393 research outputs found

    Digital Dilemma 2018

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    In October 2018 a one-day conference was held at the UCL Institute of Archaeology focussing on the ‘Digital Dilemma’ in biological archaeology —specifically human remains research where the use of digitisation methods have increased exponentially over the last decade while comparatively little discussion of the ethical and legal considerations of these data has taken place. Papers presented at Digital Dilemma 2018 explored the use of digital data in human remains research, discussing both the benefits provided by these data, areas of ethical or methodological concern and suggestions for future research. This paper and the following conference proceedings will discuss this research demonstrating the importance that this Digital Dilemma in archaeology continues to be discussed and considered in future research

    Power-law persistence and trends in the atmosphere: A detailed study of long temperature records

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    We use several variants of the detrended fluctuation analysis to study the appearance of long-term persistence in temperature records, obtained at 95 stations all over the globe. Our results basically confirm earlier studies. We find that the persistence, characterized by the correlation C(s) of temperature variations separated by s days, decays for large s as a power law, C(s) ~ s^(-gamma). For continental stations, including stations along the coastlines, we find that gamma is always close to 0.7. For stations on islands, we find that gamma ranges between 0.3 and 0.7, with a maximum at gamma = 0.4. This is consistent with earlier studies of the persistence in sea surface temperature records where gamma is close to 0.4. In all cases, the exponent gamma does not depend on the distance of the stations to the continental coastlines. By varying the degree of detrending in the fluctuation analysis we obtain also information about trends in the temperature records.Comment: 5 pages, 4 including eps figure

    Nonextensivity of the cyclic Lattice Lotka Volterra model

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    We numerically show that the Lattice Lotka-Volterra model, when realized on a square lattice support, gives rise to a {\it finite} production, per unit time, of the nonextensive entropy Sq=1ipiqq1S_q= \frac{1- \sum_ip_i^q}{q-1} (S1=ipilnpi)(S_1=-\sum_i p_i \ln p_i). This finiteness only occurs for q=0.5q=0.5 for the d=2d=2 growth mode (growing droplet), and for q=0q=0 for the d=1d=1 one (growing stripe). This strong evidence of nonextensivity is consistent with the spontaneous emergence of local domains of identical particles with fractal boundaries and competing interactions. Such direct evidence is for the first time exhibited for a many-body system which, at the mean field level, is conservative.Comment: Latex, 6 pages, 5 figure

    Phase Transitions and Oscillations in a Lattice Prey-Predator Model

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    A coarse grained description of a two-dimensional prey-predator system is given in terms of a 3-state lattice model containing two control parameters: the spreading rates of preys and predators. The properties of the model are investigated by dynamical mean-field approximations and extensive numerical simulations. It is shown that the stationary state phase diagram is divided into two phases: a pure prey phase and a coexistence phase of preys and predators in which temporal and spatial oscillations can be present. The different type of phase transitions occuring at the boundary of the prey absorbing phase, as well as the crossover phenomena occuring between the oscillatory and non-oscillatory domains of the coexistence phase are studied. The importance of finite size effects are discussed and scaling relations between different quantities are established. Finally, physical arguments, based on the spatial structure of the model, are given to explain the underlying mechanism leading to oscillations.Comment: 11 pages, 13 figure

    Automated 3D trabecular bone structure analysis of the proximal femur—prediction of biomechanical strength by CT and DXA

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    The standard diagnostic technique for assessing osteoporosis is dual X-ray absorptiometry (DXA) measuring bone mass parameters. In this study, a combination of DXA and trabecular structure parameters (acquired by computed tomography [CT]) most accurately predicted the biomechanical strength of the proximal femur and allowed for a better prediction than DXA alone. An automated 3D segmentation algorithm was applied to determine specific structure parameters of the trabecular bone in CT images of the proximal femur. This was done to evaluate the ability of these parameters for predicting biomechanical femoral bone strength in comparison with bone mineral content (BMC) and bone mineral density (BMD) acquired by DXA as standard diagnostic technique. One hundred eighty-seven proximal femur specimens were harvested from formalin-fixed human cadavers. BMC and BMD were determined by DXA. Structure parameters of the trabecular bone (i.e., morphometry, fuzzy logic, Minkowski functionals, and the scaling index method [SIM]) were computed from CT images. Absolute femoral bone strength was assessed with a biomechanical side-impact test measuring failure load (FL). Adjusted FL parameters for appraisal of relative bone strength were calculated by dividing FL by influencing variables such as body height, weight, or femoral head diameter. The best single parameter predicting FL and adjusted FL parameters was apparent trabecular separation (morphometry) or DXA-derived BMC or BMD with correlations up to r = 0.802. In combination with DXA, structure parameters (most notably the SIM and morphometry) added in linear regression models significant information in predicting FL and all adjusted FL parameters (up to R adj = 0.872) and allowed for a significant better prediction than DXA alone. A combination of bone mass (DXA) and structure parameters of the trabecular bone (linear and nonlinear, global and local) most accurately predicted absolute and relative femoral bone strength

    A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence

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    Digital twins and artificial intelligence have shown promise for improving the robustness, responsiveness, and productivity of industrial systems. However, traditional digital twin approaches are often only employed to augment single, static systems to optimise a particular process. This article presents a paradigm for combining digital twins and modular artificial intelligence algorithms to dynamically reconfigure manufacturing systems, including the layout, process parameters, and operation times of numerous assets to allow system decision-making in response to changing customer or market needs. A knowledge graph has been used as the enabler for this system-level decision-making. A simulation environment has been constructed to replicate the manufacturing process, with the example here of an industrial robotic manufacturing cell. The simulation environment is connected to a data pipeline and an application programming interface to assist the integration of multiple artificial intelligence methods. These methods are used to improve system decision-making and optimise the configuration of a manufacturing system to maximise user-selectable key performance indicators. In contrast to previous research, this framework incorporates artificial intelligence for decision-making and production line optimisation to provide a framework that can be used for a wide variety of manufacturing applications. The framework has been applied and validated in a real use case, with the automatic reconfiguration resulting in a process time improvement of approximately 10%

    Pck1 Gene Silencing in the Liver Improves Glycemia Control, Insulin Sensitivity, and Dyslipidemia in db/db Mice

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    OBJECTIVE—Cytosolic phosphoenolpyruvate carboxykinase (PEPCK-C; encoded by Pck1) catalyzes the first committed step in gluconeogenesis. Extensive evidence demonstrates a direct correlation between PEPCK-C activity and glycemia control. Therefore, we aimed to evaluate the metabolic impact and their underlying mechanisms of knocking down hepatic PEPCK-C in a type 2 diabetic model
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