Dora-Luz Flores

TA-Fuzzy Semantic Networks for Interaction Representation in Social Simulation

The need to model interactions between people of different cultures, religions and ethnic groups is evident. In Social Simulation, the combination of Artificial Intelligence andMulti-agent Systems has proven to be a good tool for modeling social groups, however much remains to achieve a model which represents a society with differences between individuals. Our proposal is to combine fuzzy logic, semantic networks and transactional analysis for representation of social interactions, taking into account the perception and a psychosocial profile of each individual. This model will facilitate the implementation of socially intelligent agents.

Ce,Tb-Doped Y2SiO5 Phosphor Luminescence Emissions Modeling and Simulation

White light-emitting diodes offer the possibility of efficient, safe, and reliable solid-state lighting, and thus have various applications in artificial lighting. Reported white light-emitting phosphors usually contain rare-earth metals and are generally prepared by high-energy processes. The purpose of this research is the modeling and simulation of the molar percentages of cerium and terbium required to be incorporated into a yttrium silicate (Y2SiO5:Ce,Tb) crystal structure for luminescent emission. The input data used for the modeling process comes from experiments with a set of molar concentrations of cerium and terbium ranging from 0.5 to 2%, and, from 1 to 6%, respectively; chromaticity coordinates are obtained from the photoluminescence spectra generated by the luminescent materials. With the information above, a simulation based on the proposed mathematical model was performed in order to obtain the approximation of the luminescent response of the molar concentrations whose experiments were not carried out in the laboratory. To demonstrate the results of the model and its simulation, a tool developed in the agent-based platform NetLogo is presented, which allows the user to observe the trend of the chromaticity coordinates for different molar percentages of cerium and cerium into yttrium silicate crystal structures.

Predicting the physiological response of Tivela stultorum hearts with digoxin from cardiac parameters using artificial neural networks

Multi-layer perceptron artificial neural networks (MLP-ANNs) were used to predict the concentration of digoxin needed to obtain a cardio-activity of specific biophysical parameters in Tivela stultorum hearts. The inputs of the neural networks were the minimum and maximum values of heart contraction force, the time of ventricular filling, the volume used for dilution, heart rate and weight, volume, length and width of the heart, while the output was the digoxin concentration in dilution necessary to obtain a desired physiological response. ANNs were trained, validated and tested with the dataset of the in vivo experiment results. To select the optimal network, predictions for all the dataset for each configuration of ANNs were made, a maximum 5% relative error for the digoxin concentration was set and the diagnostic accuracy of the predictions made was evaluated. The double-layer perceptron had a barely higher performance than the single-layer perceptron; therefore, both had a good predictive ability. The double-layer perceptron was able to obtain the most accurate predictions of digoxin concentration required in the hearts of T. stultorum using MLP-ANNs.

Towards A Multi-Dimensional Modelling Of Complex Social Systems Using Data Mining And Type-2 Neuro-Fuzzy System: Religious Affiliation Case Of Study.

The purpose of this paper is, to describe a work-in process for application of distributed agency methodology to multi-dimensional preference model into a complex social system. This paper shows a study case focused on a modeling system for decision-making on cognitive structure religious affiliation preference. A type-2 neurofuzzy approach is used to configure cognitive rules into an agent to build a multi-agent model for social simulation.

Agent-Based Model of the Budding Yeast Cell Cycle Regulatory Network

Kinetic parameters are a crucial aspect when studying reactions involving proteins. Unfortunately, these are often unknown or hard to measure in the laboratory. Therefore, modeling phenomena involving protein reactions without using kinetic parameters can be a significant advantage. In this work, an agent-based model of the budding yeast (Saccharomyces cerevisiae) cell cycle was created based on a cell cycle regulatory network, to obtain the correct sequence of states of the regulatory proteins. Comparing the results to a Boolean network model, having similar results, following the correct sequence of phases and reaching in 71% of the cases the biological G1 stationary state of the cell. Yeast cell cycle is highly conservated among other eukaryotes, meaning that is regulators works similar than the ones in humans; knowing that, yeast cells can be mutated to have behavior related to a specific tumor and then treated with different drugs to check which is better to kill that particular tumor. This model could be a starting point for being used in the development of cancer drugs adding cell cycle mutations that match a specific type of tumor cell cycle and an agent representing the medication or treatment.

