Use of isotope dilution method to predict bioavailability of organic pollutants in historically contaminated sediments.

Many cases of severe environmental contamination arise from historical episodes, where recalcitrant contaminants have resided in the environment for a prolonged time, leading to potentially decreased bioavailability. Use of bioavailable concentrations over bulk chemical levels improves risk assessment and may play a critical role in determining the need for remediation or assessing the effectiveness of risk mitigation operations. In this study, we applied the principle of isotope dilution to quantify bioaccessibility of legacy contaminants DDT and PCBs in marine sediments from a Superfund site. After addition of 13C or deuterated analogues to a sediment sample, the isotope dilution reached a steady state within 24 h of mixing. At the steady state, the accessible fraction (E) derived by the isotope dilution method (IDM) ranged from 0.28 to 0.89 and was substantially smaller than 1 for most compounds, indicating reduced availability of the extensively aged residues. A strong linear relationship (R2=0.86) was found between E and the sum of rapid (Fr) and slow (Fs) desorption fractions determined by sequential Tenax desorption. The IDM-derived accessible concentration (Ce) was further shown to correlate closely with tissue residue in the marine benthic polychaete Neanthes arenaceodentata exposed in the same sediments. As shown in this study, the IDM approach involves only a few simple steps and may be readily adopted in laboratories equipped with mass spectrometers. This novel method is expected to be especially useful for historically contaminated sediments or soils, for which contaminant bioavailability may have changed significantly due to aging and other sequestration processes

Perception Driven Texture Generation

This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes have not been well studied yet. Meanwhile, perceptual attributes, such as directionality, regularity and roughness are important factors for human observers to describe a texture. In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input. In this model, a preliminary trained convolutional neural network is essentially integrated with the adversarial framework, which can drive the generated textures to possess given perceptual attributes. An important aspect of the proposed model is that, if we change one of the input perceptual features, the corresponding appearance of the generated textures will also be changed. We design several experiments to validate the effectiveness of the proposed method. The results show that the proposed method can produce high quality texture images with desired perceptual properties.Comment: 7 pages, 4 figures, icme201

Evolution of particle size distribution in air in the rainfall process via the moment method

Population balance equation is converted to three moment equations to describe the dynamical behavior of particle size distribution in air in the rainfall. The scavenging coefficient is expressed as a polynomial function of the particle diameter, the raindrop diameter and the raindrop velocity. The evolutions of particle size distribution are simulated numerically and the effects of the raindrop size distribution on particle size distribution are studied. The results show that the raindrops with smaller geometric mean diameter and geometric standard deviation of size remove particles much more efficiently. The particles which fall in the “greenfield gap” are the most difficult to be scavenged from the air

Modular symmetry origin of texture zeros and quark lepton unification

The even weight modular forms of level $N$ can be arranged into the common irreducible representations of the inhomogeneous finite modular group $\Gamma_N$ and the homogeneous finite modular group $\Gamma'_N$ which is the double covering of $\Gamma_N$, and the odd weight modular forms of level $N$ transform in the new representations of $\Gamma'_N$. We find that the above structure of modular forms can naturally generate texture zeros of the fermion mass matrices if we properly assign the representations and weights of the matter fields under the modular group. We perform a comprehensive analysis for the $\Gamma'_3\cong T'$ modular symmetry. The three generations of left-handed quarks are assumed to transform as a doublet and a singlet of $T'$, we find six possible texture zeros structures of quark mass matrix up to row and column permutations. We present five benchmark quark models which can produce very good fit to the experimental data. These quark models are further extended to include lepton sector, the resulting models can give a unified description of both quark and lepton masses and flavor mixing simultaneously although they contain less number of free parameters than the observables.Comment: 36 pages, 2 figur

Exponential synchronization for reaction-diffusion neural networks with mixed time-varying delays via periodically intermittent control

This paper deals with the exponential synchronization problem for reaction-diffusion neural networks with mixed time-varying delays and stochastic disturbance. By using stochastic analysis approaches and constructing a novel Lyapunov–Krasovskii functional, a periodically intermittent controller is first proposed to guarantee the exponential synchronization of reaction-diffusion neural networks with mixed time-varying delays and stochastic disturbance in terms of p-norm. The obtained synchronization results are easy to check and improve upon the existing ones. Particularly, the traditional assumptions on control width and time-varying delays are removed in this paper. This paper also presents two illustrative examples and uses simulated results of these examples to show the feasibility and effectiveness of the proposed scheme