Speciation of lead in soils and relation with its concentration in fruits

The lead concentrations of the fruit samples were determined by using slotted tube atom trap (STAT)-enrichment-flame and graphite furnace atomic absorption spectrometry (FAAS and GF-AAS). The soil samples related to these fruits were also analysed for Pb after extraction with various chemical reagents. The relation between the fruit-lead and soil-extractable lead concentrations was examined in order to explain the bioavailability of lead. A linear relation was observed between the hot Na₂EDTA extraction-soluble Pb contents in the soil and the Pb concentrations in the mulberry (R²=0.95), strawberry and apple grown on these soils. Probable chemical forms of lead in soil were evaluated. Acceptable agreement (at least 91%) was achieved between the results of STAT-enrichment-FAAS and GF-AAS.     

Nonlinear theory of polarization-mode dispersion for fiber solitons

We consider the evolution of optical solitons inside a nonlinear dispersive fiber with random birefringence, causing polarization-mode dispersion. We convert the pair of coupled nonlinear Schr?dinger equations satisfied by the orthogonally polarized components into a Fokker-Planck equation using the collective-variable approach. We solve this equation and derive expressions for the probability density functions associated with the differential group delay and the pulse width in the limit of large propagation distances.     

Selecting rows and columns for training support vector regression models with large retail datasets

Although support vector regression models are being used successfully in various applications, the size of the business datasets with millions of observations and thousands of variables makes training them difficult, if not impossible to solve. This paper introduces the Row and Column Selection Algorithm (ROCSA) to select a small but informative dataset for training support vector regression models with standard SVM tools. ROCSA uses ε-SVR models with L₁-norm regularization of the dual and primal variables for the row and column selection steps, respectively. The first step involves parallel processing of data chunks and selects a fraction of the original observations that are either representative of the pattern identified in the chunk, or represent those observations that do not fit the identified pattern. The column selection step dramatically reduces the number of variables and the multicolinearity in the dataset, increasing the interpretability of the resulting models and their ease of maintenance. Evaluated on six retail datasets from two countries and a publicly available research dataset, the reduced ROCSA training data improves the predictive accuracy on average by 39% compared with the original dataset when trained with standard SVM tools. Comparison with the ε SSVR method using reduced kernel technique shows similar performance improvement. Training a standard SVM tool with the ROCSA selected observations improves the predictive accuracy on average by 21% compared to the practical approach of random sampling.     

The splittable flow arc set with capacity and minimum load constraints

We study the convex hull of the splittable flow arc set with capacity and minimum load constraints. This set arises as a relaxation of problems where clients have demand for a resource that can be installed in integer amounts and that has capacity limitations and lower bounds on utilization. We prove that the convex hull of this set is the intersection of the convex hull of the set with a capacity constraint and the convex hull of the set with a minimum load constraint.     

The comparison of relaxation events with spectroscopic properties of polymer composites with fibrillar dopings on the basis of low density polyethylene and polypropylene

In this study, the mechanical and electrical degradation properties of the composites of polypropylene (PP)+PP fibers, low-density polyethylene(LDPE)+PP fiber, and PP+fiber glass have been investigated and the validity of Zhourkov's thermofluctuation theory has been shown.