Optical solution for bounded NP-complete problems

We present a new optical method for solving bounded (input-length-restricted) NP-complete combinatorial problems. We have chosen to demonstrate the method with an NP-complete problem called the traveling salesman problem (TSP). The power of optics in this method is realized by using a fast matrix-vector multiplication between a binary matrix, representing all feasible TSP tours, and a gray-scale vector, representing the weights among the TSP cities. The multiplication is performed optically by using an optical correlator. To synthesize the initial binary matrix representing all feasible tours, an efficient algorithm is provided. Simulations and experimental results prove the validity of the new method.     

A perspective on the optimisation of hard carbon and related coatings for engineering applications

Hard carbon coatings hold the key to improved performance for many types of products. However the achievement of these improvements requires the selection of the appropriate type of carbon coating and therefore the correct process and appropriate deposition parameters. The huge range of properties achievable in carbon coatings is mainly due to the ability of carbon to form different types of interatomic bonds, to take up different sites, and to adopt different structures. In addition to intrinsic material properties, other factors must also be considered for each application, such as the adhesion level achievable and coating cost. This complex situation explains why the number of applications for hard carbon films is still more limited than originally expected. Despite the considerable progress achieved during the last decade in hard coating technologies, practical results often appear conflicting, with differences in properties occurring even within the same types of coatings. Furthermore, the many different deposition systems and processes which have been developed introduce further complications in regard to (for example) achievable coating uniformity and deposition rates. Thus, there is often confusion in the use of certain fundamental principles, especially regarding the growth mechanisms and the effects which produce more dense homogeneous and stable coating materials. This is especially true for the improved properties of tetrahedral amorphous carbon films, which are different from previously reported diamond-like carbon materials, and can be created by adapting and improving existing industrial processes, to offer advantages compared to earlier coatings, and hence possibilities for important new applications. This paper discusses issues relating to intrinsic material properties, and practical aspects such as adhesion, to provide a framework for the development, selection and use of hard carbon coatings in practical situations.     

Ultra-Small YPO4-YAG:Ce Composite Nanophosphors with a Photoluminescence Quantum Yield Exceeding 50%

Synthesis of high quality colloidal Cerium(III) doped yttrium aluminum garnet (Y₃Al₅O₁₂:Ce³⁺, “YAG:Ce”) nanoparticles (NPs) meeting simultaneously both ultra-small size and high photoluminescence (PL) performance is challenging, as generally a particle size/PL trade-off has been observed for this type of nanomaterials. The glycothermal route is capable to yield ultra-fine crystalline colloidal YAG:Ce nanoparticles with a particle size as small as 10 nm but with quantum yield (QY) no more than 20%. In this paper, the first ultra-small YPO₄-YAG:Ce nanocomposite phosphor particles having an exceptional QY-to-size performance with an QY up to 53% while maintaining the particle size ≈10 nm is reported. The NPs are produced via a phosphoric acid- and extra yttrium acetate-assisted glycothermal synthesis route. Localization of phosphate and extra yttrium entities with respect to cerium centers in the YAG host has been determined by fine structural analysis techniques such as X-ray diffration (XRD), solid state nuclear magnetic resonance (NMR), and high resolution scanning transmission electron microscopy (HR-STEM), and shows distinct YPO₄ and YAG phases. Finally, a correlation between the additive-induced physico-chemical environment change around cerium centers and the increasing PL performance has been suggested based on electron paramagnetic resonance (EPR), X-ray photoelectron spectrometry (XPS) data, and crystallographic simulation studies.     

Radiation damage to DNA-protein specific complexes: estrogen response element-estrogen receptor complex

The exposure of a DNA-protein regulatory complex to ionising radiation induces damage to both partner biomolecules and thus can affect its functioning. Our study focuses on a complex formed by the estrogen response element (ERE) DNA and the recombinant human estrogen receptor alpha (ER), which mediates the signalling of female sex hormones, estrogens. The method of native polyacrylamide retardation gel electrophoresis is used to study the stability of the complex under irradiation by low LET radiation ((60)Co gamma rays) and the ability of the separately irradiated partners to form complexes. The relative probabilities of ERE DNA strand breakage and base damages as well as the probabilities of damages to the ER binding domain are calculated using the Monte Carlo method-based model RADACK.     

Finite element evaluation of free-edge singular stress fields in anisotropic materials

A finite element formulation based on the work of Yamada and Okumura¹⁴ is presented to determine the order of singularity and angular variation of the stress and displacement fields surrounding a singular point on a free edge of anisotropic materials. Emphasis is placed on the computational aspects of this method when applied to configurations including fully bonded multi-material junctions intersecting a free edge as well as materials containing cracks intersecting a free edge. The study shows that the singularity of the three-dimensional stress field may be accurately determined with a relatively small number of elements only when a proper level of numerical integration is used. The method is applied to isotropic and orthotropic materials with a crack intersecting a free edge and an anisotropic three-material junction intersecting a free edge. The efficiency and accuracy of the method indicates it could be used to develop a numerical solution for the singular field that could in turn be used to create free-edge enriched finite elements.     

