A Method for the Analysis of Behavioural
Uncertainty in Evacuation Modelling
Enrico Ronchi*, Department of Fire Safety Engineering and Systems Safety,
Lund University, P.O. Box 118, 22100 Lund, Sweden
Paul A. Reneke and Richard D. Peacock, National Institute of Standards and
Technology, Gaithersburg, MD 20899, USA
Received: 4 April 2013/Accepted: 22 June 2013
Abstract. Evacuation models generally include the use of distributions or probabilis-
tic variables to simulate the variability of possible human behaviours. A single model
setup of the same evacuation scenario may therefore produce a distribution of differ-
ent occupant-evacuation time curves in the case of the use of a random sampling
method. This creates an additional component of uncertainty caused by the impact of
the number of simulated runs of the same scenario on evacuation model predictions,
here named behavioural uncertainty. To date there is no universally accepted quanti-
tative method to evaluate behavioural uncertainty and the selection of the number of
runs is left to a qualitative judgement of the model user. A simple quantitative
method using convergence criteria based on functional analysis is presented to
address this issue. The method permits (1) the analysis of the variability of model
predictions in relation to the number of runs of the same evacuation scenario, i.e. the
study of behavioural uncertainty and (2) the identification of the optimal number of
runs of the same scenario in relation to pre-defined acceptance criteria.
Keywords: Evacuation modelling, Behavioural uncertainty, Human behaviour in fire,
Functional analysis, Convergence criteria
1. Introduction
Uncertainty is divided into different components in the context of fire safety engi-
neering and modelling [1]: model input uncertainty, measurement uncertainty, and
intrinsic uncertainty.
(1) Model input uncertainty is associated with the parameters obtained from
experimental measurements that are used as model input, i.e. the assumptions
employed to derive model input from the experiments.
(2) Measurement uncertainty is associated with the experimental measurement
itself, i.e., the data collection techniques employed.
(3) Intrinsic uncertainty is the uncertainty associated with the physical and mathe-
matical assumptions and methods that are intrinsic to the model formulation.
* Correspondence should be addressed to: Enrico Ronchi, E-mail: [email protected]
Fire Technology, 50, 1545–1571, 2014
� 2013 Springer Science+Business Media New York. Manufactured in The United States
DOI: 10.1007/s10694-013-0352-7
12
In the case of evacuation data, uncertainty includes an additional component,
here named behavioural uncertainty. Behavioural uncertainty is uncertainty asso-
ciated with the stochastic nature of human behaviour, i.e. human behaviour is sto-
chastic per se [2], and a single experiment or model run may not be representative
of a full range of the behaviours of the occupants. In fa.
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.
Zlatan Ibrahimović – Sports Psychology
Outline
Introduction:
· General Info
· Nationality, Birthplace, Parents
· Childhood What he wanted to do growing up?
· When did he start playing professionally?
· Which teams did he play for?
· Give some of his career statistics and maybe records?
· What trophies has he won with club football and national team of Sweden?
· Style of Play
· What is his personality like? How do people see him in the media?\
·
Body Paragraphs
Connect the following Sports Psychology Concepts (or even those not listed) to Zlatan Ibrahimović
What is his personality type? Type A, B C, or D?
Give examples through research of where he shows this.
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· Performance increases as arousal increases but when arousal gets too high performance dramatically decreases. This is usually caused by the performer becoming anxious and sometimes making wrong decisions. Catastrophes is caused by a combination of cognitive and somatic anxieties. Cognitive is the internal worries of not performing well while somatic is the physical effects of muscle tension/butterflies and fatigue through playing.
· The graph is an inverted U where the x line is the arousal and the y is the performance. Performance peaks on the top of the inverted U and the catastrophe happens in the fall of the inverted U
HIGH TRAIT ANXIETY ATHLETES… HOW DO THEY PERCEIVE COMPETITION?
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· Trait Anxiety: is a behavioral disposition to perceive as threatening circumstances that objectively may not be dangerous and to then respond with disproportionate state anxiety.
· Somatic Trait Anxiety: the degree to which one typically perceived heightened physical symptoms (muscle tension)
· Cognitive Trait Anxiety: the degree to which one typically worries or has self doubt
· Concentration Disruption: the degree to which one typically has concentration disruption during competition
People usually with high trait anxiety usually have more state anxiety in highly competitive evaluative situations than do people with lower trait anxiety. Example two athletes are playing basketball and both are physically and statistically the same both have to shoot a final free throw to win the game. Athlete A is more laid back which means his trait anxiety is lower and he doesn't view the final shot as a overly threatening. Athlete B has a high trait anxiety and because of that he perceives the final shot as very threatening. This has an effect on his state anxiety much more than.
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overall error inherent this model due its finiteness could be compared with the actual experimental measurement
error and should be useful in guiding future investigations. In this context, we propose a strategy relying on
thermodynamic theory of information processes, to estimate this error that cannot be done an arbitrarily small.
For the considered assumptions, the calculated error of the main researched variable, measured in conventional
field studies, should not be less than the error caused by the limited number of dimensional variables of the
physical-mathematical model. Examples of practical application of the proposed concept for spacecraft heating,
climate prediction, thermal energy storage and food freezing are discussed
A learning based transportation oriented simulation systemWolfsbane_John
This document describes the conceptual development, operationalization, and empirical testing of Albatross, an activity-based travel demand model. The model predicts which activities individuals conduct, where, when, how long, with whom, and their transport mode. It incorporates situational, temporal, spatial, and institutional constraints. A key concept is that individuals make activity and travel decisions using heuristics and reinforcement learning rather than utility maximization. The model represents decision making using decision trees derived from activity diary data. Testing showed the model performs satisfactorily in predicting activity-travel patterns.
The document discusses using machine learning algorithms and supervised learning methods to develop an automated system for detecting nanoparticles and estimating their size and spatial distribution from scanning electron microscope images. The goal is to enable industrial-scale manufacturing of nanomaterials by applying quality control tools. Specifically, the research uses support vector machines and scale-invariant feature transform to extract features from images and classify pixels as nanorods or background in order to predict locations and dimensions of nanorods.
Simulating Multivariate Random Normal Data using Statistical Computing Platfo...ijtsrd
This document describes how to simulate multivariate normal random data using the statistical computing platform R. It discusses two main decomposition methods for generating such data - Eigen decomposition and Cholesky decomposition. These methods decompose the variance-covariance matrix in different ways to simulate random normal values that match the desired mean and variance-covariance structure. The document provides code examples in R to implement both methods and compares the results. It finds that both methods accurately reproduce the target multivariate normal distribution.
