Igusa's Local Zeta Functions and Exponential Sums for Arithmetically Non Degenerate Polynomials

We study the twisted local zeta function associated to a polynomial in two variables with coefficients in a non-Archimedean local field of arbitrary characteristic. Under the hypothesis that the polynomial is arithmetically non degenerate, we obtain an explicit list of candidates for the poles in terms of geometric data obtained from a family of arithmetic Newton polygons attached to the polynomial. The notion of arithmetical non degeneracy due to Saia and Z\'u\~niga-Galindo is weaker than the usual notion of non degeneracy due to Kouchnirenko. As an application we obtain asymptotic expansions for certain exponential sums attached to these polynomials.Comment: 20 pages. In this version there is a more precise statement of Lemma 2.4 and a correction to the Example in Section 4. Minor corrections adde

Novel model of cerebrospinal fluid dynamics based on hemodynamically driven cyclic brain compliance variation

This study provides a novel explanation for the CerebrQ-Spinal Fluid (CSF) flow pattern observed in phase contrast cine-MRJ studies. CSF dynamics has been traditionally explained as a bulk flow from the site of production to the site of absorption. Studies done with phase contrast cine-MRI show a more complex CSF movement, that is not explainable by the bulk flow paradigm. This study describes a mechanism explaining how the energy delivered by the heart in each cycle is responsible not only for the blood flow, but also for the CSF circulation. This mechanism is based on a cyclic variation of brain compliance, dependent on the blood volume inside the brain vessels. As the cardiac cycle changes the blood volume inside the vessels, it also conditions a compliance cycle of the brain tissue. For better comprehension of the mechanism, a conceptual model, mathematical model and computer model are described. To capture the essence of CSF dynamics a three compartmental model is created representing: the ventricular system, the intracranial subarachnoideal space, and the spinal subarachnoideal space. The implemented driving function represents the blood volume variation with time produced by the cardiac cycle. In turn it detennines cyclic changes in brain parenchyma compliance. Brain parenchyma compliance changes as a function of the blood volume inside the brain vessels; therefore, during systole the compliance diminishes, during diastole compliance increases. As brain tissue compliance changes the CSF volume inside each compartment is redistributed. Cyclic compliance variation of brain tissue creates a pulsatile CSF flow. The CSF dynamics model is also used for the analysis of altered CSF dynamics; Normal Pressure Hydrocephalus and Idiopathic Intracranial Hypertension are explained as a consequence of altered compliance of the brain tissue

A computational model of spasticity based on a decoupling of the alpha and gamma efferents

It is generally accepted that spasticity results from changes in the excitability of the stretch reflex. This change lowers the threshold of the motoneurons of the spinal cord where the integration of a signal from velocity/position sensors is processed and then fed back to the contracting muscle (alphaextrafusal and gamma-intrafusal fibers). The stretch reflex depends on the initial length of the muscle, the stretch velocity and voluntary activity. The exact sequence of the triggering events remains unknown, is poorly understood and as a result is controversial. The clinical classification scales are mainly subjective and by definition, inaccurate. This computational model of spasticity is based on the concept of the existence of a normal neuromuscular control coupling function, which ordinarily encloses the extrafusal and intrafusal fibers, and explains the spasticity as a result of the uncoupling of this normal mechanism. The model involves mechanical parameters and basic neuromuscular control theory

