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Discusses MCell (version 4.2), a popular cellular automata explorer for Windows™. Gives a brief overview of the program, demonstrates its capabilities and shows possible applications, presents the library of collected rules and patterns, and explains how user‐defined extensions can be programmed.

The purpose of this paper is to investigate two-dimensional outer totalistic cellular automata (2D-OTCA) rules other than the Game of Life rule for image scrambling. This paper presents a digital image scrambling (DIS) technique based on 2D-OTCA for improving the scrambling degree. The comparison of scrambling performance and computational effort of proposed technique with existing CA-based image scrambling techniques is also presented.

In this paper, a DIS technique based on 2D-OTCA with von Neumann neighborhood (N_vN) is proposed. Effect of three important cellular automata (CA) parameters on gray difference degree (GDD) is analyzed: first the OTCA rules, afterwards two different boundary conditions and finally the number of CA generations (k) are tested. The authors selected a random sample of gray-scale images from the Berkeley Segmentation Data set and Benchmark, BSDS300 (www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/) for the experiments. Initially, the CA is setup with a random initial configuration and the GDD is computed by testing all OTCA rules, one by one, for CA generations ranging from 1 to 10. A subset of these tested rules produces high GDD values and shows positive correlation with the k values. Subsequently, this sample of rules is used with different boundary conditions and applied to the sample image data set to analyze the effect of these boundary conditions on GDD. Finally, in order to compare the scrambling performance of the proposed technique with the existing CA-based image scrambling techniques, the authors use same initial CA configuration, number of CA generations, k=10, periodic boundary conditions and the same test images.

The experimental results are evaluated and analyzed using GDD parameter and then compared with existing techniques. The technique results in better GDD values with 2D-OTCA rule 171 when compared with existing techniques. The CPU running time of the proposed algorithm is also considerably small as compared to existing techniques.

In this paper, the authors focused on using von Neumann neighborhood (N_vN) to evolve the CA for image scrambling. The use of N_vN reduced the computational effort on one hand, and reduced the CA rule space to 1,024 as compared to about 2.62 lakh rule space available with Moore neighborhood (N_M) on the other. The results of this paper are based on original analysis of the proposed work.

This paper aims to present a framework to address the impact of people’s behaviour in the dissemination of information through mobile social networks.

This approach follows the epidemical compartmental models and uses a grid to model the nodes’ (people) behaviour in the dissemination process. The nodes’ status is determined by binary rules that update and define the flow of information between neighbour nodes. An improved stacked-layer grid model is used to implement modulations in the application of the rules and neighbourhoods to model the impact of people’s attitude, which may improve or jeopardize the efficiency of the process.

This proposal shows how grid architecture is a valuable tool to model different causes of malfunction of data dissemination. Combining different grids with different neighbourhoods and different local rules provides a wide range of possibilities to depict the impact of human awareness and decision on the dissemination of data.

This works develops a new approach for the analysis of dissemination of information which add new features to traditional methods for modelling local interactions and describing the dynamics of the communication patterns in the population.

The purpose of this paper is to model group advertising decisions, which are the collective decisions of every single advertiser within the set of advertisers who are competing in the same auction or vertical industry, and examine resulting market outcomes, via a proposed simulation framework named Experimental Platform for Search Engine Advertising (EXP-SEA) supporting experimental studies of collective behaviors in the context of search engine advertising.

The authors implement the EXP-SEA to validate the proposed simulation framework, also conduct three experimental studies on the aggregate impact of electronic word-of-mouth (eWOM), the competition level and strategic bidding behaviors. EXP-SEA supports heterogeneous participants, various auction mechanisms and also ranking and pricing algorithms.

Findings from the three experiments show that both the market profit and advertising indexes such as number of impressions and number of clicks are larger when the eWOM effect is present, meaning social media certainly has some effect on search engine advertising outcomes, the competition level has a monotonic increasing effect on the market performance, thus search engines have an incentive to encourage both the eWOM among search users and competition among advertisers, and given the market-level effect of the percentage of advertisers employing a dynamic greedy bidding strategy, there is a cut-off point for strategic bidding behaviors.

This is one of the first research works to explore collective group decisions and resulting phenomena in the complex context of search engine advertising via developing and validating a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.

