Pore-scale modeling of competition and cooperation of multispecies biofilms for nutrients in changing environments

In this article, we developed a pore-scale model of integrated lattice Boltzmann method and cellular automata to investigate competitive growth of aerobic nitrite and ammonium oxidizers in a bioreactor. The results showed that inlet nutrient concentrations have significant effects on maximum biofilm concentration, ratio of microorganisms' concentrations, growth pattern, and time. The local availability of oxygen could control the competition, resulting in different growth patterns. The coexistence of ammonium and nitrite in same inlet zone increased not only the biofilm concentration (7%) but also the ratio of microorganisms' concentrations (36%). Although this coexistence decreased the total biofilm concentration in some cases, it increased the growth rate about 25%. Changes of the maximum biomass concentration could change biofilm concentration of about 40% and microorganisms' concentrations ratio of about 30%. This framework provides a powerful tool to improve our understanding of dynamic interdependency of many complex microbial consortia systems with environments.     

Grain size modeling of a Ni-base superalloy using cellular automata algorithm

Nickel-base superalloys are used in highly demanding applications such as energy and aerospace industries. These alloys present good corrosion resistance, weldability and mechanical stability at high temperatures. Numerical methods are commonly used to predict the mechanical and microstructural behavior of heat resistant alloys. The aim of the present work was to model recrystallized grain size evolution under isothermal conditions using the cellular automata (CA) technique. The CA model was applied to simulate hot compression of Inconel 718 nickel-base alloy at 980 °C and 1020 °C. A finite element analysis was conducted to acquire input parameters to the model such as strain and strain rate. Hardening and recovery coefficients were calculated in order to represent the competitive effects during deformation. The influence of local changes of initial grain with fully and partial recrystallized microstructures were simulated by CA and compared with isothermal hot compression results. The model was able to comprehensively predict necklace type microstructures. The average grain size was generally in good agreement with the experimental data.     

A synchronous cellular automaton model of mass transport in porous media

In this work we present a fully synchronous coarse grained cellular automaton model for large-scale simulations at molecular level. The model is based on Margolus partitioning scheme, which was generalized as to describe quantitatively diffusion, adsorption and directed flow in porous media. Our aim is to create conceptually simple and computationally efficient framework to model the mass transport in porous materials with large representative volume. This work focuses on the fundamental aspects of the generalized Margolus cellular automaton. We exemplify the model by solving several diffusion problems, studying the monolayer adsorption, chromatography on disordered porous structures and chemical transformation in a system with phase separation. The results indicate that the model reflects the essential features of these phenomena. Absence of round-off errors, fully synchronous way of implementation, autonomous physically meaningful time scale and ease-to-handle boundary conditions make this model a promising framework for study various transport phenomena in porous structures.     

New MPPT algorithm for PV applications based on hybrid dynamical approach

This paper proposes a new Maximum Power Point Tracking (MPPT) algorithm for photovoltaic applications using the multicellular converter as a stage of power adaptation. The proposed MPPT technique has been designed using a hybrid dynamical approach to model the photovoltaic generator. The hybrid dynamical theory has been applied taking advantage of the particular topology of the multicellular converter. Then, a hybrid automata has been established to optimize the power production. The maximization of the produced solar energy is achieved by switching between the different operative modes of the hybrid automata, which is conditioned by some invariance and transition conditions. These conditions have been validated by simulation tests under different conditions of temperature and irradiance. Moreover, the performance of the proposed algorithm has been then evaluated by comparison with standard MPPT techniques numerically and by experimental tests under varying external working conditions. The results have shown the interesting features that the hybrid MPPT technique presents in terms of performance and simplicity for real time implementation.     

Syntactic compression codes at the zero entropy point

Syntactic compression codes compress the tree, which is the syntax of a binary source message. The ones considered here originate from image processing. The syntactic trees usedhave a constant valency and their binary labels distinguish whether the source substring derived from a node is completely zero or not. We compress them simply by deleting some redundant subtrees.

These codes fall into a theoretically new class of codes which is wider than the classical ones. They are here studied in the neighborhood of a zero of the binary entropy function. There, their behavior is close to that of an infinite run length encoding and the optimum valency is three. Finally, we open a problem, related with automata theory, which perhaps could provide a further link between Information Theory and Algorithmic Information Theory.     

