Study on Application of Diacetylene- containing Copolyurethanes (DA-coPUs) coating as strain sensors using Raman Spectroscopy

A two-phase copolymer DA-coPUs containing of 50% hard-segment has been prepared using a polyoxypropylene-diol with molecular weight of 2000 (Voranol 2000), 4,4 -diphenylmethane diisocyanate(MDI) and 2,4-hexadiyne-1,6-diol (HDD) via a one-shot, bulk polymerization process. Solution of linear, as-prepared DA-coPUs in N,N-Dimethylacetamide (DMAc) was coated onto pre-heated steel beam. Cross polymerization of the DA-coPUs were carried out using heating under nitrogen at 100◦C for 5 hours. Deformation micromechanics of the DA-coPUs has been studied using simultaneous 4-point bending testing and Raman spectroscopy. The results showed that the coating has poorer strain- induced Raman band shift factors than that of pure DA-coPUs [1

Effect of Initial HMM Choices in Multiple Sequence Training for Gesture Recognition

We present several ways to initialize and train Hidden Markov Models (HMMs) for gesture recognition. These include using a single initial model for training (reestimation), multiple random initial models, and initial models directly computed from physical considerations. Each of the initial models is trained on multiple observation sequences using both Baum-Welch and the Viterbi Path Counting algorithm on three different model structures: Fully Connected (or ergodic), Left-Right, and Left-Right Banded. After performing many recognition trials on our video database of 780 letter gestures, results show that a) the simpler the structure is, the less the effect of the initial model, b) the direct computation method for designing the initial model is effective and provides insight into HMM learning, and c) Viterbi Path Counting performs best overall and depends much less on the initial model than does Baum-Welch training

Characterising and identifying galaxy protoclusters

We study the characteristics of galaxy protoclusters using the latest L-GALAXIES semi-analytic model. Searching for protoclusters on a scale of ∼10 cMpc gives an excellent compromise between the completeness and purity of their galaxy populations, leads to high distinction from the field in overdensity space, and allows accurate determination of the descendant cluster mass. This scale is valid over a range of redshifts and selection criteria. We present a procedure for estimating, given a measured galaxy overdensity, the protocluster probability and its descendant cluster mass for a range of modelling assumptions, particularly taking into account the shape of the measurement aperture. This procedure produces lower protocluster probabilities compared to previous estimates using fixed size apertures. The relationship between active galactic nucleus (AGN) and protoclusters is also investigated and shows significant evolution with redshift; at z ∼ 2, the fraction of protoclusters traced by AGN is high, but the fraction of all AGNs in protoclusters is low, whereas atz ≥ 5 the fraction of protoclusters containing AGN is low, but most AGNs are in protoclusters. We also find indirect evidence for the emergence of a passive sequence in protoclusters at z ∼ 2, and note that a significant fraction of all galaxies reside in protoclusters at z ≥ 2, particularly the most massive

Speech Enhancement for Robust Speaker Verification

We examine the performance of Kalman filtering and smoothing techniques in the context of a working verification system to see the effect of interspeaker and intraspeaker variability

A machine learning approach to mapping baryons on to dark matter haloes using the eagle and C-EAGLE simulations

High-resolution cosmological hydrodynamic simulations are currently limited to relatively small volumes due to their computational expense. However, much larger volumes are required to probe rare, overdense environments, and measure clustering statistics of the large scale structure. Typically, zoom simulations of individual regions are used to study rare environments, and semi-analytic models and halo occupation models applied to dark matter only (DMO) simulations are used to study the Universe in the large-volume regime. We propose a new approach, using a machine learning framework to explore the halo-galaxy relationship in the periodic EAGLE simulations, and zoom C-EAGLE simulations of galaxy clusters. We train a tree based machine learning method to predict the baryonic properties of galaxies based on their host dark matter halo properties. The trained model successfully reproduces a number of key distribution functions for an infinitesimal fraction of the computational cost of a full hydrodynamic simulation. By training on both periodic simulations as well as zooms of overdense environments, we learn the bias of galaxy evolution in differing environments. This allows us to apply the trained model to a larger DMO volume than would be possible if we only trained on a periodic simulation. We demonstrate this application using the (800 Mpc)3 P-Millennium simulation, and present predictions for key baryonic distribution functions and clustering statistics from the EAGLE model in this large volume

First Light and Reionisation Epoch Simulations (FLARES) X: Environmental Galaxy Bias and Survey Variance at High Redshift

Upcoming deep galaxy surveys with JWST will probe galaxy evolution during the epoch of reionisation (EoR, $5\leq z\leq10$) over relatively compact areas (e.g. $\sim$ 300\,arcmin$^2$ for the JADES GTO survey). It is therefore imperative that we understand the degree of survey variance, to evaluate how representative the galaxy populations in these studies will be. We use the First Light And Reionisation Epoch Simulations (FLARES) to measure the galaxy bias of various tracers over an unprecedentedly large range in overdensity for a hydrodynamic simulation, and use these relations to assess the impact of bias and clustering on survey variance in the EoR. Star formation is highly biased relative to the underlying dark matter distribution, with the mean ratio of the stellar to dark matter density varying by a factor of 100 between regions of low and high matter overdensity (smoothed on a scale of 14\,$h^{-1}$cMpc). This is reflected in the galaxy distribution -- the most massive galaxies are found solely in regions of high overdensity. As a consequence of the above, galaxies in the EoR are highly clustered, which can lead to large variance in survey number counts. For mean number counts $N\lesssim 100$ (1000), in a unit redshift slice of angular area 300\,arcmin$^2$ (1.4\,deg$^2$), the 2-sigma range in $N$ is roughly a factor of four (two). We present relations between the expected variance and survey area for different survey geometries; these relations will be of use to observers wishing to understand the impact of survey variance on their results.Comment: 14 pages, 17 figures. Paper 10 in the First Light and Reionisation Epoch Simulations (FLARES) serie