The main concept of neutrosophy is that any idea has not only a certain degree of truth but also a degree of falsity and indeterminacy in its own right. Although there are many applications of neutrosophy in different disciplines, the incorporation of its logic in education and psychology is rather scarce compared to other fields. In this study, the Satisfaction with Life Scale was converted into the neutrosophic form and the results were compared in terms of confirmatory analysis by convolutional neural networks. To sum up, two different formulas are proposed at the end of the study to determine the validity of any scale in terms of neutrosophy. While the Lawshe methodology concentrates on the dominating opinions of experts limited by a one-dimensional data space analysis, it should be advocated that the options can be placed in three-dimensional data space in the neutrosophic analysis . The effect may be negligible for a small number of items and participants, but it may create enormous changes for a large number of items and participants. Secondly, the degree of freedom of Lawshe technique is only 1 in 3D space, whereas the degree of freedom of neutrosophical scale is 3, so researchers have to employ three separate parameters of 3D space in neutrosophical scale while a resarcher is restricted in a 1D space in Lawshe technique in 3D space. The third distinction relates to the analysis of statistics. The Lawhe technical approach focuses on the experts' ratio of choices, whereas the importance and correlation level of each item for the analysis in neutrosophical logic are analysed. The fourth relates to the opinion of experts. The Lawshe technique is focused on expert opinions, yet in many ways the word expert is not defined. In a neutrosophical scale, however, researchers primarily address actual participants in order to understand whether the item is comprehended or opposed to or is imprecise. In this research, an alternative technique is presented to construct a valid scale in which the scale first is transformed into a neutrosophical one before being compared using neural networks. It may be concluded that each measuring scale is used for the desired aim to evaluate how suitable and representative the measurements obtained are so that its content validity can be evaluated.
Developing of climate data for building simulation with future weather condit...Rasmus Madsen
Today, climate models are used frequently to describe past, current or future climate conditions in par-ticular building simulation. A research study of how future climate change will affect the future indoor environment and buildings energy use in a Danish context has been conducted. To fulfil this research study, information of how climate models are developed are needed as well. The research study includes an objective descriptive approach from both Danish and global research of the given topic. The gathered information from the publications is evaluated with respect to indicators for the quality of the journals as well as the authors. The method used for development of the Danish design reference year, is not clear, and to have a full knowledge of how the climate change will affect building simulation in a Danish context, further research is needed. This research for development of a new Danish weather file will require both a descriptive and analytical research.
IRJET- Violent Social Interaction RecognitionIRJET Journal
The document presents a method for detecting violent social interactions in surveillance videos. The method uses an adaptive appearance model and a low-rank and structured sparse matrix decomposition model to highlight signs of violence. Localized spatio-temporal features are analyzed to detect changes in motion across adjacent video frames. The method was evaluated on a benchmark dataset and showed promising results in accurately detecting violent social interactions.
Md simulation and stochastic simulationAbdulAhad358
Stochastic simulation involves modeling systems with random variables. It generates random values for insertion into models to understand probable outcomes. Molecular dynamic simulation computationally simulates atom and molecule movements over time based on forces. It provides time-dependent behavior analysis of biological molecules to study structure, dynamics, and thermodynamics without harming environments. Both methods help understand complex systems through numerous replications under varying scenarios.
Ambulance Service Planning Simulation And Data VisualisationKayla Jones
The document discusses the development of a simulation tool called BARTSIM to help optimize ambulance service planning for the St. John Ambulance Service in Auckland, New Zealand. Key aspects of BARTSIM include using real call data from the ambulance service's database to drive the simulation, incorporating a detailed time-varying travel model to more accurately estimate travel times, and utilizing geographic information systems for spatial visualization of data and simulation results. The tool was well-received by St. John's management and has since been adapted for other ambulance services.
A contextual bandit algorithm for mobile context-aware recommender systemBouneffouf Djallel
Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user’s content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user’s situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.
The document provides an overview of key aspects of the survey process, including:
1) It discusses the survey process as a series of steps including defining objectives, sampling, instrument design, data collection and analysis.
2) Modes of contact, response and follow-up with respondents are described as important components that can each use different media like phone, mail or web.
3) Probability and convenience samples are distinguished, with probability samples allowing statistical inference but being more expensive to conduct.
This document summarizes a paper that quantifies uncertainties in petrophysical properties derived from well logs. It shows that the interpretation model used can be a major source of uncertainty, and that Monte Carlo modeling provides a way to quantify this. Different log analysis models are compared for water, oil, and gas bearing sands. For gas sands, density and sonic porosity estimates without hydrocarbon corrections exceed the actual porosity range. Core data provides the best uncertainty estimates by validating logs, and Monte Carlo can match these when using the proper interpretation model.
This document discusses structural equation modeling and recommends a two-step approach for theory testing and development. In the two-step approach, measurement models are first estimated separately to assess construct validity before estimating full structural models. This allows for separate evaluation and respecification of the measurement model, providing a better assessment of a theory's validity. The document reviews exploratory versus confirmatory analysis and outlines the two-step modeling process as a comprehensive approach for theory testing.
Heuristics for the Maximal Diversity Selection ProblemIJMER
The problem of selecting k items from among a given set of N items such that the ‘diversity’
among the k items is maximum, is a classical problem with applications in many diverse areas such as
forming committees, jury selection, product testing, surveys, plant breeding, ecological preservation,
capital investment, etc. A suitably defined distance metric is used to determine the diversity. However,
this is a hard problem, and the optimal solution is computationally intractable. In this paper we present
the experimental evaluation of two approximation algorithms (heuristics) for the maximal diversity
selection problem
Optimal design & Population mod pyn.pptxPawanDhamala1
This document discusses optimal design and population modeling. It begins with an introduction to optimal design, noting that it allows parameters to be estimated without bias and with minimum variance. The advantages of optimal design are that it reduces experimentation costs by allowing statistical models to be estimated with fewer runs. It then describes different types of optimal designs such as A, C, D, and E optimality. The document next discusses population modeling, explaining that it is a tool for integrating data to aid drug development decisions. It notes the key components of population models are structural models, stochastic models, and covariate models. Structural models describe the response over time using algebraic or differential equations, while stochastic models describe variability and covariate models influence factors like dem
Deep Learning for Biomedical Unstructured Time SeriesPetteriTeikariPhD
1D Convolutional neural networks (CNNs) for time series analysis, and inspiration from beyond biomedical field. Short intro for various different steps involved in Time Series Analysis including outlier detection, imputation, denoising, segmentation, classification and forecasting.
