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.

     

Logging damage and injured tree mortality in tropical forest management

Using insights from the forest ecology literature, we analyze the effect of injured trees on stand composition and carbon stored in above‐ground biomass and the implications for forest management decisions. Results from a Faustmann model with data for a tropical forest on Kalimantan show that up to 50% of the basal area of the stand before harvest can consist of injured trees. Considering injured trees leads to an increase in the amount of carbon in above‐ground biomass of up to 165%. These effects are larger under reduced impact logging than under conventional logging. The effects on land expectation value and cutting cycle are relatively small. The results suggest that considering injured trees in models for tropical forest management is important for the correct assessment of the potential of financial programs to store carbon and conserve forest ecosystem services in managed tropical forests, such as reducing emissions from deforestation and forest degradation and payment for ecosystem services. Recommendations for Resource Managers

• Considering the role of injured trees is important for managing tropical forests
• These trees can cover up to 50% of basal area and contain more than 50% of the carbon stored in above‐ground biomass
• Reduced impact logging leads to a larger basal area of injured trees and more carbon stored in injured trees than conventional logging
• Injured trees play an important role when assessing the potential for carbon storage in the context of payment for forest ecosystem services.

     

Back to the future: A retrospective assessment of model‐based scenarios for the management of the shrimp fishery in French Guiana facing global change

While the number of models dedicated to predicting the consequences of alternative resource management strategies has increased, instances in which authors look back at past predictions to learn from discrepancies between these and observed developments are scarce. In the past decades, the French Guiana shrimp fishery has experienced shrimp market globalization and decreasing levels of shrimp recruitment due to environmental changes. In 2006, a bio‐economic model of this fishery was developed to simulate its possible responses to economic and environmental scenarios up to 2016. Here, we compare here these predictions to the observed trajectories. While the number of active vessels corresponds to that which was predicted, the estimated shrimp stock does not. Important driving factors had not been anticipated, including a general strike, natural disasters, and the end of the global financial crisis. These results show the importance of participative approaches involving stakeholders in the co‐construction and shared representation of scenarios. Recommendations for resource managers

• Effective fisheries resources management and a fortiori, the capacity of the fisheries to adapt to global change, requires understanding of both ecological and economics dynamics.
• The temporal trajectory of the trawling shrimp fisheries has been well monitored, and the decline of both stock and fleet is understood regarding ecological and economic changes: Changes in the environmental conditions of shrimp recruitment, and oil price increase and selling price decrease.
• However, our bio‐economic modeling work showed that, even with a good understanding of the dynamics explaining past trajectories, unpredictable events (strike, natural disasters…) have acted as other key driving factors altering the capacity of the model to represent possible futures.
• These results led us to recommend a better integration of the expertise of social and political scientists in developing models of bio‐economic systems to increase the quality of scenario predictions, and to argue for more participative approaches involving the stakeholders.

     

Interlaced solitons and vortices in coupled DNLS lattices

In the present work, we propose a new set of coherent structures that arise in nonlinear dynamical lattices with more than one component, namely interlaced solitons. In the anti-continuum limit of uncoupled sites, these are waveforms whose one component has support where the other component does not. We illustrate systematically how one can combine dynamically stable unary patterns to create stable ones for the binary case of two-components. For the one-dimensional setting, we provide a detailed theoretical analysis of the existence and stability of these waveforms, while in higher dimensions, where such analytical computations are far more involved, we resort to corresponding numerical computations. Lastly, we perform direct numerical simulations to showcase how these structures break up, when they are exponentially or oscillatorily unstable, to structures with a smaller number of participating sites.     

Cure Kinetics of Poly(acrylonitrile-butadiene-styrene) Modified Epoxy–Amine System

The cure kinetics of epoxy based on the diglycidyl ether of bisphenol A (DGEBA) modified with different amounts of poly(acrylonitrile-butadiene-styrene) (ABS) and cured with 4,4′-diaminodiphenylsulfone (DDS) was investigated by employing differential scanning calorimetry (DSC). The curing reaction was followed by using an isothermal approach over the temperature range 150–180°C. The amount of ABS in the blends was 3.6, 6.9, 10 and 12.9 wt%. Blending of ABS in the epoxy monomer did not change the reaction mechanism of the epoxy network formation, but the reaction rate seems to be decreased with the addition of the thermoplastic. A phenomenological kinetic model was used for kinetic analysis. Activation energies and kinetic parameters were determined by fitting the kinetic model with experimental data. Diffusion control was incorporated to describe the cure in the latter stages, predicting the cure kinetics over the whole range of conversion. The reaction rates for the epoxy blends were found to be lower than that of the neat epoxy. The reaction rates decreased when the ABS contents was increased, due to the dilution effect caused by the ABS on the epoxy/amine reaction mixture.     

