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• Cellular Automata (CA) modeling of crowd evacuation behavior with spatial game. • Novel long memory CA based approach models in a fully dynamic way crowd behavior. • Realistic crowd modeling by incorporating more than one velocity for moving agents. • Multi-parameter modeling including level of emergency, and previous games outcome. • Study of people behavior and choice of possible strategy in subsequent conflicts. When it comes to emergency evacuations of a big number of individuals from a certain room or a building, it is crucial that they should be able to exit the under study place in a quick and safe manner. Nevertheless, crowd behavior can be affected from various factors, among which are the distance from the exit, the visibility of the exit, the density of the crowd and how uncomfortable the conditions are inside the room for the crowd. In this study, we propose an evacuation model, in order to achieve a computationally enhanced simulation, based on Cellular Automata modeling, coupled with a Spatial Game to solve conflict situations among the anxious crowd. Additionally, aiming to a more realistic model, we added not only topological, but also behavioral characteristics coupled with the incorporation of different moving velocities and memory features to the evacuees. [ABSTRACT FROM AUTHOR]

• Graphics Processing Unit Acceleration of Fuzzy Cellular Automata for crowd evacuation. • Significant speed up of Fuzzy Cellular Automata execution of two orders of magnitude. • Fuzzy logic velocity adjustments and selection of direction experimentally proven efficient. • Incorporation of psychological-impact feature in Fuzzy Cellular Automata model. • Speed and flow vs. time diagrams for validation of model's successful response. Pedestrian and crowd dynamics are physical phenomena that are fundamentally characterized by non-linear complexity. In the same time, the need of modern way of living seeks for such dynamics real time modeling also enabling computationally efficient and affordable solutions for sake of safety and easiness of people located in gathering places all over the world. Towards this direction, Cellular Automata (CAs), a parallel computational model combining macro- and microscopic inherent attributes that could severely help with adequate modeling of the aforementioned dynamics, are one of the best compromises among different competing computational techniques. In order to overcome CAs deterministic nature, in this paper the incorporation of fuzzy logic principles in a CA model that simulates crowd dynamics and crowd evacuation processes, with the usage of a Mamdani type fuzzy inference system, is proposed. More specifically, basic concepts of fuzzy logic such as linguistic variables and if-then rules are attributed to the proposed CA model to preserve fuzzy consequents and fuzzy antecedents thus resulting in a realistic and rather efficient modeling approach. Furthermore, in the paper the implementation of fuzziness in CA dynamics is tackled with the acceleration of the proposed model through fully parallel execution on Graphics Processing Units (GPU). The GPU implementation of the fuzzy CA model is analyzed in full detail and stressed against CPU corresponding implementation resulting to an important speed up of fuzzy CA execution. This is further explored through the GPU applications of the fuzzy CA model in a real building, namely the museum 'CONSTANTIN XENAKIS', in Serres, Greece. [ABSTRACT FROM AUTHOR]

Cellular Automata (CA) have been introduced many decades ago as one of the most efficient parallel computational models able to simulate various physical processes and systems where the interactions are local. In this paper, we are trying to advance the application of CA in modeling wildfires by accounting for the fuzziness intrinsic to the numerous environmental variables and mechanisms engaged with the emergence of the phenomenon itself. The proposed Fuzzy CA (FCA) model adopts a data-driven approach, based on evolutionary optimization, which allows incorporating knowledge from real wildfires in order to enhance its accuracy. The main difficulty for doing so arrives from the computational complexity of the proposed framework and the burden of computational resources needed for its application, which would prevent the real-time prediction of fire spread scenarios. In order to tackle the aforementioned difficulties, we propose model's fully parallel implementations in Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs) hardware. In the article, we first investigate the speedup achieved by the developed parallel implementations. Then, we present and discuss two applications to heterogeneous landscapes through comparisons with observed wildfires. Moreover, we compare the proposed framework with two different modelling approaches and results found are really promising. [ABSTRACT FROM AUTHOR]

