Deformation and Fracture of Micron‐Sized Metal‐Coated Polymer Spheres: An In Situ Study

In situ imaging and analysis of the mechanical behavior of micron‐sized metal‐coated polymer particles under compression is reported. A nanoindentation set‐up mounted in a scanning electron microscope is used to observe the deformation and fracture of 10 μm polymer spheres with Ni, Ni/Au, Au, and Ag coatings. The spheres fracture in one of two metallization‐dependent modes, brittle, and ductile, depending only on the presence of a nickel layer. The metal coating always fractures parallel to the direction of compression. The mechanical properties up to the point of coating fracture are rate‐dependent due to the viscoelastic polymer core. Metal‐coated polymer spheres are an important composite material in electronics packaging, and this study demonstrates a novel method of evaluating the mechanical properties of particles to tailor them for electronic materials.

     

Latent classification models for binary data

One of the simplest, and yet most consistently well-performing set of classifiers is the naïve Bayes models (a special class of Bayesian network models). However, these models rely on the (naïve) assumption that all the attributes used to describe an instance are conditionally independent given the class of that instance. To relax this independence assumption, we have in previous work proposed a family of models, called latent classification models (LCMs). LCMs are defined for continuous domains and generalize the naïve Bayes model by using latent variables to model class-conditional dependencies between the attributes. In addition to providing good classification accuracy, the LCM has several appealing properties, including a relatively small parameter space making it less susceptible to over-fitting. In this paper we take a first step towards generalizing LCMs to hybrid domains, by proposing an LCM for domains with binary attributes. We present algorithms for learning the proposed model, and we describe a variational approximation-based inference procedure. Finally, we empirically compare the accuracy of the proposed model to the accuracy of other classifiers for a number of different domains, including the problem of recognizing symbols in black and white images.     

Interactive visual analysis of contrast-enhanced ultrasound data based on small neighborhood statistics

Contrast-enhanced ultrasound (CEUS) has recently become an important technology for lesion detection and characterization in cancer diagnosis. CEUS is used to investigate the perfusion kinetics in tissue over time, which relates to tissue vascularization. In this paper we present a pipeline that enables interactive visual exploration and semi-automatic segmentation and classification of CEUS data.For the visual analysis of this challenging data, with characteristic noise patterns and residual movements, we propose a robust method to derive expressive enhancement measures from small spatio-temporal neighborhoods. We use this information in a staged visual analysis pipeline that leads from a more local investigation to global results such as the delineation of anatomic regions according to their perfusion properties. To make the visual exploration interactive, we have developed an accelerated framework based on the OpenCL library, that exploits modern many-cores hardware. Using our application, we were able to analyze datasets from CEUS liver examinations, being able to identify several focal liver lesions, segment and analyze them quickly and precisely, and eventually characterize them.     

Efficient Vector Quantization Using the WTA-Rule with Activity Equalization

We propose a new algorithm for vector quantization, the Activity Equalization Vector quantization (AEV). It is based on the winner takes all rule with an additional supervision of the average node activities over a training interval and a subsequent re-positioning of those nodes with low average activities. The re-positioning is aimed to both an exploration of the data space and a better approximation of already discovered data clusters by an equalization of the node activities. We introduce a learning scheme for AEV which requires as previous knowledge about the data only their bounding box. Using an example of Martinetz et al. [1], AEV is compared with the Neural Gas, Frequency Sensitive Competitive Learning (FSCL) and other standard algorithms. It turns out to converge much faster and requires less computational effort.     

Effects of socio-emotional stressors on ventilation rate and subjective workload during simulated CPR by lay rescuers

Several studies have documented the occurrence of high ventilation rates during cardiopulmonary resuscitation, but to date, there have been no scientific investigation of the causes of hyperventilation. The objective of the current study was to test the effects of socio-emotional stressors on lay rescuers' ventilation rate in a simulated resuscitation setting using a manikin model. A within-subjects experiment with randomized order of conditions tested lay rescuers' ventilation rate on an intubated manikin during exposure to socio-emotional stressors and during a control condition where no external stressors were present. Ventilation rates and subjective workload were significantly higher during exposure to socio-emotional stressors than during the control condition. All but one of the nine participants ventilated at a higher ventilation rate in the experimental condition. All nine participants rated the subjective workload to be higher during exposure to socio-emotional stressors. Hence, exposure to socio-emotional stressors is associated with increased ventilation rates performed by lay rescuers during simulated cardiac arrest using a manikin model. These findings might have implications for the understanding of the type of situations which hyperventilation may occur. Awareness of these situations may have implications for training of lay rescues.     

Phototemtide A,a Cyclic Lipopeptide Heterologously Expressed from Photorhabdus temperata Meg1, Shows Selective Antiprotozoal Activity

A new cyclic lipopeptide, phototemtide A ( 1 ), was isolated from Escherichia coli expressing the biosynthetic gene cluster pttABC from Photorhabdus temperata Meg1. The structure of 1 was elucidated by HR-ESI-MS and NMR experiments. The absolute configurations of amino acids and 3-hydroxyoctanoic acid in 1 were determined by using the advanced Marfey's method and comparison after total synthesis of 1 , respectively. Additionally, three new minor derivatives, phototemtides B–D ( 2 – 4 ), were identified by detailed HPLC–MS analysis. Phototemtide A ( 1 ) showed weak antiprotozoal activity against Plasmodium falciparum, with an IC₅₀ value of 9.8 μm . The biosynthesis of phototemtides A–D ( 1 – 4 ) was also proposed.     

Electrical characterization of individual metal‐coated polymer spheres used in isotropic conductive adhesives

Isotropic conductive adhesives (ICAs) filled with metal‐coated polymer spheres (MPS) have been proposed to improve the mechanical reliability compared to conventional ICAs filled with silver flakes. The electrical properties of MPS play an important role in the electrical performance of macroscopic MPS‐based ICAs. This article deals with the electrical characterization of individual MPS using a nanoindentation‐based flat punch method, in which the resistance and the deformation of single MPS were monitored simultaneously. Four groups of silver‐coated polymer spheres (AgPS) with identical polymer cores but different silver coating thicknesses were tested. The resistance of AgPS decreases gradually with increasing deformation degree of particles, and increases when the deformation of particles is reduced. In addition, the resistance of individual AgPS is dependent on the physical properties of the silver coating, such as thickness, uniformity, and porosity. The thicker the silver coating is, the lower and more stable the resistance of AgPS is. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43764.