Available also from:
https://www.dropbox.com/s/cql2jhrt5mdyxne/timeSeries_deepLearning.pdf?dl=0
This document discusses uncertainty management in reservoir modeling. It describes how reservoir models incorporate uncertainty due to incomplete data and imperfect interpretations. Stochastic modeling uses geostatistics to generate multiple reservoir realizations consistent with available data and incorporate uncertainty. An uncertainty space of models (USM) encapsulates variability between realizations due to uncertainty. The document proposes an 8-step methodology to systematically assess and quantify uncertainty propagated to volumetric calculations and provide ranked realizations for dynamic modeling. The methodology was successfully applied in case studies and aims to reduce costs by efficiently evaluating a representative number of realizations.
Multimodal authentication is one of the prime concepts in current applications of real scenario. Various
approaches have been proposed in this aspect. In this paper, an intuitive strategy is proposed as a
framework for providing more secure key in biometric security aspect. Initially the features will be
extracted through PCA by SVD from the chosen biometric patterns, then using LU factorization technique
key components will be extracted, then selected with different key sizes and then combined the selected key
components using convolution kernel method (Exponential Kronecker Product - eKP) as Context-Sensitive
Exponent Associative Memory model (CSEAM). In the similar way, the verification process will be done
and then verified with the measure MSE. This model would give better outcome when compared with SVD
factorization[1] as feature selection. The process will be computed for different key sizes and the results
will be presented.
This document describes a methodology for identifying critical time periods from hydrological observation data that contain important information for calibrating hydrological models. The methodology uses a statistical concept called data depth to identify unusual events in discharge or precipitation time series that lie near the boundary of the multivariate data set. These unusual events, which include extremes, long dry or wet periods, and periods of strong dynamics, are considered critical periods for model calibration. The methodology is tested on discharge and precipitation data from a catchment in Germany using two hydrological models. The results show that calibration using only the critical periods identified is only slightly worse than calibration using all the data, and the model parameters have similar transferability to different time periods.
Similar to A Method for the Analysis of BehaviouralUncertainty in Evacu.docx (20)
Zoe is a second grader with autism spectrum disorders. Zoe’s father .docxransayo
Zoe is a second grader with autism spectrum disorders. Zoe’s father recently passed away in a tragic car accident. Zoe, her mom, and two older brothers have temporarily relocated from out-of-state and are now living in her grandparents’ house in a small, rural community.
Because the family had been living out-of state, Zoe has never interacted with her grandparents. She has challenges responding to social cues, including her name and in understanding gestures. She also engages in repetitive body movements. She is fond of her set of dolls and likes lining them up. When Zoe is agitated, her mother plays Mozart, which seems to have a calming effect. Zoe also enjoys macaroni and cheese.
Her grandparents do not understand Zoe’s attempts at communicating. Zoe does not respond well to crowded and noisy environments. Zoe’s mom is working outside the home for the first time.
Because of the move, Zoe has transferred to a new school, which does not currently have any students with ASD. Although her mom is generally very involved with Zoe’s education, she is away from the home much of the time due to a long commute for her new job is a neighboring city.
Zoe’s grandparents are eager and willing to help in any way they can.
Imagine you are serving as an ASD consultant at Zoe’s new school. Using the COMPASS model, create a COMPASS Action Plan for Zoe by complete the following tasks:
Identify the personal challenges for Zoe;
Identify the environmental challenges for Zoe;
Identify potential supports; and
Identify and prioritize teaching goals.
In addition, include a 250-500-word rationale that explains how your action plan for Zoe demonstrates collaboration in a respectful, culturally responsive way while promoting understanding, resolving conflicts, and building consensus around her interventions.
.
Zlatan Ibrahimović – Sports Psychology
Outline
Introduction:
· General Info
· Nationality, Birthplace, Parents
· Childhood What he wanted to do growing up?
· When did he start playing professionally?
· Which teams did he play for?
· Give some of his career statistics and maybe records?
· What trophies has he won with club football and national team of Sweden?
· Style of Play
· What is his personality like? How do people see him in the media?\
·
Body Paragraphs
Connect the following Sports Psychology Concepts (or even those not listed) to Zlatan Ibrahimović
What is his personality type? Type A, B C, or D?
Give examples through research of where he shows this.
CATASTROPHE THEORY… OCCURS WHEN? WHAT DOES THE GRAPH LOOK LIKE
· Arousal: is a blend of physiological and psychological activity in a person and it refers to the intensity dimensions of motivation at a particular moment. It ranges from not aroused, to completely aroused, to highly aroused; this is when individuals are mentally and physically activated.
· Performance increases as arousal increases but when arousal gets too high performance dramatically decreases. This is usually caused by the performer becoming anxious and sometimes making wrong decisions. Catastrophes is caused by a combination of cognitive and somatic anxieties. Cognitive is the internal worries of not performing well while somatic is the physical effects of muscle tension/butterflies and fatigue through playing.
· The graph is an inverted U where the x line is the arousal and the y is the performance. Performance peaks on the top of the inverted U and the catastrophe happens in the fall of the inverted U
HIGH TRAIT ANXIETY ATHLETES… HOW DO THEY PERCEIVE COMPETITION?
· Anxiety: is a negative emotional state in which feelings of nervousness, worry and apprehension are associated with activation or arousal of the body
· Trait Anxiety: is a behavioral disposition to perceive as threatening circumstances that objectively may not be dangerous and to then respond with disproportionate state anxiety.
· Somatic Trait Anxiety: the degree to which one typically perceived heightened physical symptoms (muscle tension)
· Cognitive Trait Anxiety: the degree to which one typically worries or has self doubt
· Concentration Disruption: the degree to which one typically has concentration disruption during competition
People usually with high trait anxiety usually have more state anxiety in highly competitive evaluative situations than do people with lower trait anxiety. Example two athletes are playing basketball and both are physically and statistically the same both have to shoot a final free throw to win the game. Athlete A is more laid back which means his trait anxiety is lower and he doesn't view the final shot as a overly threatening. Athlete B has a high trait anxiety and because of that he perceives the final shot as very threatening. This has an effect on his state anxiety much more than.