Mainstream sociology tends to consider mental processes and their underlying structures, including the perception of society, mainly to be a result of socialization, which is generally conceptualized in terms of the more or less intentional, interpersonal transmission of cultural elements. In contrast and rightly so, marxist theory has always insisted on praxis as an essential feature of consciousness formation. The concept of alienation, when it is not entirely subjectivized (as it is in the Seeman tradition), is usually derived directly from basic structural conditions of capitalism, especially from the coerced division of labor (Wallimann 1981). Conceived to be a constant of the entire system, it is of little use to explain within‐system variations of current images of society and of one's place within it.

The purpose of this paper is to present a discrete compartmental susceptible-asymptomatic-infected-dead (SAID) model to address the expansion of plant pests. The authors examined the case of Xylella fastidiosa in almond trees in the province of Alicante (Spain) to define the best eradication/contention protocol depending on the environmental parameters such as climatic factors, distance between trees, isolation of the plots, etc.

This approach considers the expansion of the disease among the almond trees orchards by means of a grid model. The cells of the grid represent a tree (or even a group of trees) that can be susceptible (healthy), asymptomatic (infected by the bacterium but without symptoms), infected or dead. When time passes, the status of the cells is determined by binary rules that update following both a neighborhood and a delay pattern. The model assumes that the environmental parameters have a crucial impact on the expansion of the disease, so a grid is assigned to each parameter to model the single effect caused by this parameter. The expansion is then the weighted sum of all the grids.

This proposal shows how the grid architecture, along with an update rule and a neighborhood pattern, is a valuable tool to model the pest expansion. This model has already been analyzed in previous works and has been compared with the corresponding continuous models solved by ordinary differential equations, coming to find the homologous parameters between both approaches. Thus, it has been possible to prove that the combination neighborhood-update rule is responsible for the rate of expansion and recovering/death of the illness. The delays (between susceptible and asymptomatic, asymptomatic and infected, infected and recovered/dead) may have a crucial impact on both the peak of infected and the recovery/death rate. This theoretical model has been successfully tested in the case of the dissemination of information through mobile social networks and is also currently under study in the case of expansion of COVID-19.

This work develops a new approach for the analysis of expansion of plant pests. This approach provides both behavioral variability at the cell level (by its capability to modify the neighborhood and/or the update rule and/or the delays) and modularity (by easy scaling the number of grids). This provides a wide range of possibilities to deal with realistic scenarios.

The conceptualisation of technology adoption has largely been based on the Bass or some Bass-derived model – notably, the logistic model. Logistic-type models offer limited insights regarding the adoption process dynamics or the utility value of their results. The purpose of this paper is to outline an alternative technology adoption framework based on complex adaptive networks.

An agent-based methodological approach is proposed. In it the actors, factors, goals, and adaptive learning influences driving solar energy technology adoption (SETA) process are first substantiated by empirical evidence gathered using field questionnaires and then incorporated in the simulation of a dynamic complex adaptive network of SETA. The complex adaptive network model is based on simple heuristic rules applied using a modified preferential attachment scheme within a NetLogo simulation environment.

The interim results suggest an emergent network where prominent hub “driver” agents underlining the robustness of the model are statistically discernible.

The research is limited to solar photovoltaic and solar water heating technology adoption in Botswana households; however, its results are far-reaching.

These results can be related to sustainable energy policy design. There, targeted incentive mechanisms can be formulated against the backdrop of the identified environmental factors and actors; the aim being to accelerate and cascade SETA.

The results could also be cascaded to other sectors and other non-solar technologies, thus providing a general alternative framework for enabling the widespread adoption of technologies.

This research therefore represents a novel way of utilizing the new science of networks to accelerate SETA.

The aim of this paper is to highlight the structure implemented in biological systems. Our modelling world view is based on automata and a hierarchical structure of biological system, because a feedback loop (the basic structure of any biological system) is a hierarchical structure, the processors of which are automata. Our modelling of a body metabolism (without energy production systems from lipids and proteins), is divided into two parts: Basic structure of a feedback loop and the structure of control of loops.

Develops an original 12‐step management of technology protocol and applies it to 51 applications which range from Du Pont’s failure in Nylon to the Single Online Trade Exchange for Auto Parts procurement by GM, Ford, Daimler‐Chrysler and Renault‐Nissan. Provides many case studies with regards to the adoption of technology and describes seven chief technology officer characteristics. Discusses common errors when companies invest in technology and considers the probabilities of success. Provides 175 questions and answers to reinforce the concepts introduced. States that this substantial journal is aimed primarily at the present and potential chief technology officer to assist their survival and success in national and international markets.