Nonlinear time series prediction based on a power-law noise model

In this paper we investigate the influence of a power-law noise model, also called noise, on the performance of a feed-forward neural network used to predict time series. We introduce an optimization procedure that optimizes the parameters the neural networks by maximizing the likelihood function based on the power-law model. We show that our optimization procedure minimizes the mean squared leading to an optimal prediction. Further, we present numerical results applying method to time series from the logistic map and the annual number of sunspots demonstrate that a power-law noise model gives better results than a Gaussian model.

A new model based approach for the prediction and optimisation of thermal homogeneity in single screw extrusion

In polymer extrusion, delivery of a melt which is homogenous in composition and temperature is important for good product quality. However, the process is inherently prone to temperature fluctuations which are difficult to monitor and control via single point based conventional thermo- couples. In this work, the die melt temperature profile was monitored by a thermocouple mesh and the data obtained was used to generate a model to predict the die melt temperature profile. A novel nonlinear model was then proposed which was demonstrated to be in good agreement with training and unseen data. Furthermore, the proposed model was used to select optimum process settings to achieve the desired average melt temperature across the die while improving the temperature homogeneity. The simulation results indicate a reduction in melt temperature variations of up to 60%.

In Silico Prediction of the Mechanobiological Response of Arterial Tissue: Application to Angioplasty and Stenting

One way to restore physiological blood flow to occluded arteries involves the deformation of plaque using an intravascular balloon and preventing elastic recoil using a stent. Angioplasty and stent implantation cause unphysiological loading of the arterial tissue, which may lead to tissue in-growth and reblockage; termed “restenosis.” In this paper, a computational methodology for predicting the time-course of restenosis is presented. Stress-induced damage, computed using a remaining life approach, stimulates inflammation (production of matrix degrading factors and growth stimuli). This, in turn, induces a change in smooth muscle cell phenotype from contractile (as exists in the quiescent tissue) to synthetic (as exists in the growing tissue). In this paper, smooth muscle cell activity (migration, proliferation, and differentiation) is simulated in a lattice using a stochastic approach to model individual cell activity. The inflammation equations are examined under simplified loading cases. The mechanobiological parameters of the model were estimated by calibrating the model response to the results of a balloon angioplasty study in humans. The simulation method was then used to simulate restenosis in a two dimensional model of a stented artery. Cell activity predictions were similar to those observed during neointimal hyperplasia, culminating in the growth of restenosis. Similar to experiment, the amount of neointima produced increased with the degree of expansion of the stent, and this relationship was found to be highly dependant on the prescribed inflammatory response. It was found that the duration of inflammation affected the amount of restenosis produced, and that this effect was most pronounced with large stent expansions. In conclusion, the paper shows that the arterial tissue response to mechanical stimulation can be predicted using a stochastic cell modeling approach, and that the simulation captures features of restenosis development observed with real stents. The modeling approach is proposed for application in three dimensional models of cardiovascular stenting procedures.

Methods in wind power prediction & forecasting

Globally on-shore wind power has seen considerable growth in all grid systems. In the coming decade off-shore wind power is also expected to expand rapidly. Wind power is variable and intermittent over various time scales because it is weather dependent. Therefore wind power integration into traditional grids needs additional power system and electricity market planning and management for system balancing. This extra system balancing means that there is additional system costs associated with wind power assimilation. Wind power forecasting and prediction methods are used by system operators to plan unit commitment, scheduling and dispatch and by electricity traders and wind farm owners to maximize profit. Accurate wind power forecasting and prediction has numerous challenges. This paper presents a study of the existing and possible future methods used in wind power forecasting and prediction for both on-shore and off-shore wind farms.

Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes

Computing has recently reached an inflection point with the introduction of multicore processors. On-chip thread-level parallelism is doubling approximately every other year. Concurrency lends itself naturally to allowing a program to trade performance for power savings by regulating the number of active cores; however, in several domains, users are unwilling to sacrifice performance to save power. We present a prediction model for identifying energy-efficient operating points of concurrency in well-tuned multithreaded scientific applications and a runtime system that uses live program analysis to optimize applications dynamically. We describe a dynamic phase-aware performance prediction model that combines multivariate regression techniques with runtime analysis of data collected from hardware event counters to locate optimal operating points of concurrency. Using our model, we develop a prediction-driven phase-aware runtime optimization scheme that throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each program phase. The use of prediction reduces the overhead of searching the optimization space while achieving near-optimal performance and power savings. A thorough evaluation of our approach shows a reduction in power consumption of 10.8 percent, simultaneous with an improvement in performance of 17.9 percent, resulting in energy savings of 26.7 percent.

PREDICTION OF NETILMICIN DISPOSITION IN NEONATES

Multiple regression analyses of data from 33 neonates who received netilmicin therapy showed that concurrent treatment with other drugs (Drg), creatinine clearance (CL(cr)), gestational age (GA), and an apgar score of less than 6 at 1 min (Agl') were significant determinants of netilmicin clearance. Apparent volume of distribution was significantly affected by postnatal age (PNA), gender, the presence of ascites and/or oedema (A/O), and whether or not the neonate was small for gestational age (SGA). The following formulae were obtained: CL (ml min-1 kg-1) = -0.108 - 0.210 (Drg) + 0.152(CL(cr)) + 0.019(GA) -0.128(Agl') (multiple R = 0.725, p

Low pressure carbon dioxide solubility in pure electrolyte solvents for lithium-ion batteries as a function of temperature. Measurement and prediction

Experimental values for the carbon dioxide solubility in eight pure electrolyte solvents for lithium ion batteries – such as ethylene carbonate (EC), propylene carbonate (PC), dimethyl carbonate (DMC), ethyl methyl carbonate (EMC), diethyl carbonate (DEC), ?-butyrolactone (?BL), ethyl acetate (EA) and methyl propionate (MP) – are reported as a function of temperature from (283 to 353) K and atmospheric pressure. Based on experimental solubility data, the Henry’s law constant of the carbon dioxide in these solvents was then deduced and compared with reported values from the literature, as well as with those predicted by using COSMO-RS methodology within COSMOthermX software and those calculated by the Peng–Robinson equation of state implemented into Aspen plus. From this work, it appears that the CO2 solubility is higher in linear carbonates (such as DMC, EMC, DEC) than in cyclic ones (EC, PC, ?BL). Furthermore, the highest CO2 solubility was obtained in MP and EA solvents, which are comparable to the solubility values reported in classical ionicliquids. The precision and accuracy of the experimental values, considered as the per cent of the relative average absolute deviations of the Henry’s law constants from appropriate smoothing equations and from literature values, are close to (1% and 15%), respectively. From the variation of the Henry’s law constants with temperature, the partial molar thermodynamic functions of dissolution such as the standard Gibbs free energy, the enthalpy, and the entropy are calculated, as well as the mixing enthalpy of the solvent with CO2 in its hypothetical liquid state.

Serum eosinophilic cationic protein and blood eosinophil counts for the prediction of the presence of airways inflammation in children with wheezing

Background Serum eosinophilic cationic protein (ECP) concentrations may be useful noninvasive markers of airways inflammation in atopic asthma. However, the usefulness of serum ECP measurement for the prediction of airways inflammation in children with a history of wheezing is unknown.