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Sol-gel synthesis of tetragonal BaTiO3 thin films under fast heating
Get PDFBaTiO3 thin films with a thickness of 400 nm were prepared by spin coating onto a quartz plate and subsequently calcined at 700 °C. The effect of fast (540 K min−1) and slow (10 K min−1) heating on morphology, unit cell parameter and dielectric constant was studied. Fast heating yields tetragonal BaTiO3 films with a semiconductor band gap of 3.54 eV, dielectric constant of 685 and a surface roughness of 12 nm. In contrast, slow heating produces tetragonal BaTiO3 films with a larger band gap of 3.78 eV, a dielectric constant of 219 and a surface roughness of 35 nm. A kinetic constant of 0.0061 min−1 was obtained in the decomposition of methylene blue with UV light over BTO films at 20 °C
Near-optimal dynamic rounding of fractional matchings in bipartite graphs
Get PDFWe study dynamic (1−є)-approximate rounding of fractional matchings—a key ingredient in numerous breakthroughs in the dynamic graph algorithms literature. Our first contribution is a surprisingly simple deterministic rounding algorithm in bipartite graphs with amortized update time O(є−1 log2 (є−1 · n)), matching an (unconditional) recourse lower bound of Ω(є−1) up to logarithmic factors. Moreover, this algorithm’s update time improves provided the minimum (non-zero) weight in the fractional matching is lower bounded throughout. Combining this algorithm with novel dynamic partial rounding algorithms to increase this minimum weight, we obtain a number of algorithms that improve this dependence on n. For example, we give a high-probability randomized algorithm with Õ(є−1 · (loglogn)2)-update time against adaptive adversaries. Using our rounding algorithms, we also round known (1−є)-decremental fractional bipartite matching algorithms with no asymptotic overhead, thus improving on state-of-the-art algorithms for the decremental bipartite matching problem. Further, we provide extensions of our results to general graphs and to maintaining almost-maximal matchings
The hidden toll of the pandemic : excess mortality in non-COVID 19 hospital patients
No full textSeasonal infectious diseases can cause demand and supply pressures that reduce the ability of healthcare systems to provide high-quality care. This may generate negative spillover effects on the health outcomes of patients seeking medical help for unrelated reasons. Separating these indirect burdens from the direct consequences for infected patients is usually impossible because of a lack of suitable data and an absence of population testing. However, this paper finds robust empirical evidence of excess mortality among non-COVID-19 patients in an integrated public healthcare system: the English NHS. Analysing the forecast error in the NHS’ model for predicted mortality, we find at least one additional excess death among patients who sought medical help for reasons unrelated to COVID-19 for every 42 COVID-19-related deaths in the population. We identify COVID-19 pressures as a key driver of non-COVID-19 excess mortality in NHS hospitals during the pandemic, and characterise the hospital populations and medical conditions that are disproportionately affected. Our findings have substantive relevance in shaping our understanding of the wider burden of COVID-19, and other seasonal diseases more generally, and can contribute to debates on optimal public health policy
Branchwidth is (1,g)-self-dual
No full textA graph parameter is self-dual in some class of graphs embeddable in some surface if its value does not change in the dual graph by more than a constant factor. We prove that, in the class of connected hypergraphs without bridges and loops that are embeddable in some surface of Euler genus at most g, branchwidth (1,g) -is a -self-dual parameter, i.e., for every hypergraph g in the class, the branchwidth of its dual is at most g the branchwidth of G plus g. This is the first proof that branchwidth is an additively self-dual width parameter
Modified failproof physics-informed neural network framework for fast and accurate optical fiber transmission link modeling
No full textPhysics-informed neural networks (PINNs) have recently emerged as an important and ground-breaking technique in scientific machine learning for numerous applications including in optical fiber communications. However, the vanilla/baseline version of PINNs is prone to fail under certain conditions because of the nature of the physics-based regularization term in its loss function. The use of this unique regularization technique results in a highly complex non-convex loss landscape when visualized. This leads to failure modes in PINN-based modeling. The baseline PINN works very well as an optical fiber model with relatively simple fiber parameters and for uncomplicated transmission tasks, but struggles, when the modeling task becomes relatively complex, reaching very high error, for example, numerous modeling tasks/scenarios in soliton communication and soliton pulse development in special fibers such as Erbium-doped dispersion compensating fibers. We implement two methods to circumvent the limitations caused by the physics-based regularization term to solve this problem, namely, the so-called Scaffolding technique for PINN modeling and the Progressive Block-Learning PINN modeling strategy to solve the nonlinear Schrödinger equation (NLSE), which models pulse propagation in an optical fiber. This helps PINN learn more accurately the dynamics of pulse evolution and increases accuracy by two to three orders of magnitude. We show in addition that this error is not due to the depth or architecture of the neural network but a fundamental issue inherent to PINN by design. The results achieved indicate a considerable reduction in PINN error for complex modelling problems, with accuracy increasing by up to two orders of magnitude