Mass spectrometry investigations on electrolyte degradation products for the development of nanocomposite electrodes in lithium ion batteries

In the continuing challenge to find new routes to improve the performance of commercial lithium ion batteries cycling in alkyl carbonate-based electrolyte solutions, original designs, and new electrode materials are under active worldwide investigation. Our group has focused on the electrochemical behavior of a new generation of nanocomposite electrodes showing improved capacities (up to 3 times the capacity of conventional electrode materials). However, moving down to "nanometric-scale" active materials leads to a significant increase in electrolyte degradation, compared to that taking place within commercial batteries. Postmortem electrolyte studies on experimental coin cells were conducted to understand the degradation mechanisms. Structural analysis of the organic degradation products were investigated using a combination of complementary high-resolution mass spectrometry techniques: desorption under electron impact, electrospray ionization, and gas chromatography coupled to a mass spectrometer equipped with electron impact and chemical ionization ion sources. Numerous organic degradation products such as ethylene oxide oligomers (with methyl, hydroxyl, phosphate, and methyl carbonate endings) have been characterized. In light of our findings, possible chemical or electrochemical pathways are proposed to account for their formation. A thorough knowledge of these degradation mechanisms will enable us to propose new electrolyte formulations to optimize nanocomposite-based lithium ion battery performance.     

Microscopic and spectroscopic characterization of paintbrush-like single-walled carbon nanotubes

Understanding and controlling the chemical reactivity of carbon nanotubes (CNTs) is a fundamental requisite to prepare novel nanoscopic structures with practical uses in materials applications. Here, we present a comprehensive microscopic and spectroscopic characterization of carbon nanotubes which have been chemically modified. Specifically, scanning tunneling microscopy (STM) investigations of short-oxidized single-walled carbon nanotubes (SWNTs) functionalized with aliphatic chains via amide reaction reveal the presence of bright lumps both on the sidewalls and at the tips. The functionalization pattern is consistent with the oxidation reaction which mainly occurs at the nanotube tips. Thermogravimetric analysis (TGA), steady-state electronic absorption (UV-vis-NIR), and Raman spectroscopic studies confirm the STM observations.     

Prevalence and predictors of posttraumatic stress in women undergoing an ovarian cancer investigation.

This study assessed posttraumatic stress disorder (PTSD) in women being investigated for an ovarian cancer diagnosis to determine prevalence and factors predicting PTSD in these patients. Participants (N = 75) were recruited from the Princess Margaret Hospital in Toronto, Ontario, after their initial clinic appointment and given a prediagnostic assessment that included measures of PTSD, depression, stress and pain. One month later, patients received an identical postdiagnostic assessment. No cases of clinical PTSD were detected, although 13.6% of participants were identified with subsyndromal PTSD. Multiple regression analyses showed that those participants reporting significant baseline depressive symptoms, definitively diagnosed with ovarian cancer, and with shorter treatment wait times were more likely to have a significant increase in PTSD symptoms. Supportive interventions aimed at reducing PTSD symptoms, launched prior to an ovarian cancer diagnosis, might optimally be directed at patients with baseline depressive symptoms and those with shorter treatment wait times. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Shape-driven segmentation of the arterial wall in intravascular ultrasound images

Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3-D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a nonparametric probability-density-based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach.     

Collision Avoidance Using a Cerebellar Model Arithmetic Computer Neural Network

Avoiding collisions with obstacles in a clustered environment is a difficult task for autonomous vehicles. Deterministic algorithms cannot address all scenarios encountered and may fail to perform in dynamically changing environments. Neural networks, owing to their ability to map complex relationships between multiple input-output patterns, can learn the task of maneuvering around and in-between obstacles to reach a goal state. The Cerebellar Model Arithmetic Computer (CMAC) neural network in particular is based on a model of human sensory motor responses and can efficiently model responsive control actions. A CMAC neural network controller was developed and examined, in simulation, for its suitability to capture a driver's function of steering and braking. The performance of the controller was tested in a simulation of a moving platform (vehicle) encountering obstacles of various shapes, whereas the CMAC was trained only with limited shapes and scenarios. Preliminary simulation results have shown the CMAC's ability to successfully generalize its learned patterns to avoid obstacles after only a few training sessions. The CMAC output is generated in a computationally efficient manner with physically and economically realizable memory sizes. Therefore, real-time hardware implementation of the controller is feasible. This research demonstrates that the method has the ability to accommodate more complex scenarios.