First Light And Reionisation Epoch Simulations (FLARES) : IV. The size evolution of galaxies at z ≥ 5

We present the intrinsic and observed sizes of galaxies at z >= 5 in the First Light And Reionisation Epoch Simulations (flares). We employ the large effective volume of flares to produce a sizeable sample of high-redshift galaxies with intrinsic and observed luminosities and half-light radii in a range of rest-frame ultraviolet (UV) and visual photometric bands. This sample contains a significant number of intrinsically ultracompact galaxies in the far-UV (1500 angstrom), leading to a negative intrinsic far-UV size-luminosity relation. However, after the inclusion of the effects of dust these same compact galaxies exhibit observed sizes that are as much as 50 times larger than those measured from the intrinsic emission, and broadly agree with a range of observational samples. This increase in size is driven by the concentration of dust in the core of galaxies, heavily attenuating the intrinsically brightest regions. At fixed luminosity we find a galaxy size redshift evolution with a slope of m = 1.21-1.87 depending on the luminosity sample in question, and we demonstrate the wavelength dependence of the size-luminosity relation that will soon be probed by the James Webb Space Telescope.Peer reviewe

Integrated primary care for patients with mental and physical multimorbidity: cluster randomised controlled trial of collaborative care for patients with depression comorbid with diabetes or cardiovascular disease

PublishedOpen Access ArticleObjective To test the effectiveness of an integrated collaborative care model for people with depression and long term physical conditions. Design Cluster randomised controlled trial. Setting 36 general practices in the north west of England. Participants 387 patients with a record of diabetes or heart disease, or both, who had depressive symptoms (≥10 on patient health questionaire-9 (PHQ-9)) for at least two weeks. Mean age was 58.5 (SD 11.7). Participants reported a mean of 6.2 (SD 3.0) long term conditions other than diabetes or heart disease; 240 (62%) were men; 360 (90%) completed the trial. Interventions Collaborative care included patient preference for behavioural activation, cognitive restructuring, graded exposure, and/or lifestyle advice, management of drug treatment, and prevention of relapse. Up to eight sessions of psychological treatment were delivered by specially trained psychological wellbeing practitioners employed by Improving Access to Psychological Therapy services in the English National Health Service; integration of care was enhanced by two treatment sessions delivered jointly with the practice nurse. Usual care was standard clinical practice provided by general practitioners and practice nurses. Main outcome measures The primary outcome was reduction in symptoms of depression on the self reported symptom checklist-13 depression scale (SCL-D13) at four months after baseline assessment. Secondary outcomes included anxiety symptoms (generalised anxiety disorder 7), self management (health education impact questionnaire), disability (Sheehan disability scale), and global quality of life (WHOQOL-BREF). Results 19 general practices were randomised to collaborative care and 20 to usual care; three practices withdrew from the trial before patients were recruited. 191 patients were recruited from practices allocated to collaborative care, and 196 from practices allocated to usual care. After adjustment for baseline depression score, mean depressive scores were 0.23 SCL-D13 points lower (95% confidence interval −0.41 to −0.05) in the collaborative care arm, equal to an adjusted standardised effect size of 0.30. Patients in the intervention arm also reported being better self managers, rated their care as more patient centred, and were more satisfied with their care. There were no significant differences between groups in quality of life, disease specific quality of life, self efficacy, disability, and social support. Conclusions Collaborative care that incorporates brief low intensity psychological therapy delivered in partnership with practice nurses in primary care can reduce depression and improve self management of chronic disease in people with mental and physical multimorbidity. The size of the treatment effects were modest and were less than the prespecified effect but were achieved in a trial run in routine settings with a deprived population with high levels of mental and physical multimorbidity. Trial registration ISRCTN80309252.National Institute for Health ResearchCollaboration for Leadership in Applied Health ResearchCare for Greater Mancheste

First Light and Reionisation Epoch Simulations (FLARES) - VI. The colour evolution of galaxies z=5-15

With its exquisite sensitivity, wavelength coverage, and spatial and spectral resolution, the James Webb Space Telescope (JWST) is poised to revolutionize our view of the distant, high-redshift (z > 5) Universe. While Webb's spectroscopic observations will be transformative for the field, photometric observations play a key role in identifying distant objects and providing more comprehensive samples than accessible to spectroscopy alone. In addition to identifying objects, photometric observations can also be used to infer physical properties and thus be used to constrain galaxy formation models. However, inferred physical properties from broad-band photometric observations, particularly in the absence of spectroscopic redshifts, often have large uncertainties. With the development of new tools for forward modelling simulations, it is now routinely possible to predict observational quantities, enabling a direct comparison with observations. With this in mind, in this work, we make predictions for the colour evolution of galaxies at z = 5-15 using the First Light And Reionisation Epoch Simulations (flares) cosmological hydrodynamical simulation suite. We predict a complex evolution with time, driven predominantly by strong nebular line emission passing through individual bands. These predictions are in good agreement with existing constraints from Hubble and Spitzer as well as some of the first results from Webb. We also contrast our predictions with other models in the literature: While the general trends are similar, we find key differences, particularly in the strength of features associated with strong nebular line emission. This suggests photometric observations alone should provide useful discriminating power between different models and physical states of galaxies.Peer reviewe