Feedback control of polymer flooding process considering geologic uncertainty

textPolymer flooding is economically successful in reservoirs where the water flood mobility ratio is high, and/or the reservoir heterogeneity is adverse, because of the improved sweep resulting from the mobility-controlled oil displacement. The performance of a polymer flood can be further improved if the process is dynamically controlled using updated reservoir models and a closed-loop production optimization scheme is implemented. However, the formulation of an optimal production strategy is based on uncertain production forecasts resulting from uncertainty in spatial representation of reservoir heterogeneity, geologic scenarios, inaccurate modeling, scaling, just to cite a few factors. Assessing the uncertainty in reservoir modeling and transferring it to uncertainty in production forecasts is crucial for efficiently controlling the process. This dissertation presents a feedback control framework that (1) assesses uncertainty in reservoir modeling and production forecasts, (2) updates the prior uncertainty in reservoir models by integrating continuously monitored production data, and (3) formulates optimal injection/production rates for the updated reservoir models. This approach focuses on assessing uncertainty in reservoir modeling and production forecasts originated mainly by uncertain geologic scenarios and spatial variations of reservoir properties (heterogeneity). This uncertainty is mapped in a metric space created by comparing multiple reservoir models and measuring differences in effective heterogeneity related to well connectivity and well responses characteristic of polymer flooding. Continuously monitored production data is used to refine the uncertainty map using a Bayesian inversion algorithm. In contrast to classical approach of history matching by model perturbation, a model selection problem is implemented where highly probable reservoir models are selected to represent the posterior uncertainty in production forecasts. The model selection procedure yields the posterior uncertainty associated with the reservoir model. The production optimization problem is solved using the posterior models and a proxy model of polymer flooding to rapidly evaluate the objective function and response surfaces to represent the relationship between well controls and an economic objective function. The value of the feedback control framework is demonstrated with two examples of polymer flooding where the economic performance was maximized.Petroleum and Geosystems Engineerin

A Transportation Alliance of Environmental Horticulture Producers in Georgia: Issues and Feasibility

Transportation and shipping costs of ornamental horticulture are 10% of total cost of production in Georgia. With many small to medium sized producers using their own independent transportation system, methods to optimize vehicle operations are desired. Will a transportation alliance reduce shipping costs, increase distribution efficiencies, and reduce carbon dioxide emissions among ornamental plants producers in Georgia? The study shows alliances are not only feasible, they have average total cost savings of 9%, average total miles driven savings of 8%, average number of trucks savings of 8%, average driving hours savings of 15%, and average carbon dioxide emissions savings of 8%.transportation, logistics, efficiencies, savings, environmental horticulture, Agribusiness,

COMBUSTION MODEL FOR SPARK IGNITION ENGINES OPERATING ON GASOLINE-ETHANOL BLENDS

This article presents a phenomenological combustion model using turbulent flame propagation theory developed by Keck and coworkers, 1974. The model was adapted to work with gasoline-ethanol blends, following correlations presented by Bayraktar,2005. New sub-models were introduced for intake valve velocity and combustion efficiency. These allow simulating the effect of compression ratio, spark timing and fuel change. Results show good agreement with the ones in the original work as well as with experimental results in a Cooperative Fuels Research (CFR) engine

The JGrass-NewAge system for forecasting and managing the hydrological budgets at the basin scale: models of flow generation and propagation/routing

Abstract. This paper presents a discussion of the predictive capacity of the implementation of the semi-distributed hydrological modeling system JGrass-NewAge. This model focuses on the hydrological budgets of medium scale to large scale basins as the product of the processes at the hillslope scale with the interplay of the river network. The part of the modeling system presented here deals with the: (i) estimation of the space-time structure of precipitation, (ii) estimation of runoff production; (iii) aggregation and propagation of flows in channel; (v) estimation of evapotranspiration; (vi) automatic calibration of the discharge with the method of particle swarming. The system is based on a hillslope-link geometrical partition of the landscape, combining raster and vectorial treatment of hillslope data with vector based tracking of flow in channels. Measured precipitation are spatially interpolated with the use of kriging. Runoff production at each channel link is estimated through a peculiar application of the Hymod model. Routing in channels uses an integrated flow equation and produces discharges at any link end, for any link in the river network. Evapotranspiration is estimated with an implementation of the Priestley-Taylor equation. The model system assembly is calibrated using the particle swarming algorithm. A two year simulation of hourly discharge of the Little Washita (OK, USA) basin is presented and discussed with the support of some classical indices of goodness of fit, and analysis of the residuals. A novelty with respect to traditional hydrological modeling is that each of the elements above, including the preprocessing and the analysis tools, is implemented as a software component, built upon Object Modelling System v3 and jgrasstools prescriptions, that can be cleanly switched in and out at run-time, rather than at compiling time. The possibility of creating different modeling products by the connection of modules with or without the calibration tool, as for instance the case of the present modeling chain, reduces redundancy in programming, promotes collaborative work, enhances the productivity of researchers, and facilitates the search for the optimal modeling solution