Flow structure through pool‐riffle sequences and a conceptual model for their sustainability in gravel‐bed rivers

Detailed field measurements and simulations of three‐dimensional flow structure were used to develop a conceptual model to explain the sustainability of self‐formed pool‐riffle sequences in gravel‐bed rivers. The analysis was conducted at the Red River Wildlife Management Area in Idaho, USA, and enabled characterization of the flow structure through two consecutive pool‐riffle sequences, including: identification of jet concentration and dissipation zones, and the development of local turbulence features (i.e. vertical and horizontal eddies) under different flow conditions. Three‐dimensional hydraulic simulations were used to evaluate how the flow structure varies across a range of flow conditions and with different degrees of sediment aggradation within the upstream pool. The analysis demonstrated a significant influence of the residual pool depth on the flow structure, with reduced residual depth causing a shift in the orientation of the jet and a reduction in the influence of vertical eddies and the size and intensity of horizontal eddies. The proposed conceptual model seeks to explain the sustainability of pools in terms of the flow structure in pool‐riffle morphology and how this flow structure will change as a result of altered external forcing, such as upstream sediment delivery or changes in bank stability. Copyright © 2010 John Wiley & Sons, Ltd.     

Effect of Storage Conditions and Antioxidants on the Oxidative Stability of Sunflower–Chia Oil Blends

The mixture of different proportions of sunflower with chia oil provides a simple method to prepare edible oils with a wide range of desired fatty acid compositions. Sunflower–chia (90:10 and 80:20 wt/wt) oil blends with the addition of rosemary (ROS), ascorbyl palmitate (AP) and their blends (AP:ROS) were formulated to evaluate the oxidative stability during storage at two temperature levels normally used, cool (4 ± 1 °C) and room temperature (20 ± 2 °C) for a period of 360 days. Peroxide values (PV) of the oil blends with antioxidants stored at 4 ± 1 °C showed levels ≤10.0 mequiv O₂/kg oil; the lowest levels of PV were found for blends with AP:ROS. Values higher than 10.0 mequiv O₂/kg were observed between 120–240 days for oil blends stored at 20 ± 2 °C. Similar trends were observed with p-anisidine and Totox values. The oxidative stability determined by the Rancimat method and differential scanning calorimetry showed a greater susceptibility of the oils to oxidative deterioration with increasing unsaturated fatty acids content. The addition of antioxidants increased the induction time and decreased the Arrhenius rate constant, indicating an improvement in the oxidative stability for all the oil blends. Temperature had a strong influence on the stability of these blends during storage.     

Multi-Agent System Development Based on Organizations

Organizational models have been recently used in agent theory for modeling coordination in open systems and to ensure social order in multi-agent system applications. In this paper, we propose the employment of Organization Theory for the analysis and design of multiagent systems. Thus, we first discuss the current state of the art of organization-oriented multiagent system methods, placing emphasis on their organizational features. We also review human organizational structures, and we propose several guidelines for implementing agent organizations by means of Organization Theory. Our final aim is to employ well-known human organizational structures to develop multiagent systems.     

Prenylated Flavonoids with Potential Antimicrobial Activity: Synthesis,Biological Activity,and In Silico Study

Prenylated flavonoids are an important class of naturally occurring flavonoids with important biological activity, but their low abundance in nature limits their application in medicines. Here, we showed the hemisynthesis and the determination of various biological activities of seven prenylated flavonoids, named 7–13, with an emphasis on antimicrobial ones. Compounds 9, 11, and 12 showed inhibitory activity against human pathogenic fungi. Compounds 11, 12 (flavanones) and 13 (isoflavone) were the most active against clinical isolated Staphylococcus aureus MRSA, showing that structural requirements as prenylation at position C-6 or C-8 and OH at positions C-5, 7, and 4′ are key to the antibacterial activity. The combination of 11 or 12 with commercial antibiotics synergistically enhanced the antibacterial activity of vancomycin, ciprofloxacin, and methicillin in a factor of 10 to 100 times against drug-resistant bacteria. Compound 11 combined with ciprofloxacin was able to decrease the levels of ROS generated by ciprofloxacin. According to docking results of S enantiomer of 11 with ATP-binding cassette transporter showed the most favorable binding energy; however, more studies are needed to support this result.     

