Electrochemical oxidation of multi-wall carbon nanotubes

The electrochemical modification of carbon nanotube films (buckypapers) in three different electrolytes consisting of two acids and a basic solvent at very low concentrations was studied. The electrolysis was performed at 1 A up to a maximum of 12 h. Four different characterization techniques have been employed for assessing the effectiveness of the proposed process. The results presented are very encouraging for the development of electrochemical oxidation as the main surface modification method for carbon nanotubes. It was found that the use of nitric acid electrolyte leads to scalable and controllable oxidation as compared to the basic electrolyte which was also effective but appeared to damage the graphitic structure of nanotubes during longer treatments.     

Practical methods for competing risks data: a review

Competing risks data arise naturally in medical research, when subjects under study are at risk of more than one mutually exclusive event such as death from different causes. The competing risks framework also includes settings where different possible events are not mutually exclusive but the interest lies on the first occurring event. For example, in HIV studies where seropositive subjects are receiving highly active antiretroviral therapy (HAART), treatment interruption and switching to a new HAART regimen act as competing risks for the first major change in HAART. This article introduces competing risks data and critically reviews the widely used statistical methods for estimation and modelling of the basic (estimable) quantities of interest. We discuss the increasingly popular Fine and Gray model for subdistribution hazard of interest, which can be readily fitted using standard software under the assumption of administrative censoring. We present a simulation study, which explores the robustness of inference for the subdistribution hazard to the assumption of administrative censoring. This shows a range of scenarios within which the strictly incorrect assumption of administrative censoring has a relatively small effect on parameter estimates and confidence interval coverage. The methods are illustrated using data from HIV-1 seropositive patients from the collaborative multicentre study CASCADE (Concerted Action on SeroConversion to AIDS and Death in Europe).     

Automatic stress analysis from facial videos based on deep facial action units recognition

Pattern Analysis and Applications - Stress conditions are manifested in different human body’s physiological processes and the human face. Facial expressions are modelled consistently through...     

Enhancing and replacing spectral information with intermediate structural inputs: A case study on impervious surface detection

This paper assessed the incorporation of road structural information in the classification process of impervious surface areas. A multi-process classification model was adopted and it consisted of an a priori classifier and an a posteriori classifier. The role of the a priori classifier was to classify the relatively simple portions of the image. This partial classification acted as the basis for the production of linear features using an iterative Radon transform. Spatial statistics derived from the linear features led to road structural intermediate inputs (RSIIs) (for example, distance to the closest segment endpoint). RSIIs were integrated with spectral information on the remaining unclassified pixels and an assessment was done to evaluate whether they would improve a binary impervious classification task. The experimental results on a 2006 Landsat ETM+ image suggested that classification accuracy improved by 8.4% for the portion of the dataset classified with the a posteriori classifier and led to an improvement of 3.2% over the entire dataset. In addition, a more challenging and wide-reaching hypothesis was tested, namely whether RSIIs could completely replace spectral information in portions of the image instead of complementing it. Exclusive use of RSIIs matched or improved classification accuracy obtained solely from spectral information, even when more than half of the validation dataset was forwarded to the a posteriori classifier. This finding offers an important contribution to the remote sensing community, since the proposed methodology handles the missing spectral information problem through exclusive analysis of the given degraded image; no external information, such as spectral information from other times and/or vector data, is needed.     

Molecular/Nanostructured Functional Metal Oxide Stacks for Nanoscale Nanosecond Information Storage

The metal oxide heterostructures market is exponentially growing, adhering to the trend of achieving fabrication versatility on a vast range of nonconventional electromagnetic and optical properties. A high degree of substrate tolerance and solution‐phase growth potential promise low‐cost flexible electronics and silicon‐based process compatibility. A molecule‐based complex oxide nanostructured stack integrated in an electro‐optically operable nonvolatile two‐terminal capacitive memory element is proposed. The cell demonstrates a remarkably high > 7 V memory window and write–read times down to 10 ns, promising for reliable high‐speed storage. Molecular orbital occupancy through broadband optical stimulus enables simultaneous phononic addressing and boosts the written information amount by up to 37%, achieving 10+ years storage duration. The resulting nonvolatile memories are the first‐documented complementary metal oxide semiconductor (CMOS)‐compatible long‐term‐retention molecular capacitive cell of its kind, implementing inherent structure‐emerging heat management. Great potential emerges for numerous energy‐inspired innovations, enabling functional oxide–molecular hybrids exploitation as high‐end nonvolatile memory products.     

