Handling Emergency Management in [an] Object Oriented Modeling Environment

It has been understood that protection of a nation from extreme disasters is a challenging task. Impacts of extreme disasters on a nation's critical infrastructures, economy and society could be devastating. A protection plan itself would not be sufficient when a disaster strikes. Hence, there is a need for a holistic approach to establish more resilient infrastructures to withstand extreme disasters. A resilient infrastructure can be defined as a system or facility that is able to withstand damage, but if affected, can be readily and cost-effectively restored. The key issue to establish resilient infrastructures is to incorporate existing protection plans with comprehensive preparedness actions to respond, recover and restore as quickly as possible, and to minimize extreme disaster impacts. Although national organizations will respond to a disaster, extreme disasters need to be handled mostly by local emergency management departments. Since emergency management departments have to deal with complex systems, they have to have a manageable plan and efficient organizational structures to coordinate all these systems. A strong organizational structure is the key in responding fast before and during disasters, and recovering quickly after disasters. In this study, the entire emergency management is viewed as an enterprise and modelled through enterprise management approach. Managing an enterprise or a large complex system is a very challenging task. It is critical for an enterprise to respond to challenges in a timely manner with quick decision making. This study addresses the problem of handling emergency management at regional level in an object oriented modelling environment developed by use of TopEase software. Emergency Operation Plan of the City of Hampton, Virginia, has been incorporated into TopEase for analysis. The methodology used in this study has been supported by a case study on critical infrastructure resiliency in Hampton Roads

Prediction of Ionic Cr (VI) Extraction Efficiency in Flat Sheet Supported Liquid Membrane Using Artificial Neural Networks (ANNs)

ABSTRACT:Artificial neural networks (ANNs) are computer techniques that attempt to simulate the functionality and decision-making processes of the human brain. In the past few decades, artificial neural networks (ANNs) have been extensively used in a wide range of engineering applications. There are only a few applications in liquid membrane process. The objective of this research was to develop artificial neural networks (ANNs) model to estimate Cr (VI) extraction efficiency in feed phase.Data set (413 experiment records) were obtained from a laboratory scale experimental study. Various combinations of experimental data, namely % (w/w) extractant Alamine 336 concentration in membrane phase, stirring speed in feed and stripping phase, flat sheet support type, stripping phase NaOH concentration, feed phase pH, diluents type, % (w/w) diluents concentration, polymer support type, extractant type, and time are used as inputs into the ANN so as to evaluate the degree of effect of each of these variables on Cr (VI) extraction efficiency in feed phase. The results of the ANN model is compared with multiple linear regression model (MLR). Mean square error (MSE), average absolute relative error (AARE) and coefficient of determination (R 2 ) statistics are used as comparison criteria for the evaluation of the model performances. Based on the comparisons, it was found that the ANN model could be employed successfully in estimating the Cr (VI) extraction efficiency

Componential coding in the condition monitoring of electrical machines Part 2: application to a conventional machine and a novel machine

This paper (Part 2) presents the practical application of componential coding, the principles of which were described in the accompanying Part 1 paper. Four major issues are addressed, including optimization of the neural network, assessment of the anomaly detection results, development of diagnostic approaches (based on the reconstruction error) and also benchmarking of componential coding with other techniques (including waveform measures, Fourier-based signal reconstruction and principal component analysis). This is achieved by applying componential coding to the data monitored from both a conventional induction motor and from a novel transverse flux motor. The results reveal that machine condition monitoring using componential coding is not only capable of detecting and then diagnosing anomalies but it also outperforms other conventional techniques in that it is able to separate very small and localized anomalies

Fragmentation of Fractal Random Structures

We analyze the fragmentation behavior of random clusters on the lattice under a process where bonds between neighboring sites are successively broken. Modeling such structures by configurations of a generalized Potts or random-cluster model allows us to discuss a wide range of systems with fractal properties including trees as well as dense clusters. We present exact results for the densities of fragmenting edges and the distribution of fragment sizes for critical clusters in two dimensions. Dynamical fragmentation with a size cutoff leads to broad distributions of fragment sizes. The resulting power laws are shown to encode characteristic fingerprints of the fragmented objects.Comment: Thoroughly revised version. Final version published in Physical Review Letter

Application of conflictology methods for evaluating physical protection systems effectiveness

At present, physical protection of nuclear material and nuclear facilities is actual. For the implementation of physical protection, Physical Protection System (PPS) is created at nuclear facilities. We all know that the most important characteristic is effectiveness of physical protection systems. PPS effectiveness value is determined by the probability that reaction forces can stop and intercept the intruder. There are many methods to assess the effectiveness of PPS. However, not all methods can provide an accurate quantitative assessment of the effectiveness of security systems. This work presents an approach for assessing the resistance of PPS to emerging threat (that is, the intruder to act against items of physical protection).Based on the fact that different processes are subject to universal physical laws and principles of development, a parallel between the concepts of Conflictology field was established to describe the interaction in the system "intruder against PPS"

Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution

The standard approach to analyzing 16S tag sequence data, which relies on clustering reads by sequence similarity into Operational Taxonomic Units (OTUs), underexploits the accuracy of modern sequencing technology. We present a clustering-free approach to multi-sample Illumina datasets that can identify independent bacterial subpopulations regardless of the similarity of their 16S tag sequences. Using published data from a longitudinal time-series study of human tongue microbiota, we are able to resolve within standard 97% similarity OTUs up to 20 distinct subpopulations, all ecologically distinct but with 16S tags differing by as little as 1 nucleotide (99.2% similarity). A comparative analysis of oral communities of two cohabiting individuals reveals that most such subpopulations are shared between the two communities at 100% sequence identity, and that dynamical similarity between subpopulations in one host is strongly predictive of dynamical similarity between the same subpopulations in the other host. Our method can also be applied to samples collected in cross-sectional studies and can be used with the 454 sequencing platform. We discuss how the sub-OTU resolution of our approach can provide new insight into factors shaping community assembly.Comment: Updated to match the published version. 12 pages, 5 figures + supplement. Significantly revised for clarity, references added, results not change

Embrittlement in CN3MN Grade Superaustenitic Stainless Steels

Superaustenitic stainless steels (SSS) are widely used in extreme environments such as off-shore oil wells, chemical and food processing equipment, and seawater systems due to their excellent corrosion resistance and superior toughness. The design of the corresponding heat treatment process is crucial to create better mechanical properties. In this respect, the short-term annealing behavior of CN3MN grade SSS was investigated by a combined study of Charpy impact tests, hardness measurements, scanning and transmission electron microscopy. Specimens were heat treated at 1200 K (927 A degrees C) for up to 16 minutes annealing time and their impact strengths and hardnesses were tested. The impact toughness was found to decrease to less than the half of the initial values while hardness stayed the same. Detailed fracture surface analyses revealed a ductile to brittle failure transition for relatively short annealing times. Brittle fracture occurred in both intergranular and transgranular modes. SEM and TEM indicated precipitation of nano-sized intermetallics, accounting for the intergranular embrittlement, along the grain boundaries with respect to annealing time. The transgranular fracture originated from linear defects seen to exist within the grains. Close observation of such defects revealed stacking-fault type imperfections, which lead to step-like cracking observed in microlength scales