Optical gas sensing properties of nanoporous Nb2O5 films

Nanoporous Nb2O5 has been previously demonstrated to be a viable electrochromic material with strong intercalation characteristics. Despite showing such promising properties, its potential for optical gas sensing applications, which involves the production of ionic species such as H+, has yet to be explored. Nanoporous Nb2O5 can accommodate a large amount of H+ ions in a process that results in an energy bandgap change of the material, which induces an optical response. Here, we demonstrate the optical hydrogen gas (H¬2) sensing capability of nanoporous anodic Nb2O5 with a large surface-to-volume ratio prepared via a high temperature anodization method. The large active surface area of the film provides enhanced pathways for efficient hydrogen adsorption and dissociation, which are facilitated by a thin layer of Pt catalyst. We show that the process of H2 sensing causes optical modulations that are investigated in terms of response magnitudes and dynamics. The optical modulations induced by the intercalation process and sensing properties of nanoporous anodic Nb2O5 shown in this work can potentially be used for future optical gas sensing systems.

Multi-action recognition via stochastic modelling of optical flow and gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Multi-parametric hydrogels support 3D in vitro bioengineered microenvironment models of tumour angiogenesis

Tumour microenvironment greatly influences the development and metastasis of cancer progression. The development of three dimensional (3D) culture models which mimic that displayed in vivo can improve cancer biology studies and accelerate novel anticancer drug screening. Inspired by a systems biology approach, we have formed 3D in vitro bioengineered tumour angiogenesis microenvironments within a glycosaminoglycan-based hydrogel culture system. This microenvironment model can routinely recreate breast and prostate tumour vascularisation. The multiple cell types cultured within this model were less sensitive to chemotherapy when compared with two dimensional (2D) cultures, and displayed comparative tumour regression to that displayed in vivo. These features highlight the use of our in vitro culture model as a complementary testing platform in conjunction with animal models, addressing key reduction and replacement goals of the future. We anticipate that this biomimetic model will provide a platform for the in-depth analysis of cancer development and the discovery of novel therapeutic targets.

Use of parametric modelling and climate-based metrics for the efficient design of daylight strategies in buildings with complex geometries

The purpose of this research is to assess daylight performance of buildings with climatic responsive envelopes with complex geometry that integrates shading devices in the façade. To this end two case studies are chosen due to their complex geometries and integrated daylight devices. The effect of different parameters of the daylight devices is analysed through Climate base daylight metrics.

The effect of BCMO1 gene variants on macular pigment optical density in young healthy Caucasians

Background Serum lutein (L) and zeaxanthin (Z) positively correlate with macular pigment optical density (MPOD), hence the latter is a valuable indirect tool for measuring L and Z content in the macula. L and Z have been attributed antioxidant capacity and protection from certain retinal diseases but their uptake within the eye is thought to depend on genetic, age and environmental factors. In particular gene variants within beta-carotene monooxygenase (BCMO1) are thought to modulate MPOD in the macula. Objectives: To determine the effect of BCMO1 single nucleotide polymorphisms (SNPs) rs11645428, rs6420424 and rs6464851 on macular pigment optical density (MPOD) in a cohort of young healthy participants of Caucasian origin with normal ocular health. Design In this cohort study, MPOD was assessed in 46 healthy participants (22 male and 24 female) with a mean age of 24 ± 4.0 years (range 19-33). The three SNPs, rs11645428, rs6420424, rs6564851 that have established associations with MPOD were determined using MassEXTEND (hME) Sequenom assay. One-way analysis of variance (ANOVA) was performed on groups segregated into homozygous and heterozygous BCMO1 genotypes. Correlations between body mass index (BMI), iris colour, gender, central retinal thickness (CRT), diet and MPOD were investigated. Results MPOD did not significantly vary with BCMO1 rs11645428 (F2,41 = 0.700, p = 0.503), rs6420424 (F2,41 = 0.210, p = 0.801) nor rs6464851 homozygous or heterozygous genotypes (F2,41 = 0,13, p = 0.88), in this young healthy cohort. The combination of these three SNPs into triple genotypes based on plasma conversion efficiency did not affect MPOD (F2,41 = 0.07, p = 0.9). There was a significant negative correlation with MPOD and central retinal thickness (r = - 0.39, p = 0.01) but no significant correlation between BMI, iris colour, gender and MPOD. Conclusion Our results indicate that macular pigment deposition within the central retina is not dependent on BCMO1 gene variants in young healthy people. We propose that MPOD is saturated in younger persons and/or other gene variant combinations determine its deposition.

