Prediction interval estimation for electricity price and demand using support vector machines

Uncertainty is known to be a concomitant factor of almost all the real world commodities such as oil prices, stock prices, sales and demand of products. As a consequence, forecasting problems are becoming more and more challenging and ridden with uncertainty. Such uncertainties are generally quantified by statistical tools such as prediction intervals (Pis). Pis quantify the uncertainty related to forecasts by estimating the ranges of the targeted quantities. Pis generated by traditional neural network based approaches are limited by high computational burden and impractical assumptions about the distribution of the data. A novel technique for constructing high quality Pis using support vector machines (SVMs) is being proposed in this paper. The proposed technique directly estimates the upper and lower bounds of the PI in a short time and without any assumptions about the data distribution. The SVM parameters are tuned using particle swarm optimization technique by minimization of a modified Pi-based objective function. Electricity price and demand data of the Ontario electricity market is used to validate the performance of the proposed technique. Several case studies for different months indicate the superior performance of the proposed method in terms of high quality PI generation and shorter computational times.

Heritage under fire: lessons from Iraq for cultural property protection

This chapter begins with a discussion of the impact of the Iraq War and subsequent occupation (2003-–2011) on Iraq’s heritage, documenting the most significant and devastating instances of heritage damage and destruction that occurred. Moving forward, this chapter continues with a discussion of the grave challenges facing Iraqi heritage beyond the withdrawal of US military forces in the forms of development, neglect, continued hostilities, and inexpert and haphazard excavation, preservation, protection, and restoration. Despite this troubling scenario, this chapter also examines the extent to which the Iraq conflict was a turning point for major Western military operations and the development of CPP programs which aim to better prepare military personnel for protecting cultural property in future conflicts.

Changes in mitochondrial function and mitochondria associated protein expression in response to 2-weeks of high intensity interval training

PURPOSE: High-intensity short-duration interval training (HIT) stimulates functional and metabolic adaptation in skeletal muscle, but the influence of HIT on mitochondrial function remains poorly studied in humans. Mitochondrial metabolism as well as mitochondrial-associated protein expression were tested in untrained participants performing HIT over a 2-week period. METHODS: Eight males performed a single-leg cycling protocol (12 × 1 min intervals at 120% peak power output, 90 s recovery, 4 days/week). Muscle biopsies (vastus lateralis) were taken pre- and post-HIT. Mitochondrial respiration in permeabilized fibers, citrate synthase (CS) activity and protein expression of peroxisome proliferator-activated receptor gamma coactivator (PGC-1α) and respiratory complex components were measured. RESULTS: HIT training improved peak power and time to fatigue. Increases in absolute oxidative phosphorylation (OXPHOS) capacities and CS activity were observed, but not in the ratio of CCO to the electron transport system (CCO/ETS), the respiratory control ratios (RCR-1 and RCR-2) or mitochondrial-associated protein expression. Specific increases in OXPHOS flux were not apparent after normalization to CS, indicating that gross changes mainly resulted from increased mitochondrial mass. CONCLUSION: Over only 2 weeks HIT significantly increased mitochondrial function in skeletal muscle independently of detectable changes in mitochondrial-associated and mitogenic protein expression.

Testing for stock return predictability in a large Chinese panel

This paper proposes a simple panel data test for stock return predictability that is flexible enough to accommodate three key salient features of the data, namely, predictor persistency and endogeneity, and cross-sectional dependence. Using a large panel of Chinese stock market data comprising more than one million observations, we show that most financial and macroeconomic predictors are in fact able to predict returns. We also show how the extent of the predictability varies across industries and firm sizes.

EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems

The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.

