Extreme Value Theory and Value at Risk: Application to oil market

Recent increases in energy prices, especially oil prices, have become a principal concern for consumers, corporations, and governments. Most analysts believe that oil price fluctuations have considerable consequences on economic activity. Oil markets have become relatively free, resulting in a high degree of oil-price volatility and generating radical changes to world energy and oil industries. Consequently, oil markets are naturally vulnerable to significant high price shifts. An example of such a case is the oil embargo crisis of 1973. In this newly created climate, protection against market risk has become a necessity. Value at Risk (VaR) measures risk exposure at a given probability level and is very important for risk management. Appealing aspects of Extreme Value Theory (EVT) have made convincing arguments for its use in managing energy price risks. In this paper, we model VaR for long and short trading positions in oil market by applying both unconditional and conditional EVT models to forecast Value at Risk. These models are compared to the performances of other well-known modelling techniques, such as GARCH, Historical Simulation and Filtered Historical Simulation. Both conditional EVT and Filtered Historical Simulation procedures offer a major improvement over the conventional methods. Furthermore, GARCH(1, 1)-t model may provide equally good results which are comparable to two combined procedures. Finally, our results confirm the importance of filtering process for the success of standard approaches.     

Universal mitochondrial PCR combined with species-specific dot-blot assay as a source-tracking method of human, bovine, chicken, ovine, and porcine in fecal-contaminated surface water

Mitochondrial (mt) DNA has the potential to be used as an animal-specific genetic marker for source-tracking of fecal contamination in surface waters and groundwaters. In this report, we describe the development of a method to detect in a single assay human, bovine, ovine, porcine and chicken mitochondrial (mt) DNA in water. Consensus nucleic sequences were found between human, bovine, porcine, ovine and chicken mtDNA to design three sets of PCR universal primers. Upon polymerase chain reaction (PCR) with a digoxigenin-labeled nucleotide and the universal primers, species determination was carried out by dot-blotting membranes containing specific oligonucleotides for these five animals. Our method was carried out with three river samples and three wastewater samples, and the results were compared with those obtained by multiple nested PCR with specific primers for these five species. Our results showed that the dot-blot assays were as specific and sensitive as the nested-PCR approach. The proposed method has the advantage that it requires the use of only one PCR per sample and very little amounts of DNA. Finally, it is an alternative to multiplex PCR approach which is less sensitive, and shows the way for the development of DNA arrays for source-tracking of many more animal species in fecal-contaminated water.     

PIXE analysis of medieval silver coins

We applied the proton-induced X-ray emission (PIXE) analytical technique to twenty-eight medieval silver coins, selected from the Tunisian treasury. The purpose is to study the fineness evolution from the beginning of the 7th to the 15th centuries AD. Each silver coin was cleaned with a diluted acid solution and then exposed to a 3 MeV proton beam from a 1.7 MV tandem accelerator. To allow the simultaneous detection of light and heavy elements, a funny aluminum filter was positioned in front of the Si(Li) detector entrance which is placed at 135° to the beam direction. The elements Cu, Pb, and Au were observed in the studied coins along with the major component silver. The concentration of Ag, presumably the main constituent of the coins, varies from 55% to 99%. This significant variation in the concentration of the major constituent reveals the economical difficulties encountered by each dynasty. It could be also attributed to differences in the composition of the silver mines used to strike the coins in different locations. That fineness evolution also reflects the poor quality of the control practices during this medieval period. In order to verify the ability of PIXE analytical method to distinguish between apparently similar coins, we applied hierarchical cluster analysis to our results to classify them into different subgroups of similar elemental composition.     

Assessment of temperature gradient effects on moisture transfer through thermogradient coefficient

The objective of this paper is to describe moisture transfer through porous material due to temperature gradient. For that purpose, an experimental device was set up to assess moisture flux under isothermal and non-isothermal conditions. This involves placing samples between two compartments with controlled air conditions and monitoring relative humidity and temperature profiles inside the samples over the time. To interpret these results, we proposed to express the mass flux in terms of two driving potentials: water vapor content gradient and temperature gradient. Accordingly, thermogradient coefficient was calculated and discussed. It represents the difference between the moisture fluxes under isothermal and non-isothermal conditions. The impact of temperature gradient on the moisture buffer value (MBV) was also considered through a numerical experiment taking into account thermogradient coefficient. Results show that temperature gradient implies a relative variation of the MBV for about 14%. Thus, it would be better to consider non-isothermal conditions for its assessment.     

