Applying Dynamic Training‐Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility

Volatility is a key variable in option pricing, trading, and hedging strategies. The purpose of this article is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training‐subset selection methods. These methods manipulate the training data in order to improve the out‐of‐sample patterns fitting. When applied with the static subset selection method using a single training data sample, GP could generate forecasting models, which are not adapted to some out‐of‐sample fitness cases. In order to improve the predictive accuracy of generated GP patterns, dynamic subset selection methods are introduced to the GP algorithm allowing a regular change of the training sample during evolution. Four dynamic training‐subset selection methods are proposed based on random, sequential, or adaptive subset selection. The latest approach uses an adaptive subset weight measuring the sample difficulty according to the fitness cases' errors. Using real data from S&P500 index options, these techniques are compared with the static subset selection method. Based on mean squared error total and percentage of non‐fitted observations, results show that the dynamic approach improves the forecasting performance of the generated GP models, especially those obtained from the adaptive‐random training‐subset selection method applied to the whole set of training samples.     

Evaluating leakages through GMB/GCL composite liners considering random hole distributions in wrinkle networks

This paper presents a study on the development of a hydraulic connectivity analysis-based approach for evaluating leakage rates through geomembrane (GMB)-geosynthetic clay liner (GCL) composite liners considering random hole distributions in a GMB wrinkle network. An algorithm for hydraulic connectivity analysis was developed to find the hydraulically connected wrinkles from a wrinkle network, and an explicitly expressed criterion is proposed to define the hydraulic connection between wrinkles under the assumption that only one of the two adjacent wrinkles is possible to be damaged. A Monte Carlo simulation was used to evaluate the probability weighted average of the total leakage through multiple randomly distributed holes considering numerous possible combinations of locations of holes. The proposed approach was applied to typical examples reported in the literature and shows that it can objectively quantify the effect of the hydraulic properties of the liner and overburden pressure on the hydraulic connectivity between wrinkles in a wrinkle network. The proposed approach also allowed assessing the effect of different probabilities of various hole distributions on the calculated leakage, which was demonstrated to be non-negligible, especially when the hole frequency is small.     

The Solutions on One-Dimensional Dirac Oscillator with Energy-Dependent Potentials and Their Effects on the Shannon and Fisher Quantities of Quantum Information Theory

Journal of Low Temperature Physics - In this paper, we focus, at first, on the exact solutions on the one-dimensional Dirac oscillator with the energy-dependent potentials. Then, the influence of...     

Spray drying of drug-swellable dispersant suspensions for preparation of fast-dissolving,high drug-loaded,surfactant-free nanocomposites

Bioavailability of a poorly soluble drug can be improved by preparing a drug nanosuspension and subsequently drying it into nanocomposite microparticles (NCMPs). Unfortunately, drug nanoparticles aggregate during milling and drying, causing incomplete recovery and slow dissolution. The aim of this study is to investigate the impact of various classes of dispersants on drug dissolution from drug NCMPs, with the ultimate goal of enhancing the bioavailability of poorly water-soluble drugs via high drug nanoparticle loaded, surfactant-free NCMPs. Precursor suspensions of griseofulvin (GF, model drug) nanoparticles in the presence of various dispersants were prepared via wet stirred media milling and spray dried to form the NCMPs. Hydroxypropyl cellulose (HPC, polymer) alone and with sodium dodecyl sulfate (SDS, surfactant) was used as a base-line stabilizer/dispersant during milling. Two swellable crosslinked polymers, croscarmellose sodium (CCS) and sodium starch glycolate (SSG), and a conventional soluble matrix former, Mannitol, were used in addition to HPC. Besides being used as-received, CCS was also wet co-milled with GF for two different durations to examine the impact of CCS particle size. Laser diffraction, scanning electron microscopy, powder X-ray diffraction (XRD), UV spectroscopy, NCMP redispersion and dissolution tests were used for characterization. The results show that incorporation of CCS/SSG, preferably wet-milled to a wide particle size distribution, into the spray-dried NCMPs resulted in fast release and dispersion of drug nanoparticle clusters. The swellable dispersants were superior to Mannitol in dissolution enhancement, and could achieve fast release comparable to SDS, demonstrating the feasibility of spray drying to prepare high drug-loaded, surfactant-free nanocomposites.     

