Evidence of flexoelectricity in polymer-dispersed liquid crystals

The linear electro-optic response of polymer-dispersed liquid crystal films has been studied. Micrometer-sized droplets of a cyanobiphenyl mixture (E7) were formed in a Norland Optical Adhesive 65 photopolymer by photopolymerization-induced phase separation. Samples with various average nematic droplet sizes were systematically investigated. The temperature dependence of the first-harmonic electro-optic spectra was obtained. Peculiar peaks and double peaks in the phase spectra were found at temperatures close to the clearing point. Samples with narrow distributions of droplet sizes were characterized by a double peak of the amplitude spectra in the low-frequency range. A flexoelectric origin of the first-harmonic spectra in confined nematic systems is discussed.     

Optical study of Ge x Sb20−x Te80 chalcogenide films

The optical properties of Ge _x Sb_20?x Te₈₀ films with x values of 15, 17, and 19, lying in the glass-forming region of the Ge–Sb–Te system, have been studied by spectroscopic ellipsometry in the spectral range 300–820 nm. The refractive index values were found to be 1.2–5.5 in the spectral range studied. With increasing Ge content in the films, a shift of the absorption edge toward higher energies was observed. This corresponds to an increase in the optical bandgap energy, from 0.58 to 0.76 eV.     

A smartphone-based virtual white cane

The objective of our research was to develop assistive technology for visually impaired people, with a high appreciation for the human potential to achieve, to learn, and to achieve goals. In this document, we describe a virtual white cane made of a combination of a Smartphone and a laser pointer. In our device, the laser pointer beam reflection is captured by the Smartphone camera. The distance from the virtual white cane to the reflection is computed through active triangulation. Then, a personalized vibration, the magnitude of which corresponds to distance, is generated in the Smartphone. In this way, the users receive information that could prevent collisions with obstacles in the environment. Our contributions include the development of a virtual white cane around a Smartphone and other off-the-shelf accessories and a methodology to provide personalized vibratory feedback to the user. Our experiments show that to navigate, our instrument is better option, in terms of travel time, that the use of the hands. However, the travel time is still better using a traditional white cane than our instrument.     

Large, huge or gigantic? Identifying and encoding intensity relations among adjectives in WordNet

We propose a new semantic relation for gradable adjectives in WordNet, which enriches the present, vague, similar relation with information on the degree or intensity with which different adjectives express a shared attribute. Using lexical-semantic patterns, we mine the Web for evidence of the relative strength of adjectives like “large”, “huge” and “gigantic” with respect to their attribute (“size”). The pairwise orderings we derive allow us to construct scales on which the adjectives are located. To represent the intensity relation among gradable adjectives in WordNet, we combine ordered scales with the current WordNet dumbbells based on the relation between a pair of central adjectives and a group of undifferentiated semantically similar adjectives. A new intensity relation links the adjectives in the dumbbells and their concurrent representation on scales. Besides capturing the semantics of gradable adjectives in a way that is both intuitively clear as well as consistent with corpus data, the introduction of an intensity relation would potentially result in several specific benefits for NLP.     

Hybrid Nanoscopy of Hybrid Nanomaterials

The combination of complementary techniques to characterize materials at the nanoscale is crucial to gain a more complete picture of their structure, a key step to design and fabricate new materials with improved properties and diverse functions. Here it is shown that correlative atomic force microscopy (AFM) and localization‐based super‐resolution microscopy is a useful tool that provides insight into the structure and emissive properties of fluorescent β‐lactoglobulin (βLG) amyloid‐like fibrils. These hybrid materials are made by functionalization of βLG with organic fluorophores and quantum dots, the latter being relevant for the production of 1D inorganic nanostructures templated by self‐assembling peptides. Simultaneous functionalization of βLG fibers by QD655 and QD525 allows for correlative AFM and two‐color super‐resolution fluorescence imaging of these hybrid materials. These experiments allow the combination of information about the topography and number of filaments that compose a fibril, as well as the emissive properties and nanoscale spatial distribution of the attached fluorophores. This study represents an important step forward in the characterization of multifunctionalized hybrid materials, a key challenge in nanoscience.     

Control Charts with Variable Dimension for Linear Combination of Poisson Variables

This article analyzes the simultaneous control of several correlated Poisson variables by using the Variable Dimension Linear Combination of Poisson Variables (VDLCP) control chart, which is a variable dimension version of the LCP chart. This control chart uses as test statistic, the linear combination of correlated Poisson variables in an adaptive way, i.e. it monitors either p₁ or p variables (p₁ < p) depending on the last statistic value. To analyze the performance of this chart, we have developed software that finds the best parameters, optimizing the out‐of‐control average run length (ARL) for a shift that the practitioner wishes to detect as quickly as possible, restricted to a fixed value for in‐control ARL. Markov chains and genetic algorithms were used in developing this software. The results show performance improvement compared to the LCP chart. Copyright © 2015 John Wiley & Sons, Ltd.     

Optimization of biochemical systems through mathematical programming: Methods and applications

In this work we present a general (mono and multiobjective) optimization framework for the technological improvement of biochemical systems. The starting point of the method is a mathematical model in ordinary differential equations (ODEs) of the investigated system, based on qualitative biological knowledge and quantitative experimental data. In the method we take advantage of the special structural features of a family of ODEs called power-law models to reduce the computational complexity of the optimization program. In this way, the genetic manipulation of a biochemical system to meet a certain biotechnological goal can be expressed as an optimization program with some desired properties such as linearity or convexity.The general method of optimization is presented and discussed in its linear and geometric programming versions. We furthermore illustrate the use of the method by several real case studies. We conclude that the technological improvement of microorganisms can be afforded using the combination of mathematical modelling and optimization. The systematic nature of this approach facilitates the redesign of biochemical systems and makes this a predictive exercise rather than a trial-and-error procedure.     

Two-stage cascaded classification approach based on genetic fuzzy learning for speech/music discrimination

Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents a robust and effective approach for speech/music discrimination, which relies on a two-stage cascaded classification scheme. The cascaded classification scheme is composed of a statistical pattern recognition classifier followed by a genetic fuzzy system. For the first stage of the classification scheme, other widely used classifiers, such as neural networks and support vector machines, have also been considered in order to assess the robustness of the proposed classification scheme. Comparison with well-proven signal features is also performed. In this work, the most commonly used genetic learning algorithms (Michigan and Pittsburgh) have been evaluated in the proposed two-stage classification scheme. The genetic fuzzy system gives rise to an improvement of about 4% in the classification accuracy rate. Experimental results show the good performance of the proposed approach with a classification accuracy rate of about 97% for the best trial.