Compatibilization of poly(3‐hydroxybutyrate‐co‐3‐hydroxyvalerate)–poly(lactic acid) blends with diisocyanates

Poly(3‐hydroxybutyrate‐co‐3‐hydroxyvalerate) (PHBV) was blended with poly(lactic acid) (PLA) with various reactive processing agents to decrease its brittleness and enhance its processability. Three diisocyanates, namely, hexamethylene diisocyanate, poly(hexamethylene diisocyanate), and 1,4‐phenylene diisocyanate, were used as compatibilizing agents. The morphology, thermomechanical properties, and rheological behavior were investigated with scanning electron microscopy, thermogravimetric analysis, differential scanning calorimetry, tensile testing, dynamomechanical thermal analysis in torsion mode (dynamic mechanical analysis), and oscillatory rheometry with a parallel‐plate setup. The presence of the diisocyanates resulted in an enhanced polymer blend compatibility; this led to an improvement in the overall mechanical performance but did not affect the thermal stability of the system. A slight reduction in the PHBV crystallinity was observed with the incorporation of the diisocyanates. The addition of diisocyanates to the PHBV–PLA blend resulted in a notable increase in the final complex viscosity at low frequencies when compared with the same system without compatibilizers. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44806.     

Downstream environmental effects of dam operations: Changes in habitat quality for native fish species

Hydropeaking dam operation and water extractions for irrigation have been broadly stated as alterations to natural flow regimes, which have also been noticed in the Biobío Watershed, in Central Chile, since 1996. In the Biobío River, most of native fish species are endemic and very little is known about them. Their ecological and social values have never been estimated, and there is lack of information about their habitat preferences. Furthermore, changes on fish habitat availability due to natural and/or man‐made causes have not been evaluated. In this study, eight native fish species, in a representative reach of the Biobío River, were studied and their preferred habitats were surveyed and characterized. A hydrodynamic model was built and linked to the fish habitat simulation model CASiMiR. Fuzzy rules and fuzzy sets were developed for describing habitat preference of the native fish species. CASiMiR was then used to simulate how physical habitat conditions vary due to flow control (i.e. upstream dams). Results show how overall habitat quality, expressed as weighted usable area (WUA) and hydraulic habitat suitability (HHS), changes and fluctuates due to the dam operation and how the daily hydropeaking is influencing quantity, quality and location of different habitats. The study suggests that the analysed fish are highly susceptible to flow control, as dams are currently operated, and fish habitat improvement suggestions are proposed. Copyright © 2010 John Wiley & Sons, Ltd.     

Introducing Synaptic Delays in the NEAT Algorithm to Improve Modelling in Cognitive Robotics

This paper describes and tests an approach to improve the temporal processing capabilities of the neuroevolution of augmenting topologies (NEAT) algorithm. This algorithm is quite popular within the robotics community for the production of trained neural networks without having to determine a priori their size and topology. The main drawback of the traditional NEAT algorithm is that, even though it can implement recurrent synaptic connections, which allow it to perform some time related processing tasks, its capabilities are rather limited, especially when dealing with precise time dependent phenomena. NEAT’s ability to capture the underlying dynamics that correspond to complex time series still has a lot of room for improvement. To address this issue, the paper describes a new implementation of the NEAT algorithm that is able to generate artificial neural networks (ANNs) with trainable time delayed synapses in addition to its previous capacities. We show that this approach, called \(\uptau \)-NEAT improves the behavior of the neural networks obtained when dealing with complex time related processes. Several examples are presented, both dealing with the generation of ANNs that are able to produce complex theoretical signals such as chaotic signals or real data series, as in the case of the monthly number of international airline passengers or monthly \(\hbox {CO}_{2}\) concentrations. In these examples, \(\uptau \)-NEAT clearly improves over the traditional NEAT algorithm in these tasks. A final example of the integration of this approach within a robot cognitive mechanism is also presented, showing the clear improvements it could provide in the modeling required for many cognitive processes.     

Evolutionary algorithm characterization in real parameter optimization problems

This paper deals with the constant problem of establishing a usable and reliable evolutionary algorithm (EA) characterization procedure so that final users like engineers, mathematicians or physicists can have more specific information to choose the most suitable EA for a given problem. The practical goal behind this work is to provide insights into relevant features of fitness landscapes and their relationship to the performance of different algorithms. This should help users to minimize the typical initial stage in which they apply a well-known EA, or a modified version of it, to the functions they want to optimize without really taking into account its suitability to the particular features of the problem. This trial and error procedure is usually due to a lack of objective and detailed characterizations of the algorithms in the literature in terms of the types of functions or landscape characteristics they are well suited to handle and, more importantly, the types for which they are not appropriate. Specifically, the influence of separability and modality of the fitness landscapes on the behaviour of EAs is analysed in depth to conclude that the typical binary classification of the target functions into separable/non-separable and unimodal/multimodal is too general, and characterizing the EAs’ response in these terms is misleading. Consequently, more detailed features of the fitness landscape in terms of separability and modality are proposed here and their relevance in the EAs’ behaviour is shown through experimentation using standardized benchmark functions that are described using those features. Three different EAs, the genetic algorithm, the Covariance Matrix Adaptation Evolution Strategy and Differential Evolution, are evaluated over these benchmarks and their behaviour is explained in terms of the proposed features.