Effects of short-term acetaminophen and celecoxib treatment on orthodontic tooth movement and neuronal activation in rat

Non-steroidal anti-inflammatory drugs (NSAIDs) have been used for pain relief in orthodontics, but clinical studies reported that they may reduce tooth movement (TM). By other side, TM seems to activate brain structures related to nociception, but the effects of NSAIDs in this activation have not been studied yet. We analyzed the effect of short-term treatment with acetaminophen or celecoxib in the separation of rat upper incisors, as well as in neuronal activation of the spinal trigeminal nucleus, following tooth movement. Thirty rats (400-420 g) were pretreated through oral gavage (1 ml/dose)with acetaminophen (200 mg/kg), celecoxib (50 mg/kg) or vehicle (carboxymethylcellulose 0.4%). After 30 min, they received an activated (30 g) orthodontic appliance for TM. In controls, this appliance was immediately removed after its introduction. Rats received ground food, and every 12 h, one of the drugs or vehicle. After 48 h, they were anesthetized, maxilla was radiographed, and were perfused with 4% paraformaldehyde. Brains were further processed for Fos immunohistochemistry. TM induced incisor distalization (p < 0.05) and neuronal activation of the spinal trigeminal nucleus. Treatment with both drugs did not affect tooth movement, but reduced c-fos expression in the caudalis subnucleus. No changes in c-fos expression were seen in the oralis and interpolaris subnuclei. We conclude that neither celecoxib nor acetaminophen seems to affect tooth movement, when used for 2 days, but both drugs are able to reduce the activation of brain structures related to nociception. Short-term treatment with celecoxib, thus, may be a therapeutic alternative to acetaminophen when the latter is contra indicated. (C) 2009 Elsevier Inc. All rights reserved.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

Short-term beta-hydroxy-beta-methylbutyrate supplementation does not reduce symptoms of eccentric muscle damage

Purpose: We examined the effects of short-term beta -hydroxy-beta -methylbutyrate (HIM) supplementation on symptoms of muscle damage following an acute bout of eccentric exercise. Methods: Non-resistance trained subjects were randomly assigned to a HMB supplement group (HMB, 40mg/kg bodyweight/day, n = 8) or placebo group (CON, n = 9). Supplementation commenced 6 days prior to a bout of 24 maximal isokinetic eccentric contractions of the elbow flexors and continued throughout post-testing. Muscle soreness, upper arm girth, and torque measures were assessed pre-exercise, 15 min post-exercise, and 1, 2, 3, 4, 7, and 10 days post-exercise. Results: No pre-test differences between HMB and CON groups were identified, and both performed a similar amount of eccentric work during the main eccentric exercise bout (p > .05). HMB supplementation had no effect on swelling, muscle soreness, or torque following the damaging eccentric exercise bout (p > .05). Conclusion: Compared to a placebo condition, short-term supplementation with 40mg/kg bodyweight/day of HMB had no beneficial effect on a range of symptoms associated with eccentric muscle damage. If HMB can produce an ergogenic response, a longer pre-exercise supplementation period may be necessary.

Development of a short-term model using petrophysical logging at Century Mine, North Queensland

Pasminco Century Mine has developed a geophysical logging system to provide new data for ore mining/grade control and the generation of Short Term Models for mine planning. Previous work indicated the applicability of petrophysical logging for lithology prediction, however, the automation of the method was not considered reliable enough for the development of a mining model. A test survey was undertaken using two diamond drilled control holes and eight percussion holes. All holes were logged with natural gamma, magnetic susceptibility and density. Calibration of the LogTrans auto-interpretation software using only natural gamma and magnetic susceptibility indicated that both lithology and stratigraphy could be predicted. Development of a capability to enforce stratigraphic order within LogTrans increased the reliability and accuracy of interpretations. After the completion of a feasibility program, Century Mine has invested in a dedicated logging vehicle to log blast holes as well as for use in in-fill drilling programs. Future refinement of the system may lead to the development of GPS controlled excavators for mining ore.

Short-term autocorrelation in Australian equities

This paper examines the statistical and economic significance of short-term autocorrelation in Australian equities. We document large negative first-order autocorrelation in individual stock returns. Preliminary results suggest this autocorrelation is economically significant, as two simple trading strategies based on the autocorrelation structure appear to yield large risk-adjusted returns. Further analysis, however, shows that these results are driven by the inclusion of small-capitalisation and low-priced stocks which are vulnerable to a number of market-microstructure-related problems. After revising the dataset to mitigate these problems, little evidence of economic significance remains.

On the robustness of short-term interest rate models

This paper investigates the robustness of a range of short–term interest rate models. We examine the robustness of these models over different data sets, time periods, sampling frequencies, and estimation techniques. We examine a range of popular one–factor models that allow the conditional mean (drift) and conditional variance (diffusion) to be functions of the current short rate. We find that parameter estimates are highly sensitive to all of these factors in the eight countries that we examine. Since parameter estimates are not robust, these models should be used with caution in practice.

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Nonlinear optimization method for short-term hydro scheduling considering head-dependency

This paper is on the problem of short-term hydro scheduling, particularly concerning head-dependent reservoirs under competitive environment. We propose a new nonlinear optimization method to consider hydroelectric power generation as a function of water discharge and also of the head. Head-dependency is considered on short-term hydro scheduling in order to obtain more realistic and feasible results. The proposed method has been applied successfully to solve a case study based on one of the main Portuguese cascaded hydro systems, providing a higher profit at a negligible additional computation time in comparison with a linear optimization method that ignores head-dependency.

Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. (C) 2012 Elsevier Ltd. All rights reserved.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Short-term wind forecasting for energy resources scheduling

This paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.