State and green crimes related to water pollution and ecological disorganization: water pollution from publicly owned treatment works (POTW) facilities across US states

Green criminologists often refer to water pollution as an example of a green crime, but have yet to produce much research on this subject. The current article addresses the need for green criminological analyses of water pollution problems, and draws attention to an overlooked issue: water pollution emissions from state owned public water treatment facilities or POTWs. Legally, POTWs may emit certain quantities and kinds of pollutants to waterways following treatment. This does not mean, however, that those emissions have no adverse ecological or public health impacts, or that those emissions cannot also be employed as examples of green crimes or green-state crimes. Indeed, from the perspective of environmental sociology and ecological Marxism, those emissions generate ecological disorganization. Moreover, POTW emissions contain numerous pollutants that generate different forms of ecological disorganization. The current study uses POTW emissions data drawn from the US EPA’s Discharge Monitoring Report system for 2014 to illustrate the extent of pollution emitted by POTWs in and across US states as one dimension of ecological disorganization. To contextualize the meaning of those data, we review US water pollution regulations, review the health and ecological impacts of chemicals emitted by POTWs, and situate those emissions within green criminological discussions of green crime and green-state crimes

Convergence and Optimality of Adaptive Mixed Finite Element Methods

The convergence and optimality of adaptive mixed finite element methods for the Poisson equation are established in this paper. The main difficulty for mixed finite element methods is the lack of minimization principle and thus the failure of orthogonality. A quasi-orthogonality property is proved using the fact that the error is orthogonal to the divergence free subspace, while the part of the error that is not divergence free can be bounded by the data oscillation using a discrete stability result. This discrete stability result is also used to get a localized discrete upper bound which is crucial for the proof of the optimality of the adaptive approximation

CONVERGENCE OF MARKOV CHAIN APPROXIMATIONS TO STOCHASTIC REACTION DIFFUSION EQUATIONS

In the context of simulating the transport of a chemical or bacterial contaminant through a moving sheet of water, we extend a well established method of approximating reaction-diffusion equations with Markov chains by allowing convection, certain Poisson measure driving sources and a larger class of reaction functions. Our alterations also feature dramatically slower Markov chain state change rates often yielding a ten to one hundred fold simulation speed increase over the previous version of the method as evidenced in our computer implementations. On a weighted L2 Hilbert space chosen to symmetrize the elliptic operator, we consider existence of and convergence to pathwise unique mild solutions of our stochastic reaction-diffusion equation. Our main convergence result, a quenched law of large numbers, establishes convergence in probability of our Markov chain approximations for each fixed path of our driving Poisson measure source. As a consequence, we also obtain the annealed law of large numbers establishing convergence in probability of our Markov chains to the solution of the stochastic reaction-diffusion equation while considering the Poisson source as a random medium for the Markov chains.

Frequency-modulated nuclear localization bursts coordinate gene regulation

In yeast, the transcription factor Crz1 is dephosphorylated and translocates into the nucleus in response to extracellular calcium. Here we show, using time-lapse microscopy, that Crz1 exhibits short bursts of nuclear localization (typically lasting 2 min) that occur stochastically in individual cells and propagate to the expression of downstream genes. Strikingly, calcium concentration controls the frequency, but not the duration, of localization bursts. Using an analytic model, we also show that this frequency modulation of bursts ensures proportional expression of multiple target genes across a wide dynamic range of expression levels, independent of promoter characteristics. We experimentally confirm this theory with natural and synthetic Crz1 target promoters. Another stress-response transcription factor, Msn2, exhibits similar, but largely uncorrelated, localization bursts under calcium stress suggesting that frequency-modulation regulation of localization bursts may be a general control strategy used by the cell to coordinate multi-gene responses to external signals

Investment Patterns and Financial Leverage

This study Investigates the influence of the type of investment opportunities facing a firm on its choice of capital structure. It is shown that the more discretionary investment opportunities a firm faces,the lower its financial leverage. Inclusion of other possible determinants of capital structure, such as availability of internal funds, tax effects and risk, while significant, do not affect the importance of discretionary investment. The evidence supports (1) the existence of a moral bazzard problem which inversely relates risky debt and discretionary investment choice, and (2) a desire by most firms to use sources of internal funds prior to entering the capital market.

Partial Transfer Learning with Selective Adversarial Networks

Adversarial learning has been successfully embedded into deep networks to learn transferable features, which reduce distribution discrepancy between the source and target domains. Existing domain adversarial networks assume fully shared label space across domains. In the presence of big data, there is strong motivation of transferring both classification and representation models from existing big domains to unknown small domains. This paper introduces partial transfer learning, which relaxes the shared label space assumption to that the target label space is only a subspace of the source label space. Previous methods typically match the whole source domain to the target domain, which are prone to negative transfer for the partial transfer problem. We present Selective Adversarial Network (SAN), which simultaneously circumvents negative transfer by selecting out the outlier source classes and promotes positive transfer by maximally matching the data distributions in the shared label space. Experiments demonstrate that our models exceed state-of-the-art results for partial transfer learning tasks on several benchmark datasets