Zia 2Do You Choose to AcceptYour mission, should you choose.docxransayo
Zia 2
Do You Choose to Accept?
Your mission, should you choose to accept it, is to go out and see Mission: Impossible-Fallout. As I sat back in my red-cushioned seat, accompanied by my brothers, I knew I was in for something special. The film takes place two years after two-thousand fifteens hit movie, Mission: Impossible-Rogue Nation. While I had no clue what to expect, I knew I was going to be in for an incredible ride as soon as the movie began with the intense dialogue between Ethan Hunt (Tom Cruise) and Solomon Lane (Sean Harris). From beginning to end, Mission: Impossible- Fallout delivers crazy action-thriller scenes, inventive special effects, and creative cinematography.
Mission: Impossible-Fallout is based on a story of an American agent who must retrieve nuclear weapons from an enemy terrorist organization with help of his specialized IMF team. The film was consistent the first hour with it involving the audience in the mission of the secret organization and trying to figure out the next move of the evil organization known as the Apostles. However, towards the middle of the movie it was revealed that one of the CIA agents was playing the role of a double spy and was on the side of the Apostles. The plot delivered intense action-packed scenes between the opposing groups that personally had me at the edge of my seat. Whether it was a chase on motorcycles, cars, speedboats, or helicopters, each scene had Ethan Hunt running for his life to save the world. Even though I was only viewing the movie from a comfortable movie theater, Hunt zigzagging through the traffic of France on a motorcycle had my fists clenched and adrenaline pumping. However, that was not even the best thriller of the movie. Ethan Hunt trailing Agent Walker in a helicopter with heavy rounds of artillery being fired at each other through the snowcapped mountains of Kashmir may very well be one of the best action scenes in cinematic history. Mission: Impossible-Fallout can be appreciated and enjoyed by all audiences because of its action-packed scenes that keep everyone extremely engaged in the plot.
Mission: Impossible-Fallout brilliantly illustrates the amazing special effects that serve to create the theme and style of the film. From creating bloody wounds to spectacular backgrounds, special effects are abundant throughout the movie. For instance, as Hunt is jumping off an airplane, the special effects of this scene include wind, rain, thunder, and clouds that make the film visually appealing and almost realistic. The thunder striking him as he is skydiving had my jaw wide open simply because of how incredible the illusion was displayed. In almost every fight between Hunt’s team and the Apostles, multiple types of special effects were utilized. Fighting sequences with Hunt angrily running towards Lane and delivering devastating punches accompanied by “POWs” and “AAAHs” seemed so realistic that it had me feeling queasy in my stomach. The gunfire during these fight.
Ziyao LiIAS 3753Dr. Manata HashemiWorking Title The Edu.docxransayo
Ziyao Li
IAS 3753
Dr. Manata Hashemi
Working Title:
The Education Gap
Research Question:
How did the youth of Iran make up the education gap resulted from the Cultural Revolution from 1980 to 1982?
This is a critical question because it involves both education and the youth of Iran. Education and the youth are both very fundamental perspectives for a society to thrive. During the cultural revolution, the education system was shut down, which would undermine the overall quality of a generation. Research of this issue will lead us to the methods used to make up the education gap. It is possible to help other countries suffering similar issues.
Thesis Statement:
After the Iran’s cultural revolution during 1980 to 1982, the youth of Iran made up the education gap caused during the revolution by promoting student movements.
Outline:
· Introduction:
· Cultural Revolution happened in Iran during 1980 to 1982. The education institutions like universities were shut down for the 3-year period. And this gap in education brought significant influence on the youth of Iran at that time. However, the education gap was made up successfully after the revolution.
· State the thesis statement:
· The education gap is made up by the youth in Iran. They promoted the student movement to help the society recover from the revolution.
· The scars left from the revolution
· The revolution lasted 3 years, young people who were supposed to be students had to quit school. The government forced schools to close. The chain of delivering knowledge was broken. And young people cannot find proper things to do when quitting school.
· Student movements
· After the cultural revolution, people in Iran realized they need to correct the current education situation recover the damages resulted from the revolution. Since Iran’s youth has a great number in the society, their power was not to be ignored. They started to fight for their own rights and profits. They were looking for ways to make up the damage has been down. Then the student movement eventually worked for recovering Iran’s education level.
· Conclusion
· The cultural revolution in Iran hurt its education continuity. However, the youth of Iran managed to make up for the damage caused by the cultural revolution. Student movements played the dominant role in this recovering process.
Bibliography:
Khosrow Sobhe (1982) Education in Revolution: is Iran duplicating the Chinese Cultural Revolution?, Comparative Education, 18:3, 271-280, DOI: 10.1080/0305006820180304
Mashayekhi M. The Revival of the Student Movement in Post-Revolutionary Iran. International Journal of Politics, Culture & Society. 2001;15(2):283. doi:10.1023/A:1012977219524.
Razavi, R. (2009). The Cultural Revolution in Iran, with Close Regard to the Universities, and its Impact on the Student Movement. Middle Eastern Studies, 45(1), 1–17. https://doi-org.ezproxy.lib.ou.edu/10.1080/00263200802547586
ZABARDAST, S. (2015). Flourishing of Occid.
Ziyan Huang (Jerry)
Assignment 4
Brand Positioning
Professor Gaur
Target audience:
HR in Ping An Bank Co., Ltd. HRs (interviewers who hire people) from Ping An Bank are usually female, aged 30-40, who look friendly and easy-going. They are sophisticated and skeptic when checking people’s resumes and asking questions during interview. Usually, HRs care about four things: 1. Graduate school ranking. 2. Working experience in bank 3. Oral expression. 4. Personal character. They prefer people who are enthusiastic, energetic and hard-working.
Q1:
Compared to other people who also look for jobs in Ping An Bank, my points of parity would be: 1. I have earned a master degree in a Top 40 U.S. graduate school. 2. I have some intern experience in another bank. My points of differentiation would be: 1. I am confidence in speaking and self-expression. I can serve both Chinese and American clients because I speak fluent Mandarin and English. 2. I am energetic and hard-working. I always have passion in learning something new, which is a key for me to develop working skills.
Q2:
My brand essence: “Energetic, hard-working and modest.”
Q3:
Positioning statement:
Ziyan Huang is for employers from bank,
Who look for excellent employees.
Ziyan Huang is an energetic, hard-working NYU graduate student,
That has passion in developing new working skills.
Because he can speak fluent Mandarin and English,
And have one year working experience in China Merchant Bank,
So that employers can trust him as a reliable candidate.