Medical data classification using interval type-2 fuzzy logic system and wavelets

This paper introduces an automated medical data classification method using wavelet transformation (WT) and interval type-2 fuzzy logic system (IT2FLS). Wavelet coefficients, which serve as inputs to the IT2FLS, are a compact form of original data but they exhibits highly discriminative features. The integration between WT and IT2FLS aims to cope with both high-dimensional data challenge and uncertainty. IT2FLS utilizes a hybrid learning process comprising unsupervised structure learning by the fuzzy c-means (FCM) clustering and supervised parameter tuning by genetic algorithm. This learning process is computationally expensive, especially when employed with high-dimensional data. The application of WT therefore reduces computational burden and enhances performance of IT2FLS. Experiments are implemented with two frequently used medical datasets from the UCI Repository for machine learning: the Wisconsin breast cancer and Cleveland heart disease. A number of important metrics are computed to measure the performance of the classification. They consist of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. Results demonstrate a significant dominance of the wavelet-IT2FLS approach compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus useful as a decision support system for clinicians and practitioners in the medical practice. copy; 2015 Elsevier B.V. All rights reserved.

Prediction interval estimation of electricity prices using PSO-tuned support vector machines

Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.

The impact of sleep restriction while performing simulated physical firefighting work on cortisol and heart rate responses

PURPOSE: Physical work and sleep restriction are two stressors faced by firefighters, yet the combined impact these demands have on firefighters' acute stress responses is poorly understood. The purpose of the present study was to assess the effect firefighting work and sleep restriction have on firefighters' acute cortisol and heart rate (HR) responses during a simulated 3-day and 2-night fire-ground deployment. METHODS: Firefighters completed multiple days of simulated physical work separated by either an 8-h (control condition; n = 18) or 4-h sleep opportunity (sleep restriction condition; n = 17). Salivary cortisol was sampled every 2 h, and HR was measured continuously each day. RESULTS: On day 2 and day 3 of the deployment, the sleep restriction condition exhibited a significantly higher daily area under the curve cortisol level and an elevated cortisol profile in the afternoon and evening when compared with the control condition. Firefighters' HR decreased across the simulation, but there were no significant differences found between conditions. CONCLUSION: Findings highlight the protective role an 8-h sleep opportunity between shifts of firefighting work has on preserving normal cortisol levels when compared to a 4-h sleep opportunity which resulted in elevated afternoon and evening cortisol. Given the adverse health outcomes associated with chronically high cortisol, especially later in the day, future research should examine how prolonged exposure to firefighting work (including restricted sleep) affects firefighters' cortisol levels long term. Furthermore, monitoring cortisol levels post-deployment will determine the minimum recovery time firefighters need to safely return to the fire-ground.

No news is not good news: evidence from the intra-day return volatility–volume relationship in Shanghai Stock Exchange

Through this research, we find that the asymmetric volatility phenomenon is reversed in the Shanghai Stock Exchange during bull markets. That is, volatility increases more with good news than with bad news. This evidence is inconsistent with the US markets. Further examination of this phenomenon reveals that the positive impact of good news on volatility is driven by the return-chasing behaviour of investors during bull markets. We also find that volatility increases after stock price declines in bear markets. After controlling for liquidity shifts, we observe similar patterns in volatility in both bull and bear markets. We posit that institutional and behavioural factors are the major driving forces of observed volatility patterns in the Chinese stock market.

Sprint interval and moderate-intensity continuous training have equal benefits on aerobic capacity, insulin sensitivity, muscle capillarisation and endothelial eNOS/NAD(P)Hoxidase protein ratio in obese men

