Analysing multiparticle quantum states

The analysis of multiparticle quantum states is a central problem in quantum information processing. This task poses several challenges for experimenters and theoreticians. We give an overview over current problems and possible solutions concerning systematic errors of quantum devices, the reconstruction of quantum states, and the analysis of correlations and complexity in multiparticle density matrices.Comment: 20 pages, 4 figures, prepared for proceedings of the "Quantum [Un]speakables II" conference (Vienna, 2014

Melt blending and characterization of carbon nanoparticles-filled thermoplastic polyurethane elastomers

In this work, thermoplastic polyurethane (TPU) elastomers reinforced with carbon nanosized particles were produced by a special melt blending technique. A TPU was melt blended with high-structured carbon black and carbon nanofibres (1 wt%). A miniature asymmetric batch mixer, which applies high shear levels to the melt, ensured good particles dispersion. The TPU material systems were then thoroughly characterized using thermogravimetric analysis, differential scanning calorimetry, tensile mechanical testing, electrical resistance measurements and flammability tests. The different nanofillers exhibited different influences on the TPU properties, these materials featuring interesting and improved multifunctional behaviours, with high propensity for large deformation sensors applications.This work was supported by FCT – Portuguese Foundation for Science and Technology through projects NANOSens – PTDC/CTM/73465/2006

The application of digital volume correlation (DVC) to evaluate strain predictions generated by finite element models of the osteoarthritic humeral head

Continuum-level finite element models (FEMs) of the humerus offer the ability to evaluate joint replacement designs preclinically; however, experimental validation of these models is critical to ensure accuracy. The objective of the current study was to quantify experimental full-field strain magnitudes within osteoarthritic (OA) humeral heads by combining mechanical loading with volumetric microCT imaging and digital volume correlation (DVC). The experimental data was used to evaluate the accuracy of corresponding FEMs. Six OA humeral head osteotomies were harvested from patients being treated with total shoulder arthroplasty and mechanical testing was performed within a microCT scanner. MicroCT images (33.5 µm isotropic voxels) were obtained in a pre- and post-loaded state and BoneDVC was used to quantify full-field experimental strains (≈ 1 mm nodal spacing, accuracy = 351 µstrain, precision = 518 µstrain). Continuum-level FEMs with two types of boundary conditions (BCs) were simulated: DVC-driven and force-driven. Accuracy of the FEMs was found to be sensitive to the BC simulated with better agreement found with the use of DVC-driven BCs (slope = 0.83, r2 = 0.80) compared to force-driven BCs (slope = 0.22, r2 = 0.12). This study quantified mechanical strain distributions within OA trabecular bone and demonstrated the importance of BCs to ensure the accuracy of predictions generated by corresponding FEMs

Chalcogenide Glass-on-Graphene Photonics

Two-dimensional (2-D) materials are of tremendous interest to integrated photonics given their singular optical characteristics spanning light emission, modulation, saturable absorption, and nonlinear optics. To harness their optical properties, these atomically thin materials are usually attached onto prefabricated devices via a transfer process. In this paper, we present a new route for 2-D material integration with planar photonics. Central to this approach is the use of chalcogenide glass, a multifunctional material which can be directly deposited and patterned on a wide variety of 2-D materials and can simultaneously function as the light guiding medium, a gate dielectric, and a passivation layer for 2-D materials. Besides claiming improved fabrication yield and throughput compared to the traditional transfer process, our technique also enables unconventional multilayer device geometries optimally designed for enhancing light-matter interactions in the 2-D layers. Capitalizing on this facile integration method, we demonstrate a series of high-performance glass-on-graphene devices including ultra-broadband on-chip polarizers, energy-efficient thermo-optic switches, as well as graphene-based mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators

Recycling aluminium AA6061 chips with reinforced boron carbide (B4C) and zirconia (ZrO2) particles via hot extrusion

Compared to the recycling process by remelting, hot extrusion significantly reduces the energy consumption and CO2 emission and ensures good mechanical and microstructural properties. This study investigates the effects of reinforcing aluminium AA6061 chips with mixed boron carbide (B4C) and zirconia (ZrO2) particles by employing a design of experiment (DOE) under 550 °C processing temperature and three hours preheating time. The findings showed that compressive strength (CS) and hardness increased with up to 5% added particles, and beyond 5%, the yielded values decreased because of materials agglomeration. However, the decreasing density was dependent on the addition of ZrO2 particles. The distribution of particles with different volume fractions of mixed particles was investigated by employing SEM, AFM, and EDS tests. Thus, the process can produce a net shape structure that utilises material-bonding consolidation to provide sufficient support to reuse the recovered materials in engineering applications, such as in the automotive industry