A LIN inspired optical bus for signal isolation in multilevel or modular power electronic converters

Proposed in this paper is a low-cost, half-duplex optical communication bus for control signal isolation in modular or multilevel power electronic converters. The concept is inspired by the Local Interconnect Network (LIN) serial network protocol as used in the automotive industry. The proposed communications bus utilises readily available optical transceivers and is suitable for use with low-cost microcontrollers for distributed control of multilevel converters. As a signal isolation concept, the proposed optical bus enables very high cell count modular multilevel cascaded converters (MMCCs) for high-bandwidth, high-voltage and high-power applications. Prototype hardware is developed and the optical bus concept is validated experimentally in a 33-level MMCC converter operating at 120 Vrms and 60 Hz.

Stochastic analysis of a radial-inflow turbine in the presence of parametric uncertainties

This paper presents an uncertainty quantification study of the performance analysis of the high pressure ratio single stage radial-inflow turbine used in the Sundstrand Power Systems T-100 Multi-purpose Small Power Unit. A deterministic 3D volume-averaged Computational Fluid Dynamics (CFD) solver is coupled with a non-statistical generalized Polynomial Chaos (gPC) representation based on a pseudo-spectral projection method. One of the advantages of this approach is that it does not require any modification of the CFD code for the propagation of random disturbances in the aerodynamic and geometric fields. The stochastic results highlight the importance of the blade thickness and trailing edge tip radius on the total-to-static efficiency of the turbine compared to the angular velocity and trailing edge tip length. From a theoretical point of view, the use of the gPC representation on an arbitrary grid also allows the investigation of the sensitivity of the blade thickness profiles on the turbine efficiency. The gPC approach is also applied to coupled random parameters. The results show that the most influential coupled random variables are trailing edge tip radius coupled with the angular velocity.

Motor response suppression and the prepotent tendency to respond: A parametric fMRI study

In the present study we utilised functional magnetic resonance imaging (fMRI) to examine cerebral activation during performance of a classic motor task in which response suppression load was parametrically varied. Linear increases in activity were observed in a distributed network of regions across both cerebral hemispheres, although with more extensive involvement of the right prefrontal cortex. Activated regions included prefrontal, parietal and occipitotemporal cortices. Decreasing activation was similarly observed in a distributed network of regions. These response forms are discussed in terms of an increasing requirement for visual cue discrimination and suppression/selection of motor responses, and a decreasing probability of the occurrence of non-target stimuli and attenuation of a prepotent tendency to respond. The results support recent proposals for a dominant role for the right-hemisphere in performance of motor response suppression tasks that emphasise the importance of the right prefrontal cortex.

Non-parametric consistency test for multiple-sensing-modality data fusion

Fusing data from multiple sensing modalities, e.g. laser and radar, is a promising approach to achieve resilient perception in challenging environmental conditions. However, this may lead to \emph{catastrophic fusion} in the presence of inconsistent data, i.e. when the sensors do not detect the same target due to distinct attenuation properties. It is often difficult to discriminate consistent from inconsistent data across sensing modalities using local spatial information alone. In this paper we present a novel consistency test based on the log marginal likelihood of a Gaussian process model that evaluates data from range sensors in a relative manner. A new data point is deemed to be consistent if the model statistically improves as a result of its fusion. This approach avoids the need for absolute spatial distance threshold parameters as required by previous work. We report results from object reconstruction with both synthetic and experimental data that demonstrate an improvement in reconstruction quality, particularly in cases where data points are inconsistent yet spatially proximal.

Single layer lead iodide: Computational exploration of structural, electronic and optical properties, strain induced band modulation and the role of spin-orbital-coupling

Graphitic like layered materials exhibit intriguing electronic structures and thus the search for new types of two-dimensional (2D) monolayer materials is of great interest for developing novel nano-devices. By using density functional theory (DFT) method, here we for the first time investigate the structure, stability, electronic and optical properties of monolayer lead iodide (PbI2). The stability of PbI2 monolayer is first confirmed by phonon dispersion calculation. Compared to the calculation using generalized gradient approximation, screened hybrid functional and spin–orbit coupling effects can not only predicts an accurate bandgap (2.63 eV), but also the correct position of valence and conduction band edges. The biaxial strain can tune its bandgap size in a wide range from 1 eV to 3 eV, which can be understood by the strain induced uniformly change of electric field between Pb and I atomic layer. The calculated imaginary part of the dielectric function of 2D graphene/PbI2 van der Waals type hetero-structure shows significant red shift of absorption edge compared to that of a pure monolayer PbI2. Our findings highlight a new interesting 2D material with potential applications in nanoelectronics and optoelectronics.

