18773440
An Agent-based Simulation Study for Exploring Organizational Adaptation
Agile enterprises with different organizational structures manifest different organizational behaviors when responding to environmental changes. In this paper, we use a computational model to examine organizational adaptation on four dimensions: Agility, Robustness, Resilience, and Survivability. We analyze the dynamics of organizational adaptation by a simulation study on the interaction between tasks and organization in a sales enterprise. The `what if' analyses in different scenarios show that more flexible communication between employees and less hierarchy level with the suitable centralization can improve organizational adaptation. The developed simulation model supports the exploration of the parametric space that defines alternative organizing processes for the optimal strategy given the specified environmental dynamics.
1229519
A decision-theoretic approach for designing proactive communication in multi-agent teamwork
Techniques that support effective communication during teamwork processes are of particular importance. Psychological study shows that an effective team often can anticipate information exchange among the team and communicate relevant information proactively. Proactive communication is crucial for understanding and sharing common goals and for cooperative actions. Communication can be valuable if it assists agents with new and timely information; it also has cost because it consumes network resources such as bandwidth. To address these issues, we present a new model that uses information production and need to capture the complex multi-agent communication process and a dynamic decision-theoretic determination of communication strategies. We also introduce a generic utility function and an algorithm, DTPC (Decision-Theoretic Proactive Communication), that focuses on representing information production and need of team members and resolving decision interactions among them for making decisions.
35439746
Role-Based and Agent-Oriental Teamwork Modeling
5679790
Proactive Information Exchange During Team Cooperation
We are concerned, ultimately, with multi-agent teams involving both software and human agents in which the software agents assist in training the humans, e.g., as virtual team members. The software agents must be capable of utilizing the teamwork mechanisms used by humans. In this paper, we describe how to give agents the same implicit communication capabilities, i.e., observation, that humans use. We show how agents can use observations of the environment and teammates' actions to estimate other agents beliefs without bothering them with unnecessary messages; we also show how agents can anticipate information needs among the team and proactively exchange information, reducing the total communication volume. To achieve these goals, we add sensing capabilities to agents and present algorithms that infer teammates' mental state from the observation. Experimental data is presented that shows that the advantage of observation in proactive information exchange as well as enhanced team performance.
40423470
Proactive Communication in Observation-Based Agent Team Corporation
14748203
A history sensitive cascade model in diffusion networks
Diffusion is a process by which information, viruses, ideas and new behavior spread over social networks. The traditional independent cascade model gives activated nodes a one-time chance to activate each of its neighboring nodes with some probability. This paper extends the traditional cascade model to be history dependent. We propose a new model called the History Sensitive Cascade Model (HSCM) that allows activated nodes to receive more than a one-time chance to activate their neighbors. HSCM provides 1) a polynomial algorithm for calculating the probability of activity for any arbitrary node at any arbitrary time in tree structure graphs, and 2) a Markov model for calculating the probability in general graphs. Finally, we perform an empirical study on HSCM under different network settings. These simulations have showed its power to observe and explain the emergent phenomena in the macro level when changing parameters in the micro level.
40389264
Application of Efficient Numerical Methods in Solution of Ordinary Differential Equations for Modeling Electrical Activity in Cardiac Cells
There is a large number of ordinary differential equations (ODEs) characterize the electrical behavior generated by ionic movements in human myocardial cell. In this paper, several approaches were investigated in order to improve the efficiency of solving the ODE systems for ten Tusscher et al.’s ionic model of human ventricular tissue. By using non-standard finite difference (NSFD) scheme, the stiffness of the ODEs system will be successfully reduced, so a larger step-size can be used. A popular multi-step method called backward differentiation formulation (BDF) was also incorporated into the computational model for testing the largest possible time steps. The results show that NSFD can be as much as 10 times more efficient than standard forward Euler in single cell model simulation while maintaining an acceptable level of accuracy. The investigation of BDF method shows that a large step size is not recommended for the single cell simulation application. All solvers were coupled to the partial differential equations for the complete simulation of heart tissue, and such computation scheme may be a good calculation technique in heart modeling and simulation.
36049595
The Implementation of a Fully Integrated Scheme of self-Configuration and self-Organization (FISCO) on Imote2
Wireless Sensor networks are gaining a role of importance in the research community. In this paper, we choose Imote2 to establish our testbed according to the analysis and the comparison of the hardware capabilities and software characteristics of the current WSNs products. Moreover, we present our hardware, software platforms and "30 motes" testbed to validate and evaluate our Fully Integrated Scheme of self-Configuration and self-Organization (FISCO) while further propose the rudiment of our general routing and data dissemination testbed. In the test stage, experimental results show that each mote in the network obtains the corresponding role and takes correct actions to handle every receiving message.
10866526
Adaptive simulation of facial expressions
This paper presents a space-time adaptive refinement approach to simulate facial expressions on the deformable 3D face model at interactive rate. This new computational model benefits from an adaptive sampling of both space and time to minimize computational cost. Using this technique, we achieve a guaranteed frame rate of the dynamic facial expression simulation at low computational expense.
2554459
Refinement of optical map assemblies
MOTIVATION
Genomic mutations and variations provide insightful information about the functionality of sequence elements and their association with human diseases. Traditionally, variations are identified through analysis of short DNA sequences, usually shorter than 1000 bp per fragment. Optical maps provide both faster and more cost-efficient means for detecting such differences, because a single map can span over 1 million bp. Optical maps are assembled to cover the whole genome, and the accuracy of assembly is critical.


RESULTS
We present a computationally efficient model-based method for improving quality of such assemblies. Our method provides very high accuracy even with moderate coverage (<20 x). We utilize a hidden Markov model to represent the consensus map and use the expectation-Maximization algorithm to drive the refinement process. We also provide quality scores to assess the quality of the finished map.


AVAILABILITY
Code is available from www.cmb.usc.edu/people/valouev/
37364297
Design Of Fuzzy Neural Network Controller For Marine Steam Turbine System
Due to the complication of marine steam turbine system, and the nonlinearity, uncertainty and complexity of the steam turbine model, the conventional speed regulation system can no longer meet our requirement. Fuzzy neural network (FNN) is applied to the complex process modeling and logical inference. In this paper modular modeling methods is used to establish the model of steam turbine. So a new type speed regulation system based on neural network is presented by combining the fuzzy and neural network in this paper.