Feature selection using Haar wavelet power spectrum

BACKGROUND: Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and video processing utilize them well. Wavelets, one of such techniques, have the potential to be utilized in feature selection method. The aim of this paper is to assess the capability of Haar wavelet power spectrum in the problem of clustering and gene selection based on expression data in the context of disease classification and to propose a method based on Haar wavelet power spectrum. RESULTS: Haar wavelet power spectra of genes were analysed and it was observed to be different in different diagnostic categories. This difference in trend and magnitude of the spectrum may be utilized in gene selection. Most of the genes selected by earlier complex methods were selected by the very simple present method. Each earlier works proved only few genes are quite enough to approach the classification problem [1]. Hence the present method may be tried in conjunction with other classification methods. The technique was applied without removing the noise in data to validate the robustness of the method against the noise or outliers in the data. No special softwares or complex implementation is needed. The qualities of the genes selected by the present method were analysed through their gene expression data. Most of them were observed to be related to solve the classification issue since they were dominant in the diagnostic category of the dataset for which they were selected as features. CONCLUSION: In the present paper, the problem of feature selection of microarray gene expression data was considered. We analyzed the wavelet power spectrum of genes and proposed a clustering and feature selection method useful for classification based on Haar wavelet power spectrum. Application of this technique in this area is novel, simple, and faster than other methods, fit for a wide range of data types. The results are encouraging and throw light into the possibility of using this technique for problem domains like disease classification, gene network identification and personalized drug design

Decreased expression of dual-specificity phosphatase 9 is associated with poor prognosis in clear cell renal cell carcinoma

Background: The molecular mechanisms involved in the development and progression of clear cell renal cell carcinomas (ccRCCs) are poorly understood. The objective of this study was to analyze the expression of dual-specificity phosphatase 9 (DUSP-9) and determine its clinical significance in human ccRCCs. Methods: The expression of DUSP-9 mRNA was determined in 46 paired samples of ccRCCs and adjacent normal tissues by using real-time qPCR. The expression of the DUSP-9 was determined in 211 samples of ccRCCs and 107 paired samples of adjacent normal tissues by immunohistochemical analysis. Statistical analysis was performed to define the relationship between the expression of DUSP-9 and the clinical features of ccRCC. Results: The mRNA level of DUSP-9, which was determined by real-time RT-PCR, was found to be significantly lower in tumorous tissues than in the adjacent non-tumorous tissues (p < 0.001). An immunohistochemical analysis of 107 paired tissue specimens showed that the DUSP-9 expression was lower in tumorous tissues than in the adjacent non-tumorous tissues (p < 0.001). Moreover, there was a significant correlation between the DUSP-9 expression in ccRCCs and gender (p = 0.031), tumor size (p = 0.001), pathologic stage (p = 0.001), Fuhrman grade (p = 0.002), T stage (p = 0.001), N classification (p = 0.012), metastasis (p = 0.005), and recurrence (p < 0.001). Patients with lower DUSP-9 expression had shorter overall survival time than those with higher DUSP-9 expression (p < 0.001). Multivariate analysis indicated that low expression of the DUSP-9 was an independent predictor for poor survival of ccRCC patients. Conclusion: To our knowledge, this is the first study that determines the relationship between DUSP-9 expression and prognosis in ccRCC. We found that decreased expression of DUSP-9 is associated with poor prognosis in ccRCC. DUSP-9 may represent a novel and useful prognostic marker for ccRCC

Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds

Development and application of the data envelopment analysis (DEA) method, have been the subject of numerous reviews. In this paper, we consider the papers that apply DEA methods specifically to financial services, or which use financial services data to experiment with a newly introduced DEA model. We examine 620 papers published in journals indexed in the Web of Science database, from 1985 to April 2016. We analyse the sample applying citations network analysis. This paper investigates the DEA method and its applications in financial services. We analyse the diffusion of DEA in three sub-samples: (1) banking groups, (2) money market funds, and (3) insurance groups by identifying the main paths, that is, the main flows of the ideas underlying each area of research. This allows us to highlight the main approaches, models and efficiency types used in each research areas. No unique methodological preference emerges within these areas. Innovations in the DEA methodologies (network models, slacks based models, directional distance models and Nash bargaining game) clearly dominate recent research. For each subsample, we describe the geographical distribution of these studies, and provide some basic statistics related to the most active journals and scholars

The deuteron: structure and form factors

A brief review of the history of the discovery of the deuteron in provided. The current status of both experiment and theory for the elastic electron scattering is then presented.Comment: 80 pages, 33 figures, submited to Advances in Nuclear Physic