465 research outputs found
A simulation model for the procedure of psychiatric patients\u27 diversion at william r. sharpe, jr. hospital using flocking algorithm for input modeling
The high rate of civil commitment in West Virginia indicates that the bureau of mental health in the state has been inefficient and unproductive at facilitating the procedures of mental health system delivery, to the point that the two state hospitals of West Virginia are often at their full capacity and incapable of admitting any new cases. This inadequacy at managing psychiatric emergencies causes frequent diversion of civil committed patients from the state psychiatric hospitals to other community psychiatric units, and ultimately costs the state an un-budgeted {dollar}4 million annually.;The main objective of this research is to contribute to the improvement of the mental healthcare system in West Virginia for psychiatric patients, as well as employees and all the other involved parties which benefit. This is done by optimizing capacity-related decisions at William R. Sharpe, Jr. Hospital, one of the main assigned centers for psychiatric issues in the state.;In order to achieve this outcome, this work intends to first model the arrival process of different psychiatric patients to William R. Sharpe, Jr. Hospital based on data-driven simulation for complex multi-dimensional time series, by applying a flocking algorithm to the available dataset. Including the scheme of simulating patient arrivals, a simulation model is developed to model the patients\u27 arrivals, stay, and departures at the hospital. Sensitivity analysis has been performed to investigate the impacts of various variables such as the capacity of the hospital, the number of patient arrivals of a particular category, etc
A Toolbox to Analyze Emergence in Multiagent Simulations
The field of complexity science often employs multiagent simulations to investigate complex and emergent behavior. Authors in complexity science have suggested that the discussion of complex systems could benefit from a more systematic approach and a more compact mathematical way to describe the behavior of such systems in addition to the common observations and interpretations taking place today. Regarding quantitative measures to capture emergent phenomena, several approaches have been published, but have not yet been put to wide systematic use in the research community. One reason for this could be the manual effort required to investigate multiagent systems in a quantitatively accurate form. Toward this end, there has so far been a lack of appropriate and easy-to-use IT-based tools. To eliminate this deficiency, we present a software library, which enables researchers to integrate emergence measurements into experiments with multiagent modeling tools such as Repast and NetLogo. The major benefit for researchers is that this toolbox enables them to make comparable, quantitatively well-grounded statements about the emergent behavior of the model at hand. The toolbox therefore provides researchers with a standardized artifact that can be employed in a systematic methodological approach to the analysis of multiagent systems
Modeling flocks with perceptual agents from a dynamicist perspective
Computational simulations of flocks and crowds have typically been processed by a set of logic or syntactic rules. In recent decades, a new generation of systems has emerged from dynamicist approaches in which the agents and the environment are treated as a pair of dynamical systems coupled informationally and mechanically. Their spontaneous interactions allow them to achieve the desired behavior. The main proposition assumes that the agent does not need a full model or to make inferences before taking actions; rather, the information necessary for any action can be derived from the environment with simple computations and very little internal state. In this paper, we present a simulation framework in which the agents are endowed with a sensing device, an oscillator network as controller and actuators to interact with the environment. The perception device is designed as an optic array emulating the principles of the animal retina, which assimilates stimuli resembling optic flow to be captured from the environment. The controller modulates informational variables to action variables in a sensory-motor flow. Our approach is based on the Kuramoto model that describes mathematically a network of coupled phase oscillators and the use of evolutionary algorithms, which is proved to be capable of synthesizing minimal synchronization strategies based on the dynamical coupling between agents and environment. We carry out a comparative analysis with classical implementations taking into account several criteria. It is concluded that we should consider replacing the metaphor of symbolic information processing by that of sensory-motor coordination in problems of multi-agent organizations
A survey of modern exogenous fault detection and diagnosis methods for swarm robotics