During the past years, Cellular Automata (CAs) have been extensively used for modeling of many complex systems and processes with great success. In this paper, we study a Cellular Automaton (CA) model for the influence of employees' behavior in a parameterized workplace environment taking into account different behavioral characteristics. In specific, we model employees' interactions based on their influence radius, the degree of their willingness on adaption of organizational norms and the employee's attitude in general in the under study workplace. The proposed CA model is taking into account employee loyalty, a combined statistic of the employee behavior and her/his insistence and company policies applied to the employees so as to restrain unwanted or impose desirable behavioral patterns in correspondence to the organization norms. Conclusively, the CA model facilitates the presentation and simulation of a workplace with a variety of employee behavioral characteristics and under adaptable company policies. Different workplaces were used to illustrate the simulation of employee behavior with CA model. As a result, the proposed model was practically used on two levels, firstly to estimate the workplace robustness and secondly to illustrate workspace dynamics. Finally, the CA model has been utilized to simulate behavioral patterns at a small enterprise in Greece. In specific, based on the employees answers to detailed surveys the CA model was initialized and then applied to describe the behavioral traits of the under study company employees. Finally, the proposed model, in all the examined cases can be utilized in conjunction with applied employee management techniques to facilitate managerial decisions and forecast the impact of employee behavioral changes and company decisions. [ABSTRACT FROM AUTHOR]

For several decades the Cellular Automata (CA) modeling approach has been proven to be self-sufficient to inspire modelers and practitioners on how to apply successfully their ideas on modern computational platforms by using their inherent parallelism enabling high performance computing and modeling. One can easily describe CA as a mature parallel computational system robust enough to evolve or better say to adapt itself to the new computing paradigms of multiprocessors and graphical processing units with noteworthy efficacy and admirable modeling results. This Special Section is exactly dedicated to the application of CA paradigm to research and industry containing extended versions of papers originally published in proceedings of 11th International Conference on Cellular Automata for Research and Industry (ACRI 2014), a biannual conference dedicated to CA theory and applications, including also papers submitted in reference to the corresponding open Call for Papers in the context of Journal of Computational Science . [ABSTRACT FROM AUTHOR]

Resistance switching devices are of special importance because of their application in resistive memories (RRAM) which are promising candidates for replacing current nonvolatile memories and realize storage class memories. These devices exhibit usually memristive properties with many discrete resistance levels and implement artificial synapses. The last years, researchers have demonstrated memristive chips as accelerators in computing, following new in-memory and neuromorphic computational approaches. Many different metal oxides have been used as resistance switching materials in MIM or MIS structures. Understanding of the mechanism and the dynamics of resistance switching is very critical for the modeling and use of memristors in different applications. Here, we demonstrate the bipolar resistance switching of silicon nitride thin films using heavily doped Si and Cu as bottom and top-electrodes, respectively. Analysis of the current-voltage characteristics reveal that under space-charge limited conditions and appropriate current compliance setting, multi-level resistance operation can be achieved. Furthermore, a flexible tuning protocol for multi-level resistance switching was developed applying appropriate SET/RESET pulse sequences. Retention and random telegraph noise measurements performed at different resistance levels. The present results reveal the attractive properties of the examined devices. [ABSTRACT FROM AUTHOR]

Memristors have been utilized as an unconventional computational substrate and gained interest as a medium to implement neuromorphic computations. A mathematical model that also proved its potential is Learning Cellular Automata, that is an amalgam of Cellular Automata and Learning Automata. The realization of the common characteristics of memristive circuits and Learning Cellular Automata can only lead to their combination. Namely, both manage to blend storage and processing capabilities in their basic entity. This study involves the definition of memristive circuits that realize the computing behavior of Learning Cellular Automata. An example of this methodology is provided with the description of the implementation of edge detection for image processing. [ABSTRACT FROM AUTHOR]