An Analysis of Human Behavior during Evacuation

The World Trade Center Evacuation: The evacuation of the WTC complex represents one of the largest full-scale evacuations of people in modern times.

Analysis and modeling of heaping behavior of granular mixtures within a computational mechanics framework

This paper presents a continuum model of the flow of granular material during filling of a silo, using a viscoplastic constitutive relation based on the Drucker-Prager plasticity yield function. The performed simulations demonstrate the ability of the model to realistically represent complex features of granular flows during filling processes, such as heap formation and non-zero inclination angle of the bulk material-air interface. In addition, micro-mechanical parametrizations which account for particle size segregation are incorporated into the model. It is found that numerical predictions of segregation phenomena during filling of a binary granular mixture agree well with experimental results. Further numerical tests indicate the capability of the model to cope successfully with complex operations involving granular mixtures.

Numerical optimisation techniques applied to evacuation analysis

A common problem faced by fire safety engineers in the field of evacuation analysis concerns the optimal design of an arbitrarily complex structure in order to minimise evacuation times. How does the engineer determine the best solution? In this study we introduce the concept of numerical optimisation techniques to address this problem. The study makes user of the buildingEXODUS evacuation model coupled with classical optimisation theory including Design of Experiments (DoE) and Response Surface Models (RSM). We demonstrate the technique using a relatively simple problem of determining the optimal location for a single exit in a square room.

Determining factors of the innovative behavior of cooperatives: an analysis of the case of Castilla y León

Cooperatives have a long historical experience in the Spanish economy and have demonstrated their ability to compete against traditional firms in the market. To maintain this capability, while taking advantage of the competitive advantages associated with their idiosyncrasies as social economy enterprises, they should take into consideration that the economy is increasingly globalized and increasingly knowledge-based, especially with regards to technological content. As a consequence, the innovative capacity appears to be a key aspect in order to be able to challenge competitors. This article characterizes the innovative behavior of cooperatives in the region of Castile and Leon and analyses the internal and external factors affecting their innovative performance, based on data from a survey of 581 cooperatives. The results of the empirical analysis, which is performed by multivariate binary logistic regression on various types of innovation, lead us to identify the size of the organizations, the existence of planning, the R & D activities and the human capital as the main determining factors.

Social networks applied to training

In this article, we address the importance and relevance that social networks exhibit in their use as an educational resource.  This relevance relies in the possibility of implementing new learning resources or increasing the level of the participant's connectivity, as well as developing learning communities.  Also, the risk entailed from their use is discussed, especially for the students that have a low technological education or those having excessive confidence on the media.  It is important to highlight that the educational use of social networks is not a simple extension or translation of the student's habitual, recreational use, but that it implies an important change in the roles given to teachers as well as learners; from accommodative learning environments that only encourage memorization to other environments that demand an active, reflective, collaborative and proactive attitude, that require the development/acquisition of technological as well as social abilities, aptitudes and values.  It is also important to highlight that a correct implementation and adequate use will not only foment formal learning, but also informal and non-formal learning.

Multivariate Statistical Analysis Applied to an IL6 Signal Transduction Model in Hepatocytes

This paper introduces the application of linear multivariate statistical techniques, including partial least squares (PLS), canonical correlation analysis (CCA) and reduced rank regression (RRR), into the area of Systems Biology. This new approach aims to extract the important proteins embedded in complex signal transduction pathway models.The analysis is performed on a model of intracellular signalling along the janus-associated kinases/signal transducers and transcription factors (JAK/STAT) and mitogen activated protein kinases (MAPK) signal transduction pathways in interleukin-6 (IL6) stimulated hepatocytes, which produce signal transducer and activator of transcription factor 3 (STAT3).A region of redundancy within the MAPK pathway that does not affect the STAT3 transcription was identified using CCA. This is the core finding of this analysis and cannot be obtained by inspecting the model by eye. In addition, RRR was found to isolate terms that do not significantly contribute to changes in protein concentrations, while the application of PLS does not provide such a detailed picture by virtue of its construction.This analysis has a similar objective to conventional model reduction techniques with the advantage of maintaining the meaning of the states prior to and after the reduction process. A significant model reduction is performed, with a marginal loss in accuracy, offering a more concise model while maintaining the main influencing factors on the STAT3 transcription.The findings offer a deeper understanding of the reaction terms involved, confirm the relevance of several proteins to the production of Acute Phase Proteins and complement existing findings regarding cross-talk between the two signalling pathways.

Structural influence of gene networks on their inference: analysis of C3NET

Background: The availability of large-scale high-throughput data possesses considerable challenges toward their functional analysis. For this reason gene network inference methods gained considerable interest. However, our current knowledge, especially about the influence of the structure of a gene network on its inference, is limited.

