Scheduling with step-improving processing times

2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

First-principles calculations and experimental studies of: XYZ 2 thermoelectric compounds: Detailed analysis of van der Waals interactions

First-principles calculations can accelerate the search for novel high-performance thermoelectric materials. However, the prediction of the thermoelectric properties is strongly dependent on the approximations used for the calculations. Here, thermoelectric properties were calculated with different computational approximations (i.e., PBE-GGA, HSE06, spin-orbit coupling and DFT-D3) for three layered XYZ2 compounds (TmAgTe2, YAgTe2, and YCuTe2). In addition to the computations, the structural, electrical and thermal properties of these compounds were measured experimentally and compared to the computations. An enhanced prediction of the crystal structure and heat capacity was achieved with the inclusion of van der Waals interactions due to more accurate modeling of the interatomic forces. In particular, a large shift of the acoustic phonons and low-frequency optical phonons to lower frequencies was observed from the dispersion-optimized structure. From the phonon dispersion curves of these compounds, the ultralow thermal conductivity in the investigated XYZ2 compounds could be described by a recent developed minimum thermal conductivity model. For the prediction of the electrical conductivity, a temperature-dependent relaxation time was used, and it was limited by acoustic phonons. While HSE06 has only a small influence on the electrical properties due to a computed band gap energy of >0.25 eV, the inclusion of both van der Waals interactions and spin-orbit coupling leads to a more accurate band structure, resulting in better prediction of electrical properties. Furthermore, the experimental thermoelectric properties of YAgTe2, TmAg0.95Zn0.05Te2 and TmAg0.95Mg0.05Te2 were measured, showing an increase in zT of TmAg0.95Zn0.05Te2 by more than 35% (zT = 0.47 ± 0.12) compared to TmAgTe2

Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding

This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images, with pixel-level semantic annotations for $16$ images with dense fog. Our experiments show that 1) our fog simulation slightly outperforms a state-of-the-art competing simulation with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly by leveraging unlabeled real foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201

Brain endothelial miR-146a negatively modulates T-cell adhesion through repressing multiple targets to inhibit NF-kappa B activation

Pro-inflammatory cytokine-induced activation of nuclear factor, NF-κB has an important role in leukocyte adhesion to, and subsequent migration across, brain endothelial cells (BECs), which is crucial for the development of neuroinflammatory disorders such as multiple sclerosis (MS). In contrast, microRNA-146a (miR-146a) has emerged as an anti-inflammatory molecule by inhibiting NF-κB activity in various cell types, but its effect in BECs during neuroinflammation remains to be evaluated. Here, we show that miR-146a was upregulated in microvessels of MS-active lesions and the spinal cord of mice with experimental autoimmune encephalomyelitis. In vitro, TNFα and IFNγ treatment of human cerebral microvascular endothelial cells (hCMEC/D3) led to upregulation of miR-146a. Brain endothelial overexpression of miR-146a diminished, whereas knockdown of miR-146a augmented cytokine-stimulated adhesion of T cells to hCMEC/D3 cells, nuclear translocation of NF-κB, and expression of adhesion molecules in hCMEC/D3 cells. Furthermore, brain endothelial miR-146a modulates NF-κB activity upon cytokine activation through targeting two novel signaling transducers, RhoA and nuclear factor of activated T cells 5, as well as molecules previously identified, IL-1 receptor-associated kinase 1, and TNF receptor-associated factor 6. We propose brain endothelial miR-146a as an endogenous NF-κB inhibitor in BECs associated with decreased leukocyte adhesion during neuroinflammation. </p

Approaching Theoretical Performances of Electrocatalytic Hydrogen Peroxide Generation by Cobalt-Nitrogen Moieties

Electrocatalytic oxygen reduction reaction (ORR) has been intensively studied for environmentally benign applications. However, insufficient understanding of ORR 2 e−-pathway mechanism at the atomic level inhibits rational design of catalysts with both high activity and selectivity, causing concerns including catalyst degradation due to Fenton reaction or poor efficiency of H2O2 electrosynthesis. Herein we show that the generally accepted ORR electrocatalyst design based on a Sabatier volcano plot argument optimises activity but is unable to account for the 2 e−-pathway selectivity. Through electrochemical and operando spectroscopic studies on a series of CoNx/carbon nanotube hybrids, a construction-driven approach based on an extended “dynamic active site saturation” model that aims to create the maximum number of 2 e− ORR sites by directing the secondary ORR electron transfer towards the 2 e− intermediate is proven to be attainable by manipulating O2 hydrogenation kinetics

Functional organisation for verb generation in children with developmental language disorder

Developmental language disorder (DLD) is characterised by difficulties in learning one's native language for no apparent reason. These language difficulties occur in 7% of children and are known to limit future academic and social achievement. Our understanding of the brain abnormalities associated with DLD is limited. Here, we used a simple four-minute verb generation task (children saw a picture of an object and were instructed to say an action that goes with that object) to test children between the ages of 10–15 years (DLD N = 50, typically developing N = 67). We also tested 26 children with poor language ability who did not meet our criteria for DLD. Contrary to our registered predictions, we found that children with DLD did not have (i) reduced activity in language relevant regions such as the left inferior frontal cortex; (ii) dysfunctional striatal activity during overt production; or (iii) a reduction in left-lateralised activity in frontal cortex. Indeed, performance of this simple language task evoked activity in children with DLD in the same regions and to a similar level as in typically developing children. Consistent with previous reports, we found sub-threshold group differences in the left inferior frontal gyrus and caudate nuclei, but only when analysis was limited to a subsample of the DLD group (N = 14) who had the poorest performance on the task. Additionally, we used a two-factor model to capture variation in all children studied (N = 143) on a range of neuropsychological tests and found that these language and verbal memory factors correlated with activity in different brain regions. Our findings indicate a lack of support for some neurological models of atypical language learning, such as the procedural deficit hypothesis or the atypical lateralization hypothesis, at least when using simple language tasks that children can perform. These results also emphasise the importance of controlling for and monitoring task performance

Multi-lepton signals from the top-prime quark at the LHC

We analyze the collider signatures of models with a vector-like top-prime quark and a massive color-octet boson. The top-prime quark mixes with the top quark in the Standard Model, leading to richer final states than ones that are investigated by experimental collaborations. We discuss the multi-lepton final states, and show that they can provide increased sensitivity to models with a top-prime quark and gluon-prime. Searches for new physics in high multiplicity events are an important component of the LHC program and complementary to analyses that have been performed.Comment: 7 pages, 4 figures, 2 table

New Physics Signals in Longitudinal Gauge Boson Scattering at the LHC

We introduce a novel technique designed to look for signatures of new physics in vector boson fusion processes at the TeV scale. This functions by measuring the polarization of the vector bosons to determine the relative longitudinal to transverse production. In studying this ratio we can directly probe the high energy E^2-growth of longitudinal vector boson scattering amplitudes characteristic of models with non-Standard Model (SM) interactions. We will focus on studying models parameterized by an effective Lagrangian that include a light Higgs with non-SM couplings arising from TeV scale new physics associated with the electroweak symmetry breaking, although our technique can be used in more general scenarios. We will show that this technique is stable against the large uncertainties that can result from variations in the factorization scale, improving upon previous studies that measure cross section alone