.
Zhtavius Moye
04/19/2019
BUSA 4126
SWOT Analysis
Dr. Setliff
PORSCHE
Strengths
· Brand Recognition
Not only a brand, but a status symbol for wealth and luxury
· Lean Factory Production
Manpower is low compared to the use of raw materials and supplies
· High Profit Share
The reputation is well-known for good treatment
Weaknesses
· Small automotive manufacture
Porsche has offered the same line of cars for years before extending.
· Limited Customer Sector
Not everyone can afford a Porsche
· Location
Since beginning of time, Porsche has been in Stuttgart, Germany. No space to expand
Opportunities
· Expansion
Deliveries increased in China by 12% but needs more in Asia, Japan, and Indonesia.
· Electric Mobility
A chance to expand Porsche name to many more industries and markets with top competitors such as Tesla.
· S1, O2: Brand recognition extends the range for profitability for the 2020 fully electric Porsche Taycan.
· S3, O1: The annual profitability of the company will encourage others to become a part of the business.
· S2, O1: The cost of a Porsche effects expansion, but by expanding to China could significantly increase rates.
· S3, O1: The location in Germany is a problem for expansion due to limited space of Stuttgart.
Threats
· Technology
Modern technology is advancing to lower cost vehicles.
· Market Competition
Vehicles with similar characteristics at lower cost.
· S3, O2: Weighing heavily on the market Porsche’s reputation will continue to stand abroad its competitors.
· S2, O1: Limited labor will call for more software developers in the more modern technology, especially introducing the fully electric Porsche Taycan.
· S1, O1: Porsche is a company that believes in staying at its classic and luxury perception to their buyers. Still giving all newly updated technology certain things such as an automatic start engine will not be an asset.
· S2, O2: Combined leaves Porsche at a limitation of customers making it hard to expand the market.
VIOLATION OF CIVIL RIGHTS ACT IN ELECTIONS 1
VIOLATION OF CIVIL RIGHTS ACT IN ELECTIONS 2
Violation of Civil Rights Act in Elections
Jake Bookard
Savannah State University
Violation of Civil Rights Act in Elections
Introduction
Despite the assurance of minority voter’s rights by the constitution and the fourteenth amendment, cases of rights violation with regards to the voting process are still on the rise in the US. Minority groups are often discriminated or blocked from participating in the voting process both in ways that they can discern and through cunning plans that can involve the voting process. Some of the main reasons why minorities’ constitutional rights are violated include racial discrimination by majority races, and to manipulate the outcome of the elections so as to keep minority groups out of the political leadership structure. The fourteenth amendment and the constitution do not sufficiently safeguard the rights of minority groups during elections beca.
Zichun Gao Professor Karen Accounting 1AIBM FInancial Stat.docxransayo
Zichun Gao Professor Karen Accounting 1A
IBM FInancial Statement Analysis
Financial Ratios 2019 2018 Formula
Current Ratio 1.02 1.29 CA/CL
Profit Margin 12.22% 12.35% Net Income/Total Revenue
Receiveables Turnover 9.80 10.71 Revenue/Average AR
Average Collection Period 36.72 33.62 365/Receiveables Turnover
Inventory Turnover 25.11 25.36 COST/Average Inventory
Days in Inventory 14.53 14.39 365/Inventory Turnover
Debts to Asset Ratio 0.86 0.86 Total Debts/Total Assets
IBM's days in inventory is around two weeks and this means that goods in the inventory
as efficnetly distributed and that there is a consitantly good inventory control for the
company.
The company's debts to assets ratio is the same for two years and this means that the
company has less debt than asset. However, it is still a relatively poor ratio because this
might show that there are potential problems for the company to generate sufficient
revenue.
The current ratio of the company has decreased over the year, and this means that the
company has less liquid assets to cover its short term liabilities. Since the ratio is
currently approaching 1, the company might be having liquidation problem.
The profit margin for IBM is very stable and it has been about 12% for two years. The
company is performing the profit-generating ability at an average level and it is having
an average profit margin in the industry.
The receiveables turnover is good for the company while between these two years, there
is a decline. As the company is collecting its accounts receiveables around 10 times per
year, the collection is frequent.
The company has been collecting money from customers on credit sales approximately
once every month, and the company usually has fast credit collection, which means that
the risk for credit sales is relatively low.
Inventory turnover measures how many times a company sells and replaces inventory
during a year and for IBM, the number of times is stable and it is constantly around 25.
This means that the company has an efficient control of its goods in the inventory.
Free Cash Flow 11.90 11.90 CF_Operation-Capital Expenditures
Return on Assets 0.06 0.08 Net Income/Total Assets
Asset Turnover 0.51 0.65 Revenue/Assets
Figures From Financial Statement
From Income Statement pg.68
Net Income 9431 9828
Total Revenue 77147 79591
Cost 40657 42655
From Consolidated Balance Sheet pg.70
Current Assets 38420 49146
Current Liabilities 37701 38227
Accounts Receiveables 7870 7432
Inventory 1619 1682
Total Assets 152186 123382
Total Liabilities 131202 106452
From Cash Flow Overview pg.59
Net Cash From Op 14.3 15.6
Capital expenditures 2.4 3.7
The company currently has 11.9 billion dollars free cash flow for two years and this is a
relatively high level of free cash flow. With the high free cash flow, the company can
have more oportunity to expand, invest in new projects, pay dividends, or invest the
money into Resea.
Zheng Hes Inscription This inscription was carved on a stele erec.docxransayo
Zheng He's Inscription
This inscription was carved on a stele erected at a temple to the goddess the Celestial Spouse at Changle in Fujian province in 1431. Message written before his last voyage.
The Imperial Ming Dynasty unifying seas and continents, surpassing the three dynasties even goes beyond the Han and Tang dynasties. The countries beyond the horizon and from the ends of the earth have all become subjects and to the most western of the western or the most northern of the northern countries, however far they may be, the distance and the routes may be calculated. Thus the barbarians from beyond the seas, though their countries are truly distant, "have come to audience bearing precious objects and presents.
