Entanglement of two delocalised electrons

Several convenient formulae for the entanglement of two indistinguishable delocalised spin-1/2 particles are introduced. This generalizes the standard formula for concurrence, valid only in the limit of localised or distinguishable particles. Several illustrative examples are given.Comment: 4 page

Carbonation and self-healing in concrete: Kinetic Monte Carlo simulations of mineralization

Industrial applications of carbonation such as self-healing and carbon capture and storage have been limited, due to a lack of reliable predictive models linking the chemistry of carbonation at the molecular scale to microstructure development and macroscopic properties. This work proposes a coarse-grained Kinetic Monte Carlo (KMC) approach to simulate microstructural evolution of a model cement paste during carbonation, along with evolution of pore solution chemistry and saturation indexes of solid species involved. The simulations predict the effective rate constants for Ca(OH)2 dissolution and CaCO3 precipitation as kCa(OH)2 = 2.20 × 10−5 kg/m3/s and kCaCO3 = 4.24 × 10−6 kg/m3/s. These values are directly fed to a macroscale reactive transport model to predict carbonate penetration depth. The rate constants from the molecular scale are used in a boundary nucleation and growth model to predict self-healing of cracks. Subsequently these results are compared with experimental data, and provide good agreement. This proposed multiscale approach can help understand and manage the carbonation of both traditional and new concretes, supporting applications in residual lifetime assessment, carbon capture, and self-healing

Exclusive Neutral Pion Electroproduction in the Deeply Virtual Regime

We present measurements of the ep→epπ0 cross section extracted at two values of four-momentum transfer Q2=1.9 GeV2 and Q2=2.3 GeV2 at Jefferson Lab Hall A. The kinematic range allows one to study the evolution of the extracted cross section as a function of Q2 and W. Results are confronted with Regge-inspired calculations and GPD predictions. An intepretation of our data within the framework of semi-inclusive deep inelastic scattering is also discussed

Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

Background The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic; characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic; and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings In 2019, at the onset of the COVID-19 pandemic, USD 9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending USD 7·3 trillion (95% UI 7·2–7·4) in 2019; 293·7 times the USD 24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, USD 43·1 billion in development assistance was provided to maintain or improve health. The pandemic led to an unprecedented increase in development assistance targeted towards health; in 2020 and 2021, USD 1·8 billion in DAH contributions was provided towards pandemic preparedness in LMICs, and USD 37·8 billion was provided for the health-related COVID-19 response. Although the support for pandemic preparedness is 12·2% of the recommended target by the High-Level Independent Panel (HLIP), the support provided for the health-related COVID-19 response is 252·2% of the recommended target. Additionally, projected spending estimates suggest that between 2022 and 2026, governments in 17 (95% UI 11–21) of the 137 LMICs will observe an increase in national government health spending equivalent to an addition of 1% of GDP, as recommended by the HLIP. Interpretation There was an unprecedented scale-up in DAH in 2020 and 2021. We have a unique opportunity at this time to sustain funding for crucial global health functions, including pandemic preparedness. However, historical patterns of underfunding of pandemic preparedness suggest that deliberate effort must be made to ensure funding is maintained. Funding Bill & Melinda Gates Foundation

Cómo adaptar un modelo de aprendizaje profundo a un nuevo dominio: el caso de la extracción de relaciones biomédicas

In this article, we study the relation extraction problem from Natural Language Processing (NLP) implementing a domain adaptation setting without external resources. We trained a Deep Learning (DL) model for Relation Extraction (RE), which extracts semantic relations in the biomedical domain. However, can the model be applied to different domains? The model should be adaptable to automatically extract relationships across different domains using the DL network. Completely training DL models in a short time is impractical because the models should quickly adapt to different datasets in several domains without delay. Therefore, adaptation is crucial for intelligent systems, where changing factors and unanticipated perturbations are common. In this study, we present a detailed analysis of the problem, as well as preliminary experimentation, results, and their evaluation.En este trabajo estudiamos el problema de extracción de relaciones del Procesamiento de Lenguaje Natural (PLN). Realizamos una configuración para la adaptación de dominio sin recursos externos. De esta forma, entrenamos un modelo con aprendizaje profundo (DL) para la extracción de relaciones (RE). El modelo permite extraer relaciones semánticas para el dominio biomédico. Sin embargo, ¿El modelo puede ser aplicado a diferentes dominios? El modelo debería adaptarse automáticamente para la extracción de relaciones entre diferentes dominios usando la red de DL. Entrenar completamente modelos DL en una escala de tiempo corta no es práctico, deseamos que los modelos se adapten rápidamente de diferentes conjuntos de datos con varios dominios y sin demora. Así, la adaptación es crucial para los sistemas inteligentes que operan en el mundo real, donde los factores cambiantes y las perturbaciones imprevistas son habituales. En este artículo, presentamos un análisis detallado del problema, una experimentación preliminar, resultados y la discusión acerca de los resultados

Crossover from Anderson- to Kondo-like behavior: Universality induced by spin-charge separation

The thermodynamics of a lattice regularized asymmetric Anderson impurity in a correlated host is obtained by an exact solution. The crossover from the Anderson- to the Kondo-regime is studied, thus making contact with predictions by scaling theory. On the basis of the exact solution, the transition to universal Kondo behavior is shown to be realized by a graduate separation of the energy scales of spin and charge excitations.Comment: 18 pages, 5 figure

Rapid Liana Colonization along a Secondary Forest Chronosequence

Lianas (woody vines) can have profound effects on tree recruitment, growth, survival, and diversity in tropical forests. However, the dynamics of liana colonization soon after land abandonment are poorly understood, and thus it is unknown whether lianas alter tree regeneration early in succession. We examined the liana community in 43 forests that ranged from 1 to 31 yr old in central Panama to determine how fast lianas colonize young forests and how the liana community changes with forest succession. We found that lianas reached high densities early in succession, commonly exceeding 1000 stems/ha within the first 5 yr of forest regeneration. Lianas also increased rapidly during early succession in terms of basal area but did not show evidence of saturation within the 30 yr of our chronosequence. The relative contribution of lianas to total woody plant community in terms of basal area and density increased rapidly and reached a saturation point within 5 yr (basal area) to 15 yr (density) after land abandonment. Our data demonstrate that lianas recruit early and in high density in tropical forest regeneration, and thus lianas may have a large effect on the way in which secondary forests develop both early and throughout succession

Tiger Sharks Eat Songbirds: Reply

In response to our recent paper (Drymon et al. 2019), Yosef (2019) questions the mechanism proposed to explain interactions between tiger sharks (Galeocerdo cuvier) and migratory songbirds, while offering an alternative mechanism based on a single observation. We appreciate the comments from Yosef and the opportunity to respond