Neural network modeling of hydrological systems: A review of implementation techniques

This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers

• Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies.

• Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real‐world problems.

• More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.

     

On uniform propagation of chaos

In this paper we obtain a time-uniform propagation estimate for a system of interacting diffusion processes. Using a well defined metric function h , our result guarantees a time-uniform estimate for the convergence of a class of interacting stochastic differential equations towards their mean field limit, under conditions that ensure that the decay associated to the internal dynamics term dominates the interaction and noise terms. Our result should have diverse applications, particularly in neuroscience, and allows for models more elaborate than the one of Wilson and Cowan. In particular, the internal dynamics need not be that of linear decay.     

The lifetime of CFC substitutes studied by a network trained with chaotic mapping modified genetic algorithm and DFT calculations

The hydrohaloalkanes have attracted much attention as potential substitutes of chlorofluorocarbons (CFCs) that deplete the ozone layer and lead to great high global warming. Having a short atmospheric lifetime is very important for the potential substitutes that may also induce ozone depletion and yield high global warming gases to be put in use. Quantitative structure–activity relationship (QSAR) studies were presented for their lifetimes aided by the quantum chemistry parameters including net charges, Mulliken overlaps, E _HOMO and E _LUMO based on the density functional theory (DFT) at B3PW91 level, and the C-H bond dissociation energy based on AM1 calculations. Outstanding features of the logistic mapping, a simple chaotic system, especially the inherent ability to search the space of interest exhaustively have been utilized. The chaotic mapping aided genetic algorithm artificial neural network training scheme (CGANN) showed better performance than the conventional genetic algorithm ANN training when the structure of the data set was not favorable. The lifetimes of HFCs and HCs appeared to be greatly dependent on their energies of the highest occupied molecular orbitals. The perference of the RMSRE comparing to RMSE as objective function of ANN training was better for the samples of interest with relatively short lifetimes. C₂H₆ and C₃H₈ as potential green substitutes of CFCs present relatively short lifetimes.     

Hybrid inorganic–organic poly(carborane‐siloxane‐arylacetylene) structural isomers with in‐chain aromatics: Synthesis and properties

Structural isomers of thermo‐oxidatively stable poly(carborane‐siloxane‐arylacetylene) (PCSAA), namely, m‐PCSAA and p‐PCSAA, were synthesized by the reaction of the dimagnesium salts of m‐diethynylbenzene or p‐diethynylbenzene with 1,7‐bis(chlorotetramethyldisiloxyl)‐m‐carborane. The developed polymers have exceptional thermo‐oxidative properties similar to their diacetylene counterpart poly(carborane‐siloxane‐acetylene), PCSA. Thermal treatment of either of the PCSAAs results in a fully crosslinked thermoset by 500 °C resulting from the cycloaddition reactions involving the acetylene and aryl functionalities and subsequent formation of bridging disilylmethylene entities as discerned from Fourier transform infrared, ¹³C and ²⁹Si solid‐state NMR, and XPS studies. X‐ray diffraction analysis revealed that the thermosets obtained from p‐PCSAA possess enhanced crystallinity when compared to that obtained from m‐PCSAA possibly due to more efficient packing interactions of the p‐diethynylbenzene groups during thermoset formation. The presence of the aryl groups in the backbone of the PCSAAs' chains appeared to have enhanced the storage and bulk moduli of their thermosets when compared to the thermoset of PCSA. Dielectric studies of m‐PCSAA and p‐PCSAA revealed segmental relaxation peaks, α, above their glass transition temperatures with p‐PCSAA exhibiting a broader peak with a slower relaxation rate than m‐PCSAA. © 2013 Wiley Periodicals, Inc.^? J. Polym. Sci., Part A: Polym. Chem. 2013, 51, 2638–2650     

Chain transfer agents in cationic photopolymerization of a bis‐cycloaliphatic epoxide monomer: Kinetic and physical property effects

The effects of chain transfer agents (CTA) on cationic ring‐opening polymerization of 3,4‐epoxycyclohexylmethyl‐3′,4′‐epoxycyclohexane carboxylate (EEC) were explored. EEC was polymerized in the presence of various CTAs, and epoxide conversions monitored via Raman spectroscopy. Polymer films were prepared and analyzed by dynamic mechanical analysis. Many of the organic alcohols studied greatly enhanced epoxide polymerization rates and conversion levels. The gel fraction of polymer specimens decreased rapidly with increasing amounts of octanol (gel fraction >90% up to 0.3 equiv OH) but remained high with increasing amounts of 1,2‐propanediol (gel fraction >90% up to 0.6 equiv OH). Increasing the size of primary alcohols had little effect on the polymerization rates and conversions. The polymerization rate decreased with increasing alcohol substitution (1°>2°>3°). Acidic alcohols had very low impact on conversion and polymerization rates relative to the neat epoxy resin. The glass transition temperature was inversely related to the size and amount of CTA. © 2013 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2013     

MOEPGA: A novel method to detect protein complexes in yeast protein–protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm

The identification of protein complexes in protein–protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.     

An fMRI study of magnitude comparison and exact addition in children

By contrast to the adult literature, in which a consistent parietofrontal network for number processing has been identified, the data from studies of number processing in children have been less consistent, probably due to differences in study design and control conditions. Number processing was examined using functional magnetic resonance imaging in 18 right-handed children (8–12 years) from the Cape Coloured community in Cape Town, South Africa, using Proximity Judgment and Exact Addition (EA) tasks. The findings were consistent with the hypothesis that, as in adults, the anterior horizontal intraparietal sulcus (HIPS) plays a major role in the representation and manipulation of quantity in children. The posterior medial frontal cortex, believed to be involved in performance monitoring in more complex arithmetic manipulations in adults, was extensively activated even for relatively simple symbolic number processing in the children. Other areas activated to a greater degree in the children included the left precentral sulcus, which may mediate number knowledge and, for EA, the head of the caudate nucleus, which is part of a fronto-subcortical circuit involved in the behavioral execution of sequences. Two regions that have been linked to number processing in adults — the angular gyrus and posterior superior parietal lobule — were not activated in the children. The data are consistent with the inference that although the functional specialization of the anterior HIPS may increase as symbolic number processing becomes increasingly automatic, this region and other elements of the parietofrontal network identified in adults are already reliably and robustly activated by middle childhood.     

Cleaving Direct‐Laser‐Written Microstructures on Demand

Using an advanced functional photoresist we introduce direct‐laser‐written (DLW) 3D microstructures capable of complete degradation on demand. The networks consist exclusively of reversible bonds, formed by irradiation of a phenacyl sulfide linker, giving disulfide bonds in a radical‐free step‐growth polymerization via a reactive thioaldehyde. The bond formation was verified in solution by ESI‐MS. To induce cleavage, dithiothreitol causes a thiol–disulfide exchange, erasing the written structure. The mild cleavage of the disulfide network is highly orthogonal to other, for example, acrylate‐based DLW structures. To emphasize this aspect, DLW structures were prepared incorporating reversible structural elements into a non‐reversible acrylate‐based standard scaffold, confirming subsequent selective cleavage. The high lateral resolution achievable was verified by the preparation of well‐defined line gratings with line separations of down to 300 nm.