Key points: Skeletal muscle capillary density and vasoreactivity are reduced in obesity, due to reduced nitric oxide bioavailability. Sprint interval training (SIT) has been proposed as a time efficient alternative to moderate-intensity continuous training (MICT), but its effect on the skeletal muscle microvasculature has not been studied in obese individuals. We observed that SIT and MICT led to equal increases in capillarisation and endothelial eNOS content, while reducing endothelial NOX2 content in microvessels of young obese men. We conclude that SIT is equally effective at improving skeletal muscle capillarisation and endothelial enzyme balance, while being a time efficient alternative to traditional MICT. Sprint interval training (SIT) has been proposed as a time efficient alternative to moderate-intensity continuous training (MICT), leading to similar improvements in skeletal muscle capillary density and microvascular function in young healthy humans. In this study we made the first comparisons of the muscle microvascular response to SIT and MICT in an obese population. Sixteen young obese men (age 25 ± 1 years, BMI 34.8 ± 0.9 kg m-2) were randomly assigned to 4 weeks of MICT (40-60 min cycling at ∼65% V˙O2 peak , 5 times per week) or constant load SIT (4-7 constant workload intervals of 200% Wmax 3 times per week). Muscle biopsies were taken before and after training from the m. vastus lateralis to measure muscle microvascular endothelial eNOS content, eNOS serine1177 phosphorylation, NOX2 content and capillarisation using quantitative immunofluorescence microscopy. Maximal aerobic capacity (V˙O2 peak ), whole body insulin sensitivity and arterial stiffness were also assessed. SIT and MICT increased skeletal muscle microvascular eNOS content and eNOS ser1177 phosphorylation in terminal arterioles and capillaries (P < 0.05), but the latter effect was eliminated when normalised to eNOS content (P = 0.217). SIT and MICT also reduced microvascular endothelial NOX2 content (P < 0.05) and both increased capillary density and capillary-fibre perimeter exchange index (P < 0.05). In parallel, SIT and MICT increased V˙O2 peak (P < 0.05) and whole body insulin sensitivity (P < 0.05), and reduced central artery stiffness (P < 0.05). As no significant differences were observed between SIT and MICT it is concluded that SIT is a time efficient alternative to MICT to improve aerobic capacity, insulin sensitivity and muscle capillarisation and endothelial eNOS/NAD(P)Hoxidase protein ratio in young obese men.

What doesn’t kill you makes you fitter: A systematic review of high-intensity interval exercise for patients with cardiovascular and metabolic diseases

High-intensity interval exercise (HIIE) has gained popularity in recent years for patients with cardiovascular and metabolic diseases. Despite potential benefits, concerns remain about the safety of the acute response (during and/or within 24 hours postexercise) to a single session of HIIE for these cohorts. Therefore, the aim of this study was to perform a systematic review to evaluate the safety of acute HIIE for people with cardiometabolic diseases. Electronic databases were searched for studies published prior to January 2015, which reported the acute responses of patients with cardiometabolic diseases to HIIE (≥80% peak power output or ≥85% peak aerobic power, VO2peak). Eleven studies met the inclusion criteria (n = 156; clinically stable, aged 27-66 years), with 13 adverse responses reported (∼8% of individuals). The rate of adverse responses is somewhat higher compared to the previously reported risk during moderate-intensity exercise. Caution must be taken when prescribing HIIE to patients with cardiometabolic disease. Patients who wish to perform HIIE should be clinically stable, have had recent exposure to at least regular moderate-intensity exercise, and have appropriate supervision and monitoring during and after the exercise session.

Camus, philosophe: to return to our beginnings

Camus, Philosophe: To Return to our Beginnings is the first book on Camus to read Camus in light of, and critical dialogue with, subsequent French and European philosophy. It argues that, while not an academic philosopher, Albert Camus was a philosophe in more profound senses looking back to classical precedents, and the engaged French lumières of the 18th century. Aiming his essays and literary writings at the wider reading public, Camus’ criticism of the forms of ‘political theology’ enshrined in fascist and Stalinist regimes singles him out markedly from more recent theological and messianic turns in French thought. His defense of classical thought, turning around the notions of natural beauty, a limit, and mesure makes him a singularly relevant figure given today’s continuing debates about climate change, as well as the way forward for the post-Marxian Left.

Book-to-market ratio, default risk and return implications: from a negative perspective

A paradox is created by the common practice in stock evaluation models of excluding stocks with a negative book equity (BE). If we interpret the book-to-market ratio as a proxy for distress risk, it makes no sense to exclude these negative BE stocks since they are, prima facie, most prone to distress risk. This paper reassesses the relationship between default risk, return and the book-to-market ratio by incorporating negative BE stocks into the study. We find that negative BE stocks carry higher default risks than their positive BE counterparts and that these risks are not totally offset by higher returns. This suggests that a default risk filter can be used in the investment universe selection process through which the portfolio return can be enhanced.