Experimental evaluation of quinolinium and isoquinolinium derivatives as corrosion inhibitors of mild steel in 0.5 M H<Subscript>2</Subscript>SO<Subscript>4</Subscript> solution

The 1-methylquinolinium iodide (I) Qui⁺, I^– and 2-methylisoquinolinium iodide isoQui⁺, I^– were investigated as a corrosion inhibitors for mild steel in sulfuric acid using electrochemical impedance spectroscopy and potentiodynamic polarization techniques. The results indicated that the corrosion inhibition efficiency and extent of surface coverage were increased with increase in inhibitors concentrations. Polarization curves revealed that both inhibitors acted as a mixed-type inhibitor. The thermodynamic parameters were evaluated for corrosion inhibition process. The adsorption of both inhibitors on mild steel surface obeyed Langmuir adsorption isotherm.     

Optimal Heat Input for Estimating Luikov's Parameters in a Heat and Mass Transfer Problem

In this article, the problem of computing an optimal heat input in Luikov's heat and mass transfer problem is detailed and analyzed. The main objective is the establishment of an optimal time-dependent heat flux profile with the goal of maximizing the temperature and moisture sensitivities of some parameters to this excitation in a drying process. Such maximization makes the estimation of the desired parameters possible, easier, and with limited uncertainty intervals. It also helps to reduce the linearity dependence between the parameters of interest and the number of temperature and moisture sensors used. The estimation of the optimal heat input is obtained with Uzawa's algorithm, while the estimation of parameters is performed with Levenberg-Marquardt's method of minimization of the ordinary least-square criterion. The six dimensionless parameters characterizing Luikov's equations are estimated successfully with this optimal heat flux profile, which also helps to reduce the number of both temperature and moisture sensors needed in the estimation procedure. By doing so, the objective of estimating simultaneously the six parameters which appear in the formulation of Luikov's physical problem is reached by using a limited transient temperature and/or moisture measurements taken anywhere in the drying medium.     

Reduction by CO-CO2 and cracking of iron ore sinters,part 2: cracking

The cracking of iron ore sinters during their low-temperature reduction to magnetite, in the upper part of the blast furnace, is a considerable handicap for the operation of the furnace. However, in spite of numerous previous investigations, it is not fully understood. Dealing with two different sinters, a synthetic and an industrial one, their hematite was reduced to magnetite by CO-CO₂, with varied temperatures and gas compositions. Cracking is strongly favoured by low temperature and low CO partial pressure. Under such conditions, the cracks are initiated by localized nucleation and there is no need for extended stress along the hematite-magnetite interface. The crack propagation is independent of the hematite crystals orientation, and the glassy gangue grain boundaries easily transmit the stress.     

In vivo measurement of lower back deformations with Fourier-transform profilometry

Through the variation of their cross sections, the in vivo response of lower back muscles to low loading in an upright seated posture is explored by the Fourier-transform profilometry technique. The maximization of its sensitivity allows us to reach an adequate resolution for the evaluation of low-back displacements. Refinements of the fringe pattern analysis permit the minimization of errors. The experiments show an asymmetric distribution of the displacement during head rotation movements. Significant contribution of the lower back to grasping exertions is also observed. These results are thought to be useful for early defect detection in the lower back.     

Hysteresis of the water retention curves of geosynthetic clay liners in the high suction range

This paper examines two needle-punched geosynthetic clay liners' water retention behaviour at high suction ranges using the vapour equilibrium technique where super-saturated salt solutions controlled the relative humidity. This study shows that the bentonite form and its mineralogy affect the absorption/desorption of GCLs and their corresponding water retention curves. In particular, a granular bentonite-based GCL was found to absorb more and release less water than a powdered bentonite-based GCL due to its higher montmorillonite content and larger pores. The water retention curves of both GCLs exhibited very little hysteretic behaviour at high suction. Repeated wetting-drying cycles shifted the WRCs of both GCLs slightly downward with minimal impact on their degree of hysteresis.     

Kernelized relative entropy for direct fault detection in industrial rotary kilns

The objective of this work is to use a 1‐dimensional signal that reflects the dissimilarity between multidimensional probability densities for detection. With the modified Kullback‐Leibler divergence, faults can be directly detected without any normality assumption or joint monitoring of related test statistics in different subspaces such as the T² and SPE in principal component analysis–based methods. To relieve the difficulty associated with asymptotic high‐dimensional density estimates, we have estimated the density ratio rather than the densities themselves. This can be done by approximating the density ratio with kernel basis functions and learn the weights from the available data. The developed algorithm is generic and can be applied to any industrial system as long as process historical data is available. As a case study, we apply this algorithm to a real rotary kiln in operation, which is an integral part of the cement manufacturing plant of Ain El Kebira, Algeria.