The Emperor, approving of their loyalty and sincerity, has ordered us (Zheng) He and others at the head of several tens of thousands of officers and flag-troops to ascend (use) more than one hundred large ships to go and confer presents on them in order to make manifest (make it happen) the transforming power of the (imperial) virtue and to treat distant people with kindness. From the third year of Yongle (1405) till now we have seven times received the commission (official permission) of ambassadors to countries of the western ocean. The barbarian countries which we have visited are: by way of Zhancheng (Champa Cambodia), Zhaowa (Java), Sanfoqi (Palembang- Indonesia) and Xianlo (Siam/Thailand) crossing straight over to Xilanshan (Ceylon- Sri Lanka) in South India, Guli (Calicut) [India], and Kezhi (Cochin India), we have gone to the western regions Hulumosi (Hormuz Between Oman and Iran), Adan (Aden), Mugudushu (Mogadishu- Somalia), altogether more than thirty countries large and small. We have traversed more than one hundred thousand li (distance of 500 meters) of immense water spaces and have beheld in the ocean huge waves like mountains rising sky-high, and we have set eyes on barbarian regions far away hidden in a blue transparency of light vapours, while our sails loftily unfurled like clouds day and night continued their course (rapid like that) of a star, traversing those savage waves as if we were treading a public thoroughfare. Truly this was due to the majesty and the good fortune of the Court and moreover we owe it to the protecting virtue of the divine Celestial Spouse.
The power of the goddess having indeed been manifested in previous times has been abundantly revealed in the present generation. When we arrived in the distant countries we captured alive those of the native kings who were not respectful and exterminated those barbarian robbers who were engaged in piracy, so that consequently the sea route was cleansed and pacified (to make someone or something peaceful) and the natives put their trust in it. All this is due to the favours of the goddess.
We have respectfully received an Imperial commemorative composition (essay/piece of writing) exalting the miraculous favours, which is the highest recompense and.
Zhou 1Time and Memory in Two Portal Fantasies An Analys.docxransayo
Zhou 1
Time and Memory in Two Portal Fantasies: An Analysis of Alice’s Adventure in Wonderland and "Windeye"
Life is a collection of moments, and some memories last forever. Brian Evenson
demonstrated this in “Windeye,”a story of a man who faces mental challenges because of the
life-long memory of his sister. In spite of the fact that his mother insists that the sister did not
exist, the protagonist stuck to this belief until his old age. The basis of the protagonist’s
problems is the intense love and unforgettable memories he shared with his imagined sister.
A great portion of his childhood memories is centered around his sister and their exploration
of the windeye. Windeye, the corruption of the word window, is a portal that causes the
disappearance of the protagonist’s sister. The popular portal fantasy, Alice’s Adventure in Wonderland, illustrates a similar story in the same sub-genre where a girl travels through a
rabbit hole and experiences a fantasy world which chronicles her changes from naive child-
like responses to more adult-like problem solving reactions. In “Windeye,” Brian Evenson
utilizes the portal trope to develop conflict and outcomes while exploring the themes of time
and memory. In both stories, the use of the portal trope creates a distinct world that is
separate from reality; however, the outcomes are different, and ultimately, Alice’s Adventure in Wonderland presents the theme of growth while “Windeye” explores time and memories.
The use of time factors allows the reader to travel back to the origin of the story in “Windeye” and experience the beginning of the central conflict. It is in his past that the
protagonist develops strong childhood memories of a sister, which is the cause of his future
mental challenges. In the present, the narrator is old and rickety as he uses a cane to walk but
is still reminiscent of the past (Evenson). He holds firm to the belief that he might have a
chance of meeting his sister again and thus contemplates the future and the sister’s
appearance. The plot of “Windeye” is composed of distinctive life moments: the past, the
present, and the future, which offer a clear and complete description of the events. The theme
Zhou 2
of time allows the reader to understand why the protagonist profoundly feels that his sister exists. In essence, it is time travel that gives the story a picture of the events that lead to the current situation.
The portal fantasy is a fictional literary device where a character enters into a
fantastical world through a portal or a hole. In Alice’s Adventures in Wonderland, Carroll
uses a rabbit hole as a physical portal to move through time. Comparably, Evenson utilizes
the windeye, a window that can only be seen from one side, as a physical portal. When the
sister touches the windeye, her brother believes that she enters into another reality through
the portal as Alice does. In contrast, the protagonist also experiences a new reality as he is.
Zhang 1
Yixiang Zhang
Tamara Kuzmenkov
English 101
June 2, 2020
Comparing Gas-Powered Cars and Electric Cars
Electric cars have become increasingly popular in the past century. These cars use
electric motors instead of conventional gasoline engines. Electric cars pollute less and utilize
energy more efficiently than gas-powered vehicles; therefore, modern research is focusing on
improving electric vehicles, such as increasing the storage capacity of the batteries. This essay
seeks to identify the differences and similarities between the two types of cars focusing on their
performance, price, and convenience.
An electric car is a car that is primarily powered by electricity. The conventional gas-
powered cars require diesel or gasoline to power the engines. These cars have gas tanks that store
fuel and the engine converts the gas to the energy that powers the motor. Similarly, electric cars
have batteries, or fuel cells that store and convert electricity to energy used to propel electric
motors (What Are Electric Cars?). Four components present in electric cars distinguish it from
the gas-powered cars (Alternative Fuels Data Center: How Do All-Electric Cars Work?). The
first is the charge port. Since electricity powers an electric car, there has to be a port to connect
to an external power source when charging the battery. The second is an electric traction motor
that propels the vehicle. The third is a traction battery pack. This battery serves the same purpose
as the gas tank; thus, it stocks electric power to propel the motor. The forth is a direct current
converter. This component converts the current to low voltage power that is needed to power the
electric engine.
Tamara Kuzmenkov
90000001730094
You need to watch the panapto session for this paper assignment and FOLLOW the instructions I give there. Your topic sentence must follow the patterns set forth by your thesis. So, this first paragraph must have a topic sentence about GAS POWERED cars and PRICE. That is what you have set forth in your thesis. Watch the panapto session. And ask me questions if you do not understand what I mean.
Tamara Kuzmenkov
90000001730094
No, you cannot 'announce' what your essay will do. And this is NOT the thesis I approved. What I approved:"Both gas-powered cars and electric cars are now in use, but their price, performance and convenience may vary, which may influence people's decisions about which type to use."
Zhang 2
Differences between gas-powered cars and electric cars
The initial purchase price of an electric car is much higher than that of a gas-powered car.
Consumers intending to own a vehicle have the option of buying or leasing. The initial cost of a
car depends on an individual's disposable income and savings. Knez et al. noted that "When it
comes to financial features, the most important thing seems to be the total price of the vehicle"
(55). The difference in price between electr.