Systematic generation of cyclic operating procedures based on timed automata

Manual synthesis of cyclic operating procedure in a realistic system is widely regarded as a difficult task since it is both time-consuming and error-prone. It is thus desirable to develop a systematic approach to automatically generate the optimal schedule of operation steps so as to achieve one or more specific production goal. The timed automata are utilized in the present work for such a purpose. In particular, all components in a given system and the corresponding control specifications are characterized with automata according to the proposed modeling rules. By using parallel composition, a system automaton can be produced with these models and the most appropriate operation path can then be identified accordingly. For any practical application, a sequential function chart and the corresponding Gantt chart can also be easily extracted from this path. Three examples are presented in this paper to demonstrate the feasibility of the proposed approach.     

Measuring the neighbourhood effect to calibrate land use models

Many spatially explicit land use models include the neighbourhood effect as a driver of land use changes. The neighbourhood effect includes the inertia of land uses over time, the conversion from one land use to another, and the attraction or repulsion of surrounding land uses. The neighbourhood effect is expressed in the neighbourhood rules, but calibration of the neighbourhood rules is not straightforward. This paper aims to characterise the neighbourhood effect of observed land use changes and use this information to improve the calibration of land use models. We measured the over- and underrepresentation of land uses in the neighbourhood of observed land use changes using a modified version of the enrichment factor. Enrichment factors of observed land use changes in Germany between 1990 and 2000 indicate that the neighbourhood effect exists. This suggests that it is appropriate to use neighbourhood rules to simulate urban land use changes. Observed enrichment factors were used to calibrate a land use model for Germany from 1990 to 2000 and the obtained neighbourhood rules were validated independently from 2000 to 2006. The results show that both the allocation accuracy and the pattern accuracy of the land use model improved for the calibration period, as well as for the independent validation period. This indicates that enrichment factors can be used to improve the calibration of the neighbourhood rules in land use models.     

Hidden potentialities

The search for “complexity signatures” in natural laws is a main concern for researchers working in many different fields, going from physics to biology. Very simple laws are able to produce unforeseen behaviors, and from their “simplicity” sometimes it is not possible to predict anything about the potential complexities they are able to produce when applied to random initial conditions. Here elementary cellular automata (ECA) are used to illustrate this idea. In fact, using a recently developed approach to establish a correspondence between ECA rules and logical functions that constitute their structure (logical spectra) we analyzed ECA groups represented by automata 120 and 164. The automaton governed by rule 120 generates complex patterns under certain random initial conditions, whereas the one corresponding to rule 164 is known as a simple law when it is evaluated at random initial conditions. However, slight changes in the initial conditions of both ECA produce dramatic “simplicity–complexity transitions”, as it is shown here. These examples show that complexity, even in very simple formal systems, results from a subtle interplay between the structure of the laws and the initial conditions. Moreover, they draw attention to the importance of investigating if analogous comportments hold in large-scale natural phenomena as the yet enigmatic genotype–phenotype mapping.     

A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers

This paper presents a robust, efficient and parameter-setting-free evolutionary approach for the optimal design of compact heat exchangers. A learning automata based particle swarm optimization (LAPSO) is developed for optimization task. Seven design parameters, including discreet and continuous ones, are considered as optimization variables. To make the constraint handling straightforward, a self-adaptive penalty function method is employed. The efficiency and the accuracy of the proposed method are demonstrated through two illustrative examples that include three objectives, namely minimum total annual cost, minimum weight and minimum number of entropy generation units. Numerical results indicate that the presented approach generates the optimum configuration with higher accuracy and a higher success rate when compared with genetic algorithms (GAs) and particle swarm optimization (PSO).     

Bars on blocks: A cellular automata model of crime and liquor licensed establishment density

Criminologists have extensively researched the problems generated by licensed establishments. Violent offending and disorderly behavior resulting from pubs, taverns, dance clubs and bars are of particular interest to this field of study. The relative density of these liquor establishments has been found to be associated with the level of violence and disorder in surrounding areas. A complex systems approach can be used to further understand the dynamic interplay between licensed establishments, violent offending and disorder, and urban planning decisions. The model presented here utilizes cellular automata as the mathematical framework to view the varying impact of liquor licensing density on crime. This study uses a sample of liquor establishments and crime data from the City of Vancouver in British Columbia. The cellular automata model incorporates transition rules which govern the change of city blocks from low-risk blocks to high-risk blocks. The results represented by a 50 × 50 cellular grid show that high-risk blocks multiply when liquor licenses are grouped. Two scenarios are presented to contrast the impact of grouping high-risk blocks which contain more liquor establishments and dispersing such blocks. A third scenario demonstrates how increasing the positive influence in a grouped scenario stops high-risk blocks from taking over the entire grid. Future iterations of this model will incorporate census data, public transportation data, land use data and entertainment districts from other cities to further analyze the effect of licensed establishments on the distribution of crime.