Measurement and modeling of metoclopramide hydrochloride (anti-emetic drug) solubility in supercritical carbon dioxide

Knowledge of drug solubility data in supercritical carbon dioxide (SC-CO₂) is a fundamental step in producing nano and microparticles through supercritical fluid technology. In this work, for the first time, the solubility of metoclopramide hydrochloride (MCP) in SC-CO₂ was measured in pressure and temperature range of 12 to 27 MPa and 308 to 338 K, respectively. The results represented a range mole fractions of 0.15 × 10^-5 to 5.56 × 10^-5. To expand the application of the obtained data, six semi-empirical models and three models based on the Peng-Robinson equation of state (PR + VDW, PR + WS + Wilson and PR + MHV1 + COSMOSAC) with different mixing rules and various ways to describe intermolecular interactions were investigated. Furthermore, total enthalpy, sublimation enthalpy and solvation enthalpy relevant to MCP solvating in SC-CO₂ were estimated.     

Prediction of an outcome using NETwork Clusters (NET-C)

Birth weight is a key consequence of environmental exposures and metabolic alterations and can influence lifelong health. While a number of methods have been used to examine associations of trace element (including essential nutrients and toxic metals) concentrations or metabolite concentrations with a health outcome, birth weight, studies evaluating how the coexistence of these factors impacts birth weight are extremely limited. Here, we present a novel algorithm NETwork Clusters (NET-C), to improve the prediction of outcome by considering the interactions of features in the network and then apply this method to predict birth weight by jointly modelling trace element and cord blood metabolite data. Specifically, by using trace element and/or metabolite subnetworks as groups, we apply group lasso to estimate birth weight. We conducted statistical simulation studies to examine how both sample size and correlations between grouped features and the outcome affect prediction performance. We showed that in terms of prediction error, our proposed method outperformed other methods such as (a) group lasso with groups defined by hierarchical clustering, (b) random forest regression and (c) neural networks. We applied our method to data ascertained as part of the New Hampshire Birth Cohort Study on trace elements, metabolites and birth outcomes, adjusting for other covariates such as maternal body mass index (BMI) and enrollment age. Our proposed method can be applied to a variety of similarly structured high-dimensional datasets to predict health outcomes.     

Selection of the most effective kinetic model of lactase hydrolysis by immobilized Aspergillus niger and free β-galactosidase

The kinetic model of the hydrolysis of lactose by β-galactosidase from Aspergillus niger immobilized on a commercial ceramic monoliths was estimated in the attendance of lactose and its hydrolysis reaction products galactose and glucose. The aim of this work was to developing kinetic model of lactase hydrolysis by Aspergillus niger. The variables in this study are temperature, pH, enzyme concentration, substrate concentration and final product. The optimum temperature used to achieve the best hydrolysis performance in the kinetic model selection was 55 and 60 °C. The optimum pH used for enzyme activity was about 3.5 to 4. Five kinetic models were proposed to confirm experimental data the enzymatic reaction of the lactose hydrolysis by the β-galactosidase. The kinetics of lactose hydrolysis by both Immobilized and soluble lactases were scrutinized in a batch reactor system in the lack of any mass conduction restriction. In both instance the galactose inhibition kinetic models predicted the experimental data. The model is capable to fit the experimental data correctly in the extensive experimental span studied.     

Shannon entropy as a predictor of avoided crossing in confined atoms

Avoided crossing is one of the unique spectroscopic features of a confined atomic system. Shannon information entropy of the ground state and some of the excited states of confined H atom as a predictor of avoided crossing is studied in this work. This is accomplished by varying the strength of the confinement and examining structure properties like ionization energy and Shannon information entropy. Along with the energy level repulsion at the avoided crossing, Shannon information entropy is also exchanged between the involved states. This work also addresses a question: In addition to that regarding localization, what other property of the system can be extracted from Shannon entropy? Insightful connection is discovered between Shannon entropy and the average value of confinement potential, Coulomb potential, and kinetic energy.