Intuitionistic Fuzzy 2-Metric Space and Some Topological Properties

The notion of intuitionistic fuzzy metric space was introduced by Park (2004) and the concept of intuitionistic fuzzy normed space by Saadati and Park (2006). Recently Mursaleen and Lohani introduced the concept of intuitionistic fuzzy 2-metric space (2009) and intuitionistic fuzzy 2-norm space. This paper studies precompactness and metrizability in this new setup of intuitionistic fuzzy 2-metric space.

An Autonomous Multi-Agent Simulation Model for Acute Inflammatory Response

This research proposes an agent-based simulation model combined with the strength of systemic dynamic mathematical model, providing a new modeling and simulation approach of the pathogenesis of AIR. AIR is the initial stage of a typical sepsis episode, often leading to severe sepsis or septic shocks. The process of AIR has been in the focal point affecting more than 750,000 patients annually in the United State alone. Based on the agent-based model presented herein, clinicians can predict the sepsis pathogenesis for patients using the prognostic indicators from the simulation results, planning the proper therapeutic interventions accordingly. Impressively, the modeling approach presented creates a friendly user-interface allowing physicians to visualize and capture the potential AIR progression patterns. Based on the computational studies, the simulated behavior of the agent–based model conforms to the mechanisms described by the system dynamics mathematical models established in previous research.

Mitigation Strategies for Foot and Mouth Disease

Prediction of epidemics such as Foot and Mouth Disease (FMD) is a global necessity in addressing economic, political and ethical issues faced by the affected countries. In the absence of precise and accurate spatial information regarding disease dynamics, learning- based predictive models can be used to mimic latent spatial parameters so as to predict the spread of epidemics in time. This paper analyzes temporal predictions from four such learning-based models, namely: neural network, autoregressive, Bayesian network, and Monte-Carlo simulation models. The prediction qualities of these models have been validated using FMD incidence reports in Turkey. Additionally, the authors perform simulations of mitigation strategies based on the predictive models to curb the impact of the epidemic. This paper also analyzes the cost-effectiveness of these mitigation strategies to conclude that vaccinations and movement ban strategies are more cost-effective than premise culls before the onset of an epidemic outbreak; however, in the event of existing epidemic outbreaks, premise culling is more effective at controlling FMD.

Generating Fully Bounded Chaotic Attractors

Generating chaotic attractors from nonlinear dynamical systems is quite important because of their applicability in sciences and engineering. This paper considers a class of 2-D mappings displaying fully bounded chaotic attractors for all bifurcation parameters. It describes in detail the dynamical behavior of this map, along with some other dynamical phenomena. Also presented are some phase portraits and some dynamical properties of the given simple family of 2-D discrete mappings.

Resource Distribution Strategies for Mitigation of Cross-Regional Influenza Pandemics

As recently acknowledged by the Institute of Medicine, the existing pandemic mitigation models lack dynamic decision support capabilities. This paper develops a simulation optimization model for generating dynamic resource distribution strategies over a network of regions exposed to a pandemic. While the underlying simulation mimics the disease and population dynamics of the affected regions, the optimization model generates progressive allocations of mitigation resources, including vaccines, antivirals, healthcare capacities, and social distancing enforcement measures. The model strives to minimize the impact of ongoing outbreaks and the expected impact of the potential outbreaks, considering measures of morbidity, mortality, and social distancing, translated into the cost of lost productivity and medical expenses. The model was implemented on a simulated outbreak involving four million inhabitants. The strategy was compared to pro-rata and myopic strategies. The model is intended to assist public health policy makers in developing effective distribution policies during influenza pandemics.

Mathematical Model to Assess the Relative Effectiveness of Rift Valley Fever Countermeasures

Mathematical modeling of infectious diseases is increasingly used to explicate the mechanics of disease propagation, impact of controls, and sensitivity of countermeasures. The authors demonstrate use of a Rift Valley Fever (RVF) model to study efficacy of countermeasures to disease transmission parameters. RVF is a viral infectious disease that propagates through infected mosquitoes and primarily affects animals but also humans. Vaccines exist to protect against the disease but there is lack of data comparing efficacy of vaccination with alternative countermeasures such as managing mosquito population or destroying infected livestock. This paper presents a compartmentalized multispecies deterministic ordinary differential equation model of RVF propagation among livestock through infected Aedes and Culex mosquitoes and exercises the model to study the efficacy of vector adulticide, vector larvicide, livestock vaccination, and livestock culling on livestock population. Results suggest that livestock vaccination and culling offer the greatest benefit in terms of reducing livestock morbidity and mortality.

What Does Artificial Life Tell Us About Death?

This paper discusses how concepts developed within artificial life (ALife) can help demystify the notion of death. This is relevant because sooner or later we will all die; death affects us all. Studying the properties of living systems independently of their substrate, ALife describes life as a type of organization. Thus, death entails the loss of that organization. Within this perspective, different notions of death are derived from different notions of life. Also, the relationship between life and mind and the implications of death to the mind are discussed. A criterium is proposed in which the value of life depends on its uniqueness, i.e. a living system is more valuable if it is harder to replace. However, this does not imply that death in replaceable living systems is unproblematic. This is decided on whether there is harm to the system produced by death. The paper concludes with speculations about how the notion of death could be shaped in the future.

An Observer Approach for Deterministic Learning Using Patchy Neural Networks with Applications to Fuzzy Cognitive Networks

In this paper, a new methodology is proposed for deterministic learning with neural networks. Using an observer that employs the integral of the sign of the error term, asymptotic estimation of the respective nonlinear vector field is achieved. Patchy Neural Networks (PNNs) are introduced to identify the unknown nonlinearity from the observer’s output and the state measurements. The proposed scheme achieves learning with a single pass from the respective patches and does not need standard persistency of excitation conditions. Furthermore, the PNN weights are updated algebraically, reducing the computational load of learning significantly. Simulation results for a Duffing oscillator and a fuzzy cognitive network illustrate the effectiveness of the proposed approach.

Considerations on Strategies to Improve EOG Signal Analysis

Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared with parametric, frequency-based approach using an autoregressive (AR) model as well as template matching as a time based method. The results indicate that parametric AR modeling using the Burg method, which does not retain the phase information, gives poor class separation. Conversely, the projection on the approximation space of the fourth level of Haar wavelet decomposition yields feature sets that enhance the class separation. Furthermore, for this method the number of dimensions in the feature space is much reduced as compared to template matching, which makes it much more efficient in terms of computation. This paper also reports on an example application utilizing wavelet decomposition and the Linear Discriminant Analysis (LDA) for classification, which was implemented and evaluated successfully. In this application, a virtual keyboard acts as the front-end for user interactions.

Existence of Positive Solutions for Generalized p-Laplacian BVPs

where are given. The authors examine and discuss these solutions.