Lean Manufacturing: A Strategy for Waste Reduction

In this chapter, we present relevant information about lean manufacturing (LM), its beginnings, evolution, as well as the main LM techniques and tools. The main purpose of this chapter is to know the current state of LM methodology and describe the leading tools that have contributed to successful LM projects worldwide. For this purpose, we conducted a literature review of 130 scientific papers, dividing LM tools, and technologies in two periods to track their evolution. Then, we conducted an analysis based on odd ratio and hypothesis testing for the proportions of frequency in the use of LM tools and technologies. This literature review aims at supporting practitioners in understanding the implications of lean manufacturing and knowing the main T&T that have contributed, during the last 12 years, to waste reduction and increased productivity in the industrial sector.

An agent-based model of the fission yeast cell cycle

The objective of this paper is to develop a computational model of the fission yeast (Schizosaccharomyces pombe) cell cycle using agent-based modeling (ABM), to study the sequence of states of the proteins and time of the cell cycle phases, under the action of proteins that regulate its cell cycle. The model relies only on the conceptual model of the yeast cell cycle regulatory network, where each protein has been represented as an agent with a property called activity that represents its biological function and a stochastic Brownian movement. The results indicate that the simulated phase time did have similar results in comparison with other models using mathematical approaches. Similarly, the correct sequence of states was achieved, and the model was run under different initial states to understand its emergent behaviors. The cell reached the G1 stationary state 94% of the times when running the model under biological initial conditions and 87% of the times when running the model through all the different combinations of initial states. Such results imply that the cell was capable to fix toward the biological expected phenomena. These results show that ABM is a suitable technique to study protein–protein interactions without using, often unavailable, kinetic parameters, or differential equations. This model sets as a base for further studies that involve the cell cycle of the fission yeast, with a special attention to studies and development of drug treatments for specific types of cancer.

An Agent-Based Model of an Oil Dispersant's Effect on a Marine Species

Computer simulations are used in situations where the object of study can not be investigated by traditional methods, or not to use more of the test organisms that have already been used. Simulations are useful to test hypotheses or as a support tool to observing other results. The agent based modeling is a powerful and flexible tool that supports simulation experiments in the laboratory, biologists are being used in conjunction with computer specialists as a valuable tool to investigate the properties of biological systems. In this paper, an agent-based model is presented, from a bio-marker enzyme experiment run at the laboratory with mussel Mytilus edulis, marine species of the coast of Baja California, Mexico, exposed to oil dispersant (Nokomis 3-F4) obtained in toxicity tests, also Net Logo simulation tool is used to show the impact over three different tissues (enzymatic activity) of the mussel produced by the oil dispersant.

Model of Interaction Among Embedded Fuzzy Agents in Ubiquitous Computing Environments

This research is motivated by the need to propose a model for studying the people interaction in educational spaces such as interactive museums, assisted by ubiquitous computing environments and aided of fuzzy logic. People are represented as mobile agent software, which has certain characteristics, limited knowledge and the necessity to interact with others to achieve individual or collective goals in a specific or dynamic context. Fuzzy agents have the capacity to deal with the uncertainty and imprecision presents by user’s interaction. We propose a model of interaction context-dependent among embedded fuzzy agents in ubiquitous computing environments that facilitate the interaction between people and the interactive museum, taking into account its ontology, autonomy, proactivity, mobility and social skills.

A complex social system simulation using type-2 fuzzy logic and multiagent system

The need of better representation of complex systems, such social systems, has made that the use of new simulation techniques are increasingly accepted, one of these accepted techniques are multi-agent systems. In addition to represent the uncertainty that is required by them, fuzzy logic and particularly type-2 fuzzy logic are being accepted. A system with three different types of agents is presented as case of study, each agent is assigned to a role with specific goals to be achieved in both ways individually and as teams, the success or failure is determined by group performance rather than individual achievement. It is also taken into account the environment or context as another type of agent. Fuzzy inference systems are defined for each of the agents to represent the concepts interpretation.