles with Hyaluronic Acid and/or Chitosan as Promising Antimicrobial Agents for Vascular Grafts

Polymers are currently widely used to replace a variety of natural materials with respect to their favourable physical and chemical properties, and due to their economic advantage. One of the most important branches of application of polymers is the production of different products for medical use. In this case, it is necessary to face a significant disadvantage of polymer products due to possible and very common colonization of the surface by various microorganisms that can pose a potential danger to the patient. One of the possible solutions is to prepare polymer with antibacterial/antimicrobial properties that is resistant to bacterial colonization. The aim of this study was to contribute to the development of antimicrobial polymeric material ideal for covering vascular implants with subsequent use in transplant surgery. Therefore, the complexes of polymeric substances (hyaluronic acid and chitosan) with silver nitrate or silver phosphate nanoparticles were created, and their effects on gram-positive bacterial culture of Staphylococcus aureus were monitored. Stages of formation of complexes of silver nitrate and silver phosphate nanoparticles with polymeric compounds were characterized using electrochemical and spectrophotometric methods. Furthermore, the antimicrobial activity of complexes was determined using the methods of determination of growth curves and zones of inhibition. The results of this study revealed that the complex of chitosan, with silver phosphate nanoparticles, was the most suitable in order to have an antibacterial effect on bacterial culture of Staphylococcus aureus. Formation of this complex was under way at low concentrations of chitosan. The results of electrochemical determination corresponded with the results of spectrophotometric methods and verified good interaction and formation of the complex. The complex has an outstanding antibacterial effect and this effect was of several orders higher compared to other investigated complexes.     

Arbitrary shaped ice particle melting under the influence of natural convection

This work is devoted to numerical simulations of an arbitrary shaped ice particle melting inside water under the influence of natural convection. Specifically, four different shapes of the ice particle have been studied: sphere, cylinder, cross shaped cylinder, and irregular sphere with radial bumps on its surface. A 2D axisymmetric particle‐resolved numerical model has been employed on a fixed grid to study the detailed melting dynamics of an ice particle. The solid‐liquid interface is treated as a porous medium characterized by the permeability coefficient which is used to damp the velocity values inside the interface. The model results have been compared with an existing experimental results produced by A. Shukla et al. (Metal Mater Trans B. 2011; 42(1):224–235). Very good agreement between our predictions and experimental data have been achieved. Based on the analysis of numerical simulation results, melting process is found to advance through two distinct regimes, namely, establishment of the natural convection and active melting of ice particle exhibiting substantial amount of fluid‐particle interactions. A set of dimensionless parameters have been identified to distinguish between regimes. Finally, we developed a semi‐empirical to predict the melting of any arbitrary shaped ice particle and validated it against the particle‐resolved numerical simulation and experimental results. The comparison showed good agreement. Finally, the presented semi‐empirical model can be used as sub‐grid model in Euler‐Lagrange based numerical models to study the phase change phenomena in particulate flow systems. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3158–3176, 2017     

Linear color segmentation and its implementation

A framework for color image segmentation is presented, which combines color histogram analysis and region merging approach. Its main goal is to segment an image at material boundaries (i.e., discontinuities of reflectance properties) while ignoring spatial color inhomogeneities of uniformly pigmented (colored) objects, caused by accidents of illumination and viewing geometry. Theoretical examination of light spectrum transformations upon light reflection from material surfaces and upon interaction with a sensor system shows that in a wide variety of viewed scenes (even containing interreflections and highlight areas) uniformly pigmented objects are projected to the color space of the sensor as planar, linear, or point-like clusters, depending on lighting and viewing conditions and object geometry. To detect such clusters in the color space, three methods are suggested: Generalized Hough Transform method, gradient descent method, and eigenvectors method. A framework algorithm of color segmentation based on region merging approach is developed, which can use any of these methods. Testing this algorithm with both artificially generated and real images shows quite reliable results.     

Detecting Fixed Patterns in Chordal Graphs in Polynomial Time

The Contractibility problem takes as input two graphs G and H, and the task is to decide whether H can be obtained from G by a sequence of edge contractions. The Induced Minor and Induced Topological Minor problems are similar, but the first allows both edge contractions and vertex deletions, whereas the latter allows only vertex deletions and vertex dissolutions. All three problems are NP-complete, even for certain fixed graphs H. We show that these problems can be solved in polynomial time for every fixed H when the input graph G is chordal. Our results can be considered tight, since these problems are known to be W[1]-hard on chordal graphs when parameterized by the size of H. To solve Contractibility and Induced Minor, we define and use a generalization of the classic Disjoint Paths problem, where we require the vertices of each of the k paths to be chosen from a specified set. We prove that this variant is NP-complete even when k=2, but that it is polynomial-time solvable on chordal graphs for every fixed k. Our algorithm for Induced Topological Minor is based on another generalization of Disjoint Paths called Induced Disjoint Paths, where the vertices from different paths may no longer be adjacent. We show that this problem, which is known to be NP-complete when k=2, can be solved in polynomial time on chordal graphs even when k is part of the input. Our results fit into the general framework of graph containment problems, where the aim is to decide whether a graph can be modified into another graph by a sequence of specified graph operations. Allowing combinations of the four well-known operations edge deletion, edge contraction, vertex deletion, and vertex dissolution results in the following ten containment relations: (induced) minor, (induced) topological minor, (induced) subgraph, (induced) spanning subgraph, dissolution, and contraction. Our results, combined with existing results, settle the complexity of each of the ten corresponding containment problems on chordal graphs.     

Review of adaptation mechanisms for data-driven soft sensors

In this article, we review and discuss algorithms for adaptive data-driven soft sensing. In order to be able to provide a comprehensive overview of the adaptation techniques, adaptive soft sensing methods are reviewed from the perspective of machine learning theory for adaptive learning systems. In particular, the concept drift theory is exploited to classify the algorithms into three different types, which are: (i) moving windows techniques; (ii) recursive adaptation techniques; and (iii) ensemble-based methods. The most significant algorithms are described in some detail and critically reviewed in this work. We also provide a comprehensive list of publications where adaptive soft sensors were proposed and applied to practical problems. Furthermore in order to enable the comparison of different methods to standard soft sensor applications, a list of publicly available data sets for the development of data-driven soft sensors is presented.     

Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm

In order to improve the accuracy of rolling bearing fault diagnosis in mechanical equipment, a new fault diagnosis method based on back propagation neural network optimized by cuckoo search algorithm is proposed. This method use the global search ability of the cuckoo search algorithm to constantly search for the best weights and thresholds, and then give it to the back propagation neural network. In this paper, wavelet packet decomposition is used for feature extraction of vibration signals. The energy values of different frequency bands are obtained through wavelet packet decomposition, and they are input as feature vectors into optimized back propagation neural network to identify different fault types of rolling bearings. Through the three sets of simulation comparison experiments of Matlab, the experimental results show that, Under the same conditions, compared with the other five models, the proposed back propagation neural network optimized by cuckoo search algorithm has the least number of training iterations and the highest diagnostic accuracy rate. And in the complex classification experiment with the same fault location but different bearing diameters, the fault recognition correct rate of the back propagation neural network optimized by cuckoo search algorithm is 96.25%.