An agent-based model of the spread of behavioural risk-factors for cardiovascular disease in city-scale populations
No full textCardiovascular disease (CVD) is the leading cause of mortality globally, and is the second main cause of mortality in the UK. Four key modifiable behaviours are known to increase CVD risk, namely: tobacco use, unhealthy diet, physical inactivity and harmful use of alcohol. Behaviours that increase the risk of CVD can spread through social networks because individuals consciously and unconsciously mimic the behaviour of others they relate to and admire. Exploiting these social influences may lead to effective and efficient public health interventions to prevent CVD. This project aimed to construct and validate an agent-based model (ABM) of how the four major behavioural risk-factors for CVD spread through social networks in a population, and examine whether the model could be used to identify targets for public health intervention and to test intervention strategies. Previous ABMs have typically focused on a single risk factor or considered very small populations. We created a city-scale ABM to model the behavioural risk-factors of individuals, their social networks (spousal, household, friendship and workplace), the spread of behaviours through these social networks, and the subsequent impact on the development of CVD. We compared the model output (predicted CVD events over a ten year period) to observed data, demonstrating that the model output is realistic. The model output is stable up to at least a population size of 1.2M agents (the maximum tested). We found that there is scope for the modelled interventions targeting the spread of these behaviours to change the number of CVD events experienced by the agents over ten years. Specifically, we modelled the impact of workplace interventions to show that the ABM could be useful for identifying targets for public health intervention. The model itself is Open Source and is available for use or extension by other researchers
Probing a magnetar origin for the population of extragalactic fast X-ray transients detected by Chandra
Get PDFContext. Twenty-two extragalactic fast X-ray transients (FXTs) have now been discovered from two decades of Chandra data (analyzing ∼259 Ms of data), with 17 associated with distant galaxies (≳100 Mpc). Different mechanisms and progenitors have been proposed to explain their properties; nevertheless, after analyzing their timing, spectral parameters, host-galaxy properties, luminosity function, and volumetric rates, their nature remains uncertain.
Aims. We interpret a sub-sample of nine FXTs that show a plateau or a fast-rise light curve within the framework of a binary neutron star (BNS) merger magnetar model.
Methods. We fit their light curves and derive magnetar (magnetic field and initial rotational period) and ejecta (ejecta mass and opacity) parameters. This model predicts two zones: an orientation-dependent free zone (where the magnetar spin-down X-ray photons escape freely to the observer) and a trapped zone (where the X-ray photons are initially obscured and only escape freely once the ejecta material becomes optically thin). We argue that six FXTs show properties consistent with the free zone and three FXTs with the trapped zone.
Results. This sub-sample of FXTs has a similar distribution of magnetic fields and initial rotation periods to those inferred for short gamma-ray bursts, suggesting a possible association. We compare the predicted ejecta emission fed by the magnetar emission (called merger-nova) to the optical and near-infrared upper limits of two FXTs, XRT 141001 and XRT 210423 where contemporaneous optical observations are available. The non-detections place lower limits on the redshifts of XRT 141001 and XRT 210423 of z ≳ 1.5 and ≳0.1, respectively.
Conclusions. If the magnetar remnants lose energy via gravitational waves (GWs), it should be possible to detect similar objects with the current advanced LIGO detectors out to a redshift z ≲ 0.03, while future GW detectors will be able to detect them out to z ≈ 0.5
Pump-free and reconfigurable all-optical modulation format conversion for MQAM signals by parallel nonlinear Mach-Zehnder interferometers
Get PDFVillage social structure and labor market performance : evidence from the Philippines
No full textThis paper studies how social structure — the pattern of social links that connect individuals in a community — affects labor markets. Under competing views on the role of networks, social structures that discourage network hiring could improve or hinder labor market performance. We test these competing views using data on marriage networks in 15,000 villages, combined with labor force survey data. Using regressions analyses and an instrumental variable strategy, we find that individuals living in more socially fragmented villages are less likely to work in family firms, more likely to use formal job search strategies, invest more in education and earn higher wages. Social fragmentation thus discourages network hiring and improves labor market performance. These results survive 384 combinations of robustness checks. We further provide direct evidence against reverse causality