Simulating the influences of groundwater on regional geomorphology using a distributed, dynamic, landscape evolution modelling platform

A dynamic landscape evolution modelling platform (CLiDE) is presented that allows a variety of Earth system interactions to be explored under differing environmental forcing factors. Representation of distributed surface and subsurface hydrology within CLiDE is suited to simulation at sub-annual to centennial time-scales. In this study the hydrological components of CLiDE are evaluated against analytical solutions and recorded datasets. The impact of differing groundwater regimes on sediment discharge is examined for a simple, idealised catchment, Sediment discharge is found to be a function of the evolving catchment morphology. Application of CLiDE to the upper Eden Valley catchment, UK, suggests the addition of baseflow-return from groundwater into the fluvial system modifies the total catchment sediment discharge and the spatio-temporal distribution of sediment fluxes during storm events. The occurrence of a storm following a period of appreciable antecedent rainfall is found to increase simulated sediment fluxes

Enhancement of super-exchange pairing in the periodically-driven Hubbard model

We show that periodic driving can enhance electron pairing in strongly-correlated systems. Focusing on the strong-coupling limit of the doped Hubbard model we investigate in-gap, spatially inhomogeneous, on-site modulations and demonstrate that they substantially reduce electronic hopping without suppressing super-exchange interactions and pair hopping. We calculate real-time dynamics for the one-dimensional case, starting from zero and finite temperature initial states, and show that enhanced singlet-pair correlations emerge quickly and robustly in the out-of-equilibrium many-body state. Our results reveal a fundamental pairing mechanism that might underpin optically induced superconductivity in some strongly correlated quantum materials

Innovating Cultural Competence Education for Nurses

Objective To improve cultural competency levels of registered nurses on the Mother/Baby unit by educating nurses. Background Demographics are shifting in the U.S. with an increase in minority populations. Research has revealed insufficient education or a complete absence of education, resulting in nurses that are not equipped to adequately care for culturally diverse patients. Methods The Evidence-Based Practice Improvement (EBPI) Model guided the development and implementation of the project. A cultural competence education module was developed utilizing resources from the U.S. Department of Health and Human Services. Outcomes were measured using a pretest/posttest design tool, the Inventory for Assessing the Process of Cultural Competence Among Healthcare Professionals- Revised (IAPCC-R) to assess cultural competency levels. Results A paired sample t-test was used to determine if there was a significant change between pretest and posttest scores in 14 participants. Scores significantly increased (p=0.002) from 73.57 at pretest to 81.64 at posttest. Items were summed to create subscales scores for awareness, desire, skill, knowledge and encounters. Scores significantly increased for all constructs, though knowledge did not significantly increase. Conclusion Cultural competence education increased the overall cultural competency levels of registered nurses on the Mother/Baby unit. Implication for Nurses Culturally competent education has been associated with improved awareness, desire, skill and encounters, which in turn supports patient-centered care

Mitigating Gender Bias in Machine Learning Data Sets

Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.i

Simulating tidal and storm surge hydraulics with a simple 2D inertia based model, in the Humber Estuary, U.K

The hydraulic modelling of tidal estuarine environments has been largely limited to complex 3D models that are computationally expensive. This makes them unsuitable for applications which make use of live data to make real/near time forecasts, such as the modelling of storm surge propagation and associated flood inundation risks. To address this requirement for a computationally efficient method a reduced complexity, depth-integrated 2D storage cell model (Lisflood-FP) has been applied to the Humber Estuary, UK. The capability of Lisflood-FP to reproduce the tidal heights of the Humber Estuary has been shown by comparing modelled and observed tidal stage heights over a period of a week. The feasibility of using the Lisflood-FP model to forecast flood inundation risk from a storm surge is demonstrated by reproducing the major storm surge that struck the UK East Coast and Humber Estuary on 5 December 2013. Results show that even for this 2013 extreme event the model is capable of reproducing the hydraulics and tidal levels of the estuary. Using present day flood defences and observed flooding extents, the modelled flood inundation areas produced by the model were compared, showing agreement in most areas and illustrating the model's potential as a now-casting early warning system when driven by publically available data, and in near real-time. The Lisflood-FP model used was incorporated into the CAESAR-Lisflood GUI, with the calibration and verification of the estuarine hydraulics reported herein being a key step in creating an estuary evolution model, capable of operating in the decadal to century timescales that are presently underrepresented in estuarine predictive capability, and ultimately developing a model to predict the evolution of flood risk over the longer term