Swarm robotic systems are heavily inspired by observations of social insects. This often leads to robust-ness being viewed as an inherent property of them. However, this has been shown to not always be thecase. Because of this, fault detection and diagnosis in swarm robotic systems is of the utmost importancefor ensuring the continued operation and success of the swarm. This paper provides an overview of recentwork in the field of exogenous fault detection and diagnosis in swarm robotics, focusing on the four areaswhere research is concentrated: immune system, data modelling, and blockchain-based fault detectionmethods and local-sensing based fault diagnosis methods. Each of these areas have significant advan-tages and disadvantages which are explored in detail. Though the work presented here represents a sig-nificant advancement in the field, there are still large areas that require further research. Specifically,further research is required in testing these methods on real robotic swarms, fault diagnosis methods,and integrating fault detection, diagnosis and recovery methods in order to create robust swarms thatcan be used for non-trivial tasks
Deriving mesoscopic models of collective behaviour for finite populations
Animal groups exhibit emergent properties that are a consequence of local
interactions. Linking individual-level behaviour to coarse-grained descriptions
of animal groups has been a question of fundamental interest. Here, we present
two complementary approaches to deriving coarse-grained descriptions of
collective behaviour at so-called mesoscopic scales, which account for the
stochasticity arising from the finite sizes of animal groups. We construct
stochastic differential equations (SDEs) for a coarse-grained variable that
describes the order/consensus within a group. The first method of construction
is based on van Kampen's system-size expansion of transition rates. The second
method employs Gillespie's chemical Langevin equations. We apply these two
methods to two microscopic models from the literature, in which organisms
stochastically interact and choose between two directions/choices of foraging.
These `binary-choice' models differ only in the types of interactions between
individuals, with one assuming simple pair-wise interactions, and the other
incorporating higher-order effects. In both cases, the derived mesoscopic SDEs
have multiplicative, or state-dependent, noise. However, the different models
demonstrate the contrasting effects of noise: increasing order in the pair-wise
interaction model, whilst reducing order in the higher-order interaction model.
Although both methods yield identical SDEs for such binary-choice, or
one-dimensional, systems, the relative tractability of the chemical Langevin
approach is beneficial in generalizations to higher-dimensions. In summary,
this book chapter provides a pedagogical review of two complementary methods to
construct mesoscopic descriptions from microscopic rules and demonstrates how
resultant multiplicative noise can have counter-intuitive effects on shaping
collective behaviour.Comment: Second version, 4 figures, 2 appendice
Tracking collective cell motion by topological data analysis
By modifying and calibrating an active vertex model to experiments, we have
simulated numerically a confluent cellular monolayer spreading on an empty
space and the collision of two monolayers of different cells in an antagonistic
migration assay. Cells are subject to inertial forces and to active forces that
try to align their velocities with those of neighboring ones. In agreement with
experiments, spreading tests exhibit finger formation in the moving interfaces,
swirls in the velocity field, and the polar order parameter and correlation and
swirl lengths increase with time. Cells inside the tissue have smaller area
than those at the interface, as observed in recent experiments. In antagonistic
migration assays, a population of fluidlike Ras cells invades a population of
wild type solidlike cells having shape parameters above and below the geometric
critical value, respectively. Cell mixing or segregation depends on the
junction tensions between different cells. We reproduce experimentally observed
antagonistic migration assays by assuming that a fraction of cells favor
mixing, the others segregation, and that these cells are randomly distributed
in space. To characterize and compare the structure of interfaces between cell
types or of interfaces of spreading cellular monolayers in an automatic manner,
we apply topological data analysis to experimental data and to numerical
simulations. We use time series of numerical simulation data to automatically
group, track and classify advancing interfaces of cellular aggregates by means
of bottleneck or Wasserstein distances of persistent homologies. These
topological data analysis techniques are scalable and could be used in studies
involving large amounts of data. Besides applications to wound healing and
metastatic cancer, these studies are relevant for tissue engineering,
biological effects of materials, tissue and organ regeneration.Comment: 34 pages, 25 figures, the final version will appear in PLoS
Computational Biolog
Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting
Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of energy usage forecasting by optimizing the alpha value in exponential smoothing, thereby improving forecasting accuracy. The results showed that all DL methods experienced improved accuracy when using optimum alpha. LSTM has the most optimal MAPE, RMSE, and R2 values compared to other methods. This research promotes energy management, decision-making, and efficiency by providing an innovative framework for accurate forecasting of energy use, thus contributing to a sustainable and efficient energy system
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