Zhang �1
Nick Zhang
Mr. Bethea
Lyric Peotry
13 November 2018
Reputation by Taylor Swift
After Taylor Swift fell into disrepute, she was truly reborn. As a creative singer
who reveals a lot of real life emotions and details in her works, she constantly refines
and shares her emotional connection with her audience. In her new album, people find
resonance in her work, connect it with their own lives. "Reputation" is not only the
original efforts of Taylor Swift, but also means that she turned gorgeously and
dominated. This album is like a swearing word from her to the world. Revenge fantasy,
sweet love, painful growth... all the good and bad things that happened in these stages
of life, her music seems to have gone through with us all over again.
But last August, the now 28-year-old singer declared that "the old Taylor is
dead" in her eerie single "Look What You Made Me Do," the beginning of a new era for
Swift (Weatherby). The disclosure of the society, the accusations of rumor makers,
these straight-forward lyrics shred the ugly face of those unscrupulous people. Taylor
Swift did not endure the rumors in the society, but created this rock album after the
silence. If 1989 is still what Taylor hopes to gain the understanding of the public, this
album is really a matter of opening up the past concerns, saying goodbye to the past
as well as being a true Taylor Swift. No longer caring about the so-called "reputation ",
preferring to be burned to death by those ridiculous "images." This air of newfound
jadedness is one of the many ways in which Swift broadcasts her long-overdue loss of
Zhang �2
innocence on “Reputation,” an album that captures the singer during the most
turbulent but commercially successful period of her career. (Primeau)
The cover is black and white, the picture is Taylor's head, and the side is the
newspaper's article and title words. The cover of the album may be a metaphor, it
reveals that Taylor can no longer stand the report of the gossip media, and the chain on
the neck represents depression and breathlessness. The theme and style of the album
are all refined from their own lives. The emotions and themes interpreted in her songs
make the audience feel more deeply that her album is her life. Without even using any
real words, fans can surmise what this means — a reference to the endless headlines
and stories the singer has spurred in recent years. (Primeau) Reputation, come to diss
the past and all opponents.
The lyrics and MV are full of real stalks in Taylor Swift's life , with Taylor's
resentment for circles and industry since his debut. In the era of streaming singles, she
is the rare young star who still worships at the altar of the album, an old-fashioned
instinct that serves her surprisingly well. (Battan) "Look What You Made Me Do" is a
counterattack against Kanye West and Kim Kardashian, Katy Perry and numerous
online "black mold". And .
Zero trust is a security stance for networking based on not trusting.docxransayo
The document provides an assignment to research and write a report on the zero trust security model. The report should describe the purpose of zero trust and how it differs from other models, provide an overview of how zero trust works in a network environment, and explain how zero trust incorporates least privilege access through role-based access control and attribute-based access control. The report should be around 2 pages and 600 words.
Zero plagiarism4 referencesNature offers many examples of sp.docxransayo
Zero plagiarism
4 references
Nature offers many examples of specialization and collaboration. Ant colonies and bee hives are but two examples of nature’s sophisticated organizations. Each thrives because their members specialize by tasks, divide labor, and collaborate to ensure food, safety, and general well-being of the colony or hive.
In this Discussion, you will reflect on your own observations of and/or experiences with informaticist collaboration. You will also propose strategies for how these collaborative experiences might be improved.
Of course, humans don’t fare too badly in this regard either. And healthcare is a great example. As specialists in the collection, access, and application of data, nurse informaticists collaborate with specialists on a regular basis to ensure that appropriate data is available to make decisions and take actions to ensure the general well-being of patients.
Post
a description of experiences or observations about how nurse informaticists and/or data or technology specialists interact with other professionals within your healthcare organization. Suggest at least one strategy on how these interactions might be improved. Be specific and provide examples. Then, explain the impact you believe the continued evolution of nursing informatics as a specialty and/or the continued emergence of new technologies might have on professional interactions.
.
Zero plagiarism4 referencesLearning ObjectivesStudents w.docxransayo
Zero plagiarism
4 references
Learning Objectives
Students will:
Develop diagnoses for clients receiving psychotherapy*
Analyze legal and ethical implications of counseling clients with psychiatric disorders*
* The Assignment related to this Learning Objective is introduced this week and
submitted
in
Week 4
.
Select a client whom you observed or counseled this week. Then, address the following in your Practicum Journal:
Describe the client (without violating HIPAA regulations) and identify any pertinent history or medical information, including prescribed medications.
Using the
Diagnostic and Statistical Manual of Mental Health Disorders
, 5th edition (DSM-5), explain and justify your diagnosis for this client.
Explain any legal and/or ethical implications related to counseling this client.
Support your approach with evidence-based literature.
.
Zero Plagiarism or receive a grade of a 0.Choose one important p.docxransayo
Zero Plagiarism or receive a grade of a 0.
Choose one important police function: Law enforcement, order maintenance or service, etc.
OR
Choose one important police strategy: Traditional Policing, Community Policing, Data Driven Policing, etc.
Write a research paper describing the strateugy or function in detail and discussing the significance of the strategy or function with respect to the roles in society.
Format: Title Page, Outline, Text, and References
Must have 3 sources
You can use your textbook: Cox, Steven M., et al. (2020). Introduction to Policing. Fourth Edition. Thousand Oaks, CA: SAGE Publications, Inc.
Paper must by 6 pages long
APA Style
.
ZACHARY SHEMTOB AND DAVID LATZachary Shemtob, formerly editor in.docxransayo
ZACHARY SHEMTOB AND DAVID LAT
Zachary Shemtob, formerly editor in chief of the Georgetown Law Review, is a clerk in the US District Court for the Southern District of New York. David Lat is a former federal prosecutor. Their essay originally appeared in the New York Times in 2011.
Executions Should Be Televised
Earlier this month, Georgia conducted its third execution this year. This would have passed relatively unnoticed if not for a controversy surrounding its videotaping. Lawyers for the condemned inmate, Andrew Grant DeYoung, had persuaded a judge to allow the recording of his last moments as part of an effort to obtain evidence on whether lethal injection caused unnecessary suffering.
Though he argued for videotaping, one of Mr. DeYoung’s defense lawyers, Brian Kammer, spoke out against releasing the footage to the public. “It’s a horrible thing that Andrew DeYoung had to go through,” Mr. Kammer said, “and it’s not for the public to see that.”
We respectfully disagree. Executions in the United States ought to be made public.
