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.

Fasciola hepatica: Comparative effects of host resistance and parasite intra-specific interactions on size and reproductive histology in flukes from rats infected with isolates differing in triclabendazole sensitivity

The efficacies of putative fasciolicides and vaccines against Fasciola hepatica are frequently monitored in clinical and field trials by determination of fluke egg output in host faeces and by worm counts in the host liver at autopsy. Less often used are parameters based on fluke size and histology, yet these can provide important indications of specific effects on the development of particular germ-line or somatic tissues, especially in relation to the timing and profligacy of egg production. In this study. F. hepatica metacercariae of two distinct isolates, the triclabendazole (TCBZ)-sensitive Cullompton isolate and the TCBZ-resistant Oberon isolate, were administered to rats as single-isolate or mixed-isolate infections. At autopsy 16 weeks later individual adult flukes were counted, measured and the reproductive organs were examined histologically. The degree of development of the testis tubules in each fluke was represented by a numerical score, based on the proportion of the histological section profiles occupied by testis tissue. The level of anti-F. hepatica antibody in the serum of each rat was determined by ELISA. It was found that Cullompton flukes were significantly larger than Oberon flukes, and that significantly more Cullompton metacercariae developed to adults than Oberon metacercariae. The Cullompton flukes showed histological evidence of aspermy and spermatogenic arrest, which was reflected in quantitatively reduced testicular development, as compared with the Oberon isolate. In Cullompton flukes, parthenogenetic egg development is implied. The size of Cullompton and Oberon flukes was significantly related to the number of adult flukes recovered, to the number of metacercariae administered, and to the percentage success of infection. The testis development score in both isolates was significantly related to the number of adult flukes recovered but not to the number of metacercariae administered, or to the percentage success of infection. Fluke size was positively related to testis score for both isolates, and a significant negative relationship was found between percentage success of infection and metacercarial dose. The results are interpreted in terms of differing interactions between various numbers of young flukes and host immunity during invasion of and migration in the hepatic parenchyma, and of fluke intra-specific (possibly pheromonal) stimulatory effects in the final stages of development, within the host bile ducts. No significant relationships were found between host antibody levels and fluke size or testis score. False positive serological reactions were found in some rats that had been infected, but found to harbour no flukes at autopsy. Clearly the act of eliminating the flukes involved generation of an immune response. (C) 2011 Elsevier B.V. All rights reserved.

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.