Numerical study of the effects of reinforcement/matrix interphase on stress-strain behavior of YAl2 particle reinforced MgLiAl composites

For the potential influence produced by the reinforcement/matrix interphase in particle reinforced metal matrix composites (PMMCs), a unit cell model with transition interphase was proposed. Uniaxial tensile loading was simulated and the stress/strain behavior was predicted. The results show that a transition interphase with both appropriate strength and thickness could affect the failure mode, reduce the stress concentration, and enhance the maximum strain value of the composite.

The Behavior of compressively loaded stiffener runout specimens - Part II: Finite element analysis

The termination of stiffeners in composite aircraft structures give rise to regions of high interlaminar shear and peel stresses as the load in the stiffener is diffused into the skin. This is of particular concern in co-cured composite stiffened structures where there is a relatively low resistance to through-thickness stress components at the skin-stiffener interface. In Part I, experimental results of tested specimens highlighted the influence of local design parameters on their structural response. Indeed some of the observed behavior was unexpected. There is a need to be able to analyse a range of changes in geometry rapidly to allow the analysis to form an integral part of the structural design process.

This work presents the development of a finite element methodology for modelling the failure process of these critical regions. An efficient thick shell element formulation is presented and this element is used in conjuction with the Virtual Crack Closure Technique (VCCT) to predict the crack growth characteristics of the modelled specimens. Three specimens were modelled and the qualitative aspects of crack growth were captured successfully. The shortcomings in the quantitative correlation between the predicted and observed failure loads are discussed. There was evidence to suggest that high through-thickness compressive stresses enhanced the fracture toughness in these critical regions.

Nanoscale Origins of Nonlinear Behavior in Ferroic Thin Films

The nonlinear response of a ferroic to an applied field has been studied through the phenomenological Rayleigh Law for over a hundred years. Yet, despite this, the fundamental physical mechanisms at the nanoscale that lead to macroscopic Rayleigh behavior have remained largely elusive, and experimental evidence at small length scales is limited. Here, it is shown using a combination of scanning probe techniques and phase field modeling, that nanoscale piezoelectric response in prototypical Pb(Zr,Ti)O3 films appears to follow a distinctly non-Rayleigh regime. Through statistical analysis, it is found that an averaging of local responses can lead directly to Rayleigh-like behavior of the strain on a macroscale. Phase-field modeling confirms the twist of the ferroelastic interface is key in enhancing piezoelectric response. The studies shed light on the nanoscale origins of nonlinear behavior in disordered ferroics.

Prediction of failure and fracture mechanisms of polymeric composites using finite element analysis. Part 1:Particulate filled composites

Micro-mechanical analysis of polymeric composites provides a powerful means for the quantitative assessment of their bulk behavior. In this paper we describe a robust finite element model (FEM) for the micro-structural modeling of the behavior of particulate filled polymer composites under external loads. The developed model is applied to simulate stress distribution in polymer composites containing particulate fillers. Quantitative information about the magnitude and location of maximum stress concentrations obtained from these simulations is used to predict the dominant failure and crack growth mechanisms in these composites. The model predictions are compared with the available experimental data and also with the values found using other methods reported in the literature. These comparisons show the range of the validity of the developed model and its predictive potential.

Analysis of deformation behavior and workability of advanced 9Cr-Nb-V ferritic heat resistant steels

Hot compression tests were carried out on 9Cr–Nb–V heat resistant steels in the temperature range of 600–1200 °C and the strain rate range of 10−2–100 s−1 to study their deformation characteristics. The full recrystallization temperature and the carbon-free bainite phase transformation temperature were determined by the slope-change points in the curve of mean flow stress versus the inverse of temperature. The parameters of the constitutive equation for the experimental steels were calculated, including the stress exponent and the activation energy. The lower carbon content in steel would increase the fraction of precipitates by increasing the volume of dynamic strain-induced (DSIT) ferrite during deformation. The ln(εc) versus ln(Z) and the ln(σc) versus ln(Z) plots for both steels have similar trends. The efficiency of power dissipation maps with instability maps merged together show excellent workability from the strain of 0.05 to 0.6. The microstructure of the experimental steels was fully recrystallized upon deformation at low Z value owing to the dynamic recrystallization (DRX), and exhibited a necklace structure under the condition of 1050 °C/0.1 s−1 due to the suppression of the secondary flow of DRX. However, there were barely any DRX grains but elongated pancake grains under the condition of 1000 °C/1 s−1 because of the suppression of the metadynamic recrystallization (MDRX).