Bayesian parametric bootstrap for models with intractable likelihoods

In this paper it is demonstrated how the Bayesian parametric bootstrap can be adapted to models with intractable likelihoods. The approach is most appealing when the semi-automatic approximate Bayesian computation (ABC) summary statistics are selected. After a pilot run of ABC, the likelihood-free parametric bootstrap approach requires very few model simulations to produce an approximate posterior, which can be a useful approximation in its own right. An alternative is to use this approximation as a proposal distribution in ABC algorithms to make them more efficient. In this paper, the parametric bootstrap approximation is used to form the initial importance distribution for the sequential Monte Carlo and the ABC importance and rejection sampling algorithms. The new approach is illustrated through a simulation study of the univariate g-and- k quantile distribution, and is used to infer parameter values of a stochastic model describing expanding melanoma cell colonies.

Parametric study on cement-based soft-hard-soft (SHS) multi-layer composite pavement against blast load

An innovative cement-based soft-hard-soft (SHS) multi-layer composite has been developed for protective infrastructures. Such composite consists of three layers including asphalt concrete (AC), high strength concrete (HSC), and engineered cementitious composites (ECC). A three dimensional benchmark numerical model for this SHS composite as pavement under blast load was established using LSDYNA and validated by field blast test. Parametric studies were carried out to investigate the influence of a few key parameters including thickness and strength of HSC and ECC layers, interface properties, soil conditions on the blast resistance of the composite. The outcomes of this study also enabled the establishment of a damage pattern chart for protective pavement design and rapid repair after blast load. Efficient methods to further improve the blast resistance of the SHS multi-layer pavement system were also recommended.

Multimodal polymer nanoparticles with combined 19F magnetic resonance and optical detection for tunable, targeted, multimodal imagingin vivo

Understanding the complex nature of diseased tissue in vivo requires development of more advanced nanomedicines, where synthesis of multifunctional polymers combines imaging multimodality with a biocompatible, tunable, and functional nanomaterial carrier. Here we describe the development of polymeric nanoparticles for multimodal imaging of disease states in vivo. The nanoparticle design utilizes the abundant functionality and tunable physicochemical properties of synthetically robust polymeric systems to facilitate targeted imaging of tumors in mice. For the first time, high-resolution 19F/1H magnetic resonance imaging is combined with sensitive and versatile fluorescence imaging in a polymeric material for in vivo detection of tumors. We highlight how control over the chemistry during synthesis allows manipulation of nanoparticle size and function and can lead to very high targeting efficiency to B16 melanoma cells, both in vitro and in vivo. Importantly, the combination of imaging modalities within a polymeric nanoparticle provides information on the tumor mass across various size scales in vivo, from millimeters down to tens of micrometers.

Nitrogen-doped graphene films from chemical vapor deposition of pyridine: Influence of process parameters on the electrical and optical properties

Graphene films were produced by chemical vapor deposition (CVD) of pyridine on copper substrates. Pyridine-CVD is expected to lead to doped graphene by the insertion of nitrogen atoms in the growing sp2 carbon lattice, possibly improving the properties of graphene as a transparent conductive film. We here report on the influence that the CVD parameters (i.e., temperature and gas flow) have on the morphology, transmittance, and electrical conductivity of the graphene films grown with pyridine. A temperature range between 930 and 1070 °C was explored and the results were compared to those of pristine graphene grown by ethanol-CVD under the same process conditions. The films were characterized by atomic force microscopy, Raman and X-ray photoemission spectroscopy. The optical transmittance and electrical conductivity of the films were measured to evaluate their performance as transparent conductive electrodes. Graphene films grown by pyridine reached an electrical conductivity of 14.3 × 105 S/m. Such a high conductivity seems to be associated with the electronic doping induced by substitutional nitrogen atoms. In particular, at 930 °C the nitrogen/carbon ratio of pyridine-grown graphene reaches 3%, and its electrical conductivity is 40% higher than that of pristine graphene grown from ethanol-CVD.