Insights into the use of landfill leachate to grow Chlorella sp. for lipid and fatty acids production

Clean Technologies and Environmental Policy - Microalgae provide a potential alternative for high-value products. Integrating microalgae into the landfill leachate valorization process gives the...     

A Bayesian perspective on stochastic neurocontrol.

Control design for stochastic uncertain nonlinear systems is traditionally based on minimizing the expected value of a suitably chosen loss function. Moreover, most control methods usually assume the certainty equivalence principle to simplify the problem and make it computationally tractable. We offer an improved probabilistic framework which is not constrained by these previous assumptions, and provides a more natural framework for incorporating and dealing with uncertainty. The focus of this paper is on developing this framework to obtain an optimal control law strategy using a fully probabilistic approach for information extraction from process data, which does not require detailed knowledge of system dynamics. Moreover, the proposed control method framework allows handling the problem of input-dependent noise. A basic paradigm is proposed and the resulting algorithm is discussed. The proposed probabilistic control method is for the general nonlinear class of discrete-time systems. It is demonstrated theoretically on the affine class. A nonlinear simulation example is also provided to validate theoretical development.     

Extracting text from scanned Arabic books: a large-scale benchmark dataset and a fine-tuned Faster-R-CNN model

Datasets of documents in Arabic are urgently needed to promote computer vision and natural language processing research that addresses the specifics of the language. Unfortunately, publicly available Arabic datasets are limited in size and restricted to certain document domains. This paper presents the release of BE-Arabic-9K, a dataset of more than 9000 high-quality scanned images from over 700 Arabic books. Among these, 1500 images have been manually segmented into regions and labeled by their functionality. BE-Arabic-9K includes book pages with a wide variety of complex layouts and page contents, making it suitable for various document layout analysis and text recognition research tasks. The paper also presents a page layout segmentation and text extraction baseline model based on fine-tuned Faster R-CNN structure (FFRA). This baseline model yields cross-validation results with an average accuracy of 99.4% and F1 score of 99.1% for text versus non-text block classification on 1500 annotated images of BE-Arabic-9K. These results are remarkably better than those of the state-of-the-art Arabic book page segmentation system ECDP. FFRA also outperforms three other prior systems when tested on a competition benchmark dataset, making it an outstanding baseline model to challenge.

     

Heuristics and metaheuristics for mixed blocking constraints flowshop scheduling problems

Storage or buffer capacities between successive machines in flowshop problems may be unlimited, limited or null. The last two cases can lead to blocking situations. In flowshop scheduling literature, many studies have been performed about classical flowshop problems and also about some problems with only one blocking situation between all machines.     

A distributed resource management model for Virtual Private Networks: Tit-for-Tat strategies

In this paper, we address the autonomic resource management problem for Virtual Private Networks (VPNs) in the presence of stochastic and selfish VPN operators. Resource management is one of the most important problems that faces Internet Service Providers. In the literature, the Autonomic Service Architecture is proposed to provide a resource management model that allows systems to manage themselves and aiming to utilize optimally the unused resources. Unfortunately, this model suffers from two major limitations. First, unused resources from underloaded VPNs (lenders) are utilized over the overloaded ones (borrowers) without considering the unexpected changes of the VPNs’ state, which may often happen in the case of multimedia transmissions. This may affect negatively the Quality-of-Service (QoS) of the lenders while improving the QoS of the borrowers. Second, underloaded VPNs’ operators might behave selfishly and refuse to lend their unused (spare) resources to other overloaded VPNs. To overcome these limitations, we propose a distributed autonomic resource management, which is modeled as a repeated non-cooperative game with stochastic and selfish players. The classical Tit-for-Tat strategy is modified to cope with VPN operators who are not always able to lend some resources to others. Four different strategies are derived from Tit-for-Tat to motivate VPN operators to lend their resources to others. As far as we know, our work is among the first efforts that uses repeated non-cooperative game theory to motivate selfish participants to cooperate and to distinguish between stochastic and purely selfish VPNs’ operators. In our setting, this results in cooperative sharing of unused resources among VPNs. Simulation results show that Tit-for-Tat strategy leads to deadlocks, while our strategies assure good gains to cooperative VPN operators and punish the selfish ones.     

Investigation of Structural and Optical Properties of Graphene Oxide-Coated Neodymium Nanoparticles Doped Zinc-Tellurite Glass for Glass Fiber

Journal of Inorganic and Organometallic Polymers and Materials - Carbon-based materials such as graphene oxide are known as attractive candidate to improve the current optoelectronic and laser...