Dynamical order and superconductivity in a frustrated many-body system

In triangular lattice structures, spatial anisotropy and frustration can lead to rich equilibrium phase diagrams with regions containing complex, highly entangled states of matter. In this work we study the driven two-rung triangular Hubbard model and evolve these states out of equilibrium, observing how the interplay between the driving and the initial state unexpectedly shuts down the particle-hole excitation pathway. This restriction, which symmetry arguments fail to predict, dictates the transient dynamics of the system, causing the available particle-hole degrees of freedom to manifest uniform long-range order. We discuss implications of our results for a recent experiment on photo-induced superconductivity in ${\rm \kappa - (BEDT-TTF)_{2}Cu[N(CN)_{2}]Br}$ molecules.Comment: Main Text: 7 Pages, 4 Figures, Supplementary: 4 Pages, 3 Figure

On the reliability of the theoretical internal conversion coefficients

Possible sources of uncertainties in the calculations of the internal conversion coefficients are studied. The uncertainties induced by them are estimated.Comment: 16 pages (including 3 figures inserted by 'epsfig' macro

Characterisation of a refined rat model of respiratory infection with Pseudomonas aeruginosa and the effect of ciprofloxacin

AbstractBackgroundWe sought to characterise a refined rat model of respiratory infection with P. aeruginosa over an acute time course and test the antibiotic ciprofloxacin.MethodsAgar beads were prepared±SPAN®80. Rats were inoculated with sterile agar beads or those containing 105 colony forming units (cfu) P. aeruginosa via intra-tracheal dosing. Bacterial load and inflammatory parameters were measured.ResultsDiffering concentrations of SPAN® 80 modified median agar bead diameter and reduced particle size distribution. Beads prepared with 0.01% v/v SPAN®80 were evaluated in vivo. A stable lung infection up to 7days post infection was achieved and induced BALF neutrophilia 2 and 5days post infection. Ciprofloxacin (50mg/kg) significantly attenuated infection without affecting the inflammatory parameters measured.ConclusionSPAN® 80 can control the particle size and lung distribution of agar beads and P. aeruginosa-embedded beads prepared with 0.01%v/v SPAN®80 can induce infection and inflammation over 7days

Enhancement of super-exchange pairing in the periodically-driven Hubbard model

Recent experiments performed on cuprates and alkali-doped fullerides have demonstated that key signatures of superconductivity can be induced above the equilibrium critical temperature by optical modulation. These observations in disparate physical systems may indicate a general underlying mechanism. Multiple theories have been proposed, but these either consider specific features, such as competing instabilities, or focus on conventional BCS-type superconductivity. Here we show that periodic driving can enhance electron pairing in strongly-correlated systems. Focusing on the strongly-repulsive limit of the doped Hubbard model, we investigate in-gap, spatially inhomogeneous, on-site modulations. We demonstrate that such modulations substantially reduce electronic hopping, while simultaneously sustaining super-exchange interactions and pair hopping via driving-induced virtual charge excitations. We calculate real-time dynamics for the one-dimensional case, starting from zero and finite temperature initial states, and show that enhanced singlet--pair correlations emerge quickly and robustly in the out-of-equilibrium many-body state. Our results reveal a fundamental pairing mechanism that might underpin optically induced superconductivity in some strongly correlated quantum materials.Comment: 14 pages, 11 figure