Component design and testing for a miniaturised autonomous sensor based on a nanowire materials platform

We present the design considerations of an autonomous wireless sensor and discuss the fabrication and testing of the various components including the energy harvester, the active sensing devices and the power management and sensor interface circuits. A common materials platform, namely, nanowires, enables us to fabricate state-of-the-art components at reduced volume and show chemical sensing within the available energy budget. We demonstrate a photovoltaic mini-module made of silicon nanowire solar cells, each of 0.5 mm² area, which delivers a power of 260 μW and an open circuit voltage of 2 V at one sun illumination. Using nanowire platforms two sensing applications are presented. Combining functionalised suspended Si nanowires with a novel microfluidic fluid delivery system, fully integrated microfluidic–sensor devices are examined as sensors for streptavidin and pH, whereas, using a microchip modified with Pd nanowires provides a power efficient and fast early hydrogen gas detection method. Finally, an ultra-low power, efficient solar energy harvesting and sensing microsystem augmented with a 6 mAh rechargeable battery allows for less than 20 μW power consumption and 425 h sensor operation even without energy harvesting.     

Fast parallel algorithms for graph similarity and matching

This paper addresses the problem of global graph alignment on supercomputer-class clusters. We define the alignment of two graphs, as a mapping of each vertex in the first graph to a unique vertex in the second graph so as to optimize a given similarity-based cost function.¹ Using a state of the art serial algorithm for the computation of vertex similarity scores called Network Similarity Decomposition (NSD), we derive corresponding parallel formulations. Coupling this parallel similarity algorithm with a parallel auction-based bipartite matching technique, we obtain a highly efficient and scalable graph matching pipeline. We validate the performance of our integrated approach on a large parallel platform and on diverse graph instances (including Protein Interaction, Wikipedia and Web networks). Experimental results demonstrate that our algorithms scale to large machine configurations (thousands of cores) and problem instances, enabling the alignment of networks of sizes two orders of magnitude larger than reported in the current literature.     

Uncontrolled Thyroid during Pregnancy Alters the Circulative and Exerted Metabolome

Normal levels of thyroid hormones (THs) are essential for a normal pregnancy outcome, fetal growth and the normal function of the central nervous system. Hypothyroidism, a common endocrine disorder during pregnancy, is a significant metabolic factor leading to cognitive impairments. It is essential to investigate whether patients with thyroid dysfunction may present an altered circulative and excreted metabolic profile, even after receiving treatment with thyroxine supplements. NMR metabolomics was employed to analyze 90 serum and corresponding colostrum samples. Parallel analyses of the two biological specimens provided a snapshot of the maternal metabolism through the excretive and circulating characteristics of mothers. The metabolomics data were analyzed by performing multivariate statistical, biomarker and pathway analyses. Our results highlight the impact of hypothyroidism on metabolites’ composition during pregnancy and lactation. Thyroid disorder causing metabolite fluctuations may lead to impaired lipid and glucose metabolic pathways as well as aberrant prenatal neurodevelopment, thus posing a background for the occurrence of metabolic syndrome or neurogenerative diseases later in life. This risk applies to not only untreated but also hypothyroid women under replacement therapy since our findings in both biofluids framed a different metabolic phenotype for the latter group, thus emphasizing the need to monitor women adequately after treatment initiation.     

Growth and Fabrication of High‐Quality Single Nanowire Devices with Radial p‐i‐n Junctions

Nanowires (NWs) with radial p‐i‐n junction have advantages, such as large junction area and small influence from the surface states, which can lead to highly efficient material use and good device quantum efficiency. However, it is difficult to make high‐quality core–shell NW devices, especially single NW devices. Here, the key factors during the growth and fabrication process that influence the quality of single core–shell p‐i‐n NW devices are studied using GaAs(P) NW photovoltaics as an example. By p‐doping and annealing, good ohmic contact is achieved on NWs with a diameter as small as 50–60 nm. Single NW photovoltaics are subsequently developed and a record fill factor of 80.5% is shown. These results bring valuable information for making single NW devices, which can further benefit the development of high‐density integration circuits.     

DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval

International Journal of Computer Vision - In this paper, we address the problem of high performance and computationally efficient content-based video retrieval in large-scale datasets. Current...