Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Travel-time-based thermal tracer tomography

Active thermal tracer testing is a technique to get information about the flow and transport properties of an aquifer. In this paper we propose an innovative methodology using active thermal tracers in a tomographic setup to reconstruct cross-well hydraulic conductivity profiles. This is facilitated by assuming that the propagation of the injected thermal tracer is mainly controlled by advection. To reduce the effects of density and viscosity changes and thermal diffusion, early-time diagnostics are used and specific travel times of the tracer breakthrough curves are extracted. These travel times are inverted with an eikonal solver using the staggered grid method to reduce constraints from the pre-defined grid geometry and to improve the resolution. Finally, non-reliable pixels are removed from the derived hydraulic conductivity tomograms. The method is applied to successfully reconstruct cross-well profiles as well as a 3-D block of a high-resolution fluvio-aeolian aquifer analog data set. Sensitivity analysis reveals a negligible role of the injection temperature, but more attention has to be drawn to other technical parameters such as the injection rate. This is investigated in more detail through model-based testing using diverse hydraulic and thermal conditions in order to delineate the feasible range of applications for the new tomographic approach

Stochastic Modeling Approach to Identify Uncertainties of Karst Conduit Networks in Carbonate Aquifers

The characterization of the karst conduit network is an essential task to understand the complex flow system within karst aquifers. However, this task is challenging and often associated with uncertainty. Equivalent porous media approaches for modeling flow in karst aquifers fall short of capturing the hydraulic effect of individual karst features, while process‐oriented karst evolution models imply major computational efforts. In this study, we apply the Stochastic Karst Simulator (SKS) developed by Borghi et al. (2012) to generate karst conduit networks at a regional scale of a highly karstified carbonate aquifer located in the Eastern Mediterranean region and extensively used for water supply. The SKS generates conduit network geometries reasonably quick, using a mathematical proxy that mimics conduit evolution. The conduit simulation is based on a conceptual model of the genesis of the aquifer, consisting of different karstification phases. The stochastic approach of the algorithm enables us to generate an ensemble of conduit network realizations and to represent the uncertainties of these simulations in a Karst Probability Map. With only soft input information to constrain conduit evolution, multiple equivalent realizations yield similar resulting network geometries, indicating a robust approach. The presented methodology is numerically efficient, and its input can be easily adjusted. Subsequently, the resulting stochastic spatial distribution of conductivities can be employed for the parametrization of regional karst groundwater models.Key Points: We statistically generate multiple sets of karst conduit network geometries using input data based on soft information. The resulting Karst Probability Map accounts for uncertainty in the spatial distribution of the karst conduit network. Our approach can assist in the integration of soft information into the parametrization of karst groundwater models.Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347https://doi.org/10.14279/depositonce-1602

Travel time based thermal tracer tomography

Active thermal tracer testing is a technique to get information about the flow and transport properties of an aquifer. In this paper we propose an innovative methodology using active thermal tracers in a tomographic setup to reconstruct cross-well hydraulic conductivity profiles. This is facilitated by assuming that the propagation of the injected thermal tracer is mainly controlled by advection. To reduce the effects of density and viscosity changes and thermal diffusion, early-time diagnostics are used and specific travel times of the tracer breakthrough curves are extracted. These travel times are inverted with an eikonal solver using the staggered grid method to reduce constraints from the pre-defined grid geometry and to improve the resolution. Finally, non-reliable pixels are removed from the derived hydraulic conductivity tomograms. The method is applied to successfully reconstruct cross-well profiles as well as a 3-D block of a high-resolution fluvio-aeolian aquifer analog data set. Sensitivity analysis reveals a negligible role of the injection temperature, but more attention has to be drawn to other technical parameters such as the injection rate. This is investigated in more detail through model-based testing using diverse hydraulic and thermal conditions in order to delineate the feasible range of applications for the new tomographic approach