Right now, executions are generally open only to the press and a few select witnesses. For the rest of us, the vague contours are provided in the morning paper. Yet a functioning democracy demands maximum accountability and transparency. As long as executions remain behind closed doors, those are impossible. The people should have the right to see what is being done in their name and with their tax dollars.
This is particularly relevant given the current debate on whether specific methods of lethal injection constitute cruel and unusual punishment and therefore violate the Constitution.
There is a dramatic difference between reading or hearing of such an event and observing it through image and sound. (This is obvious to those who saw the footage of Saddam Hussein’s hanging in 2006 or the death of Neda Agha-Soltan during the protests in Iran in 2009.) We are not calling for opening executions completely to the public — conducting them before a live crowd — but rather for broadcasting them live or recording them for future release, on the web or TV.
When another Georgia inmate, Roy Blankenship, was executed in June, the prisoner jerked his head, grimaced, gasped, and lurched, according to a medical expert’s affidavit. The Atlanta Journal-Constitution reported that Mr. DeYoung, executed in the same manner, “showed no violent signs in death.” Voters should not have to rely on media accounts to understand what takes place when a man is put to death.
Cameras record legislative sessions and presidential debates, and courtrooms are allowing greater television access. When he was an Illinois state senator, President Obama successfully pressed for the videotaping of homicide interrogations and confessions. The most serious penalty of all surely demands equal if not greater scrutiny.
Opponents of our proposal offer many objections. State lawyers argued that making Mr. DeYoung’s execution public raised safety concerns..
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The document is a reflective essay written by Jiawen Zeng about improving their writing skills during their English 3001 writing proficiency course over 10 weeks. The essay discusses the most serious problems Zeng previously faced with their writing, including issues with grammar, verb tenses, and content quality. It describes Zeng's initial strategy of only focusing on highlighted mistakes, but then realizing this was not enough and starting to read more books in English and write more diverse essays. The essay reflects on Zeng meeting the university's writing requirements being just the beginning, and the need to continue improving editing skills and focusing on content, evidence, and meeting further targets.
zClass 44.8.19§ Announcements§ Go over quiz #1.docxransayo
This document summarizes a lecture on the social organization of Hindustani music. It discusses key terms like gharana (musical lineage), khandan (musical family), and the distinction between soloists and accompanists. Socially, soloists came from higher castes than accompanists. Musically, the performance structure involved a soloist leading with accompanists following. Over time, accompanists gained more prominence and independence, filling important musical roles and occasionally challenging the traditional hierarchy. Lineage and pedigree (gharana/khandan) became important for musicians' social and musical identities.
zClass 185.13.19§ Announcements§ Review of last .docxransayo
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Class 18
5.13.19
§ Announcements
§ Review of last class
§ Finish lecture on Qawwali, begin intro to Pakistan
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Announcements
§ Keshav Batish senior recital, June 5 – Extra credit
§ Exam #1 results posted
§ 2 perfect scores, 25 A’s, 46 B’s, 37 C’s, 17 D and lower
§ Summer course on Indian rhythm (second session)
§ Learn tabla and dholak!
§ Enrollment open now!
z
Last class review
§ Qawwali – “Food for the soul”
§ Sufi devotional poetry set to music
§ Performed at dargah
§ ‘Urs
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Terms
§ Mehfil – small, intimate gatherings that involve entertainment of
various sorts, including music, poetry, dance etc.
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Tum Ek Gorakh Dhandha Ho
§ “You are a baffling puzzle”
§ Written by Naz Khialvi (1947-2010)
§ Pakistani lyricist and radio broadcaster
§ Popularized by Ustad Nusrat Fateh Ali Khan (1948-1997)
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Tum Ek Gorakh Dhandha Ho
kabhi yahaan tumhein dhoonda
kabhi wahaan pohancha
tumhaari deed ki khaatir kahaan
kahaan pohancha
ghareeb mit gaye paamaal ho
gaye lekin
kisi talak na tera aaj tak nishaan
pohancha
ho bhi naheen aur har ja ho
tum ik gorakh dhanda ho
At times I searched for you here,
at times I traveled there
For the sake of seeing You, how
far I have come!
Similar wanderers wiped away
and ruined, but
Your sign has still not reached
anyone
You are not, yet You are
everywhere
You are a baffling puzzle
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Bhar Do Jholi Meri
§ Traditional song
§ Popularized in movie “Bajrangi Bhaijaan” (2015)
z
Bhar Do Jholi Meri
Tere Darbaar Mein
Dil Thaam Ke Woh Aata Hai
Jisko Tu Chaahe
Hey Nabi Tu Bhulata Hai
Tere Dar Pe Sar Jhukaaye
Main Bhi Aaya Hoon
Jiski Bigdi Haye
Nabi Chaahe Tu Banata Hai
Bhar Do Jholi Meri Ya Mohammad
Lautkar Main Naa Jaunga Khaali
They come into Your court
clenching their hearts
Those people whom You desire to
see , O Prophet!
I’ve also come to Your door with
my head bowed down
You’re the One who can fix
broken fates, O Prophet!
Please fill my lap, O Prophet!
I won’t go back empty handed
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Ustad Nusrat Fateh Ali Khan
(1948-1997)
§ Pakistani vocalist
§ Sang classical (khyāl) but more famous as a Qawwali singer
§ Brought classical performance techniques to Qawwali
§ Visiting artist at University of Washington from 1992-93
§ Legacy carried on through his nephew, Rahat Fateh Ali Khan
z
Introduction to Pakistan
Badshahi Mosque, Lahore
Built in 1671 by Emperor Aurangzeb
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Pakistan
§ Prominent Bronze Age (3000-1500BCE) settlements of Mohenjo
Daro and Harrapa along Indus River Valley
§ Hinduism widespread during Vedic Age (1500-500BCE)
§ Ruled by series of Hindu, Buddhist, and eventually Muslim
(Persian) dynasties
§ Islam introduced by Sufi missionaries from 7th to 13th centuries
§ Ethnically and linguistically diverse
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Indus Valley civilization
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Pakistan ethnicities
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Modern India and Pakistan
§ By the end of 19th century British rule was in effect over much of
old Mughal Empire territory
§ The Hindu and Muslim divide among this territory was be.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Information and Communication Technology in EducationMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 2)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐈𝐂𝐓 𝐢𝐧 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧:
Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
How to Setup Default Value for a Field in Odoo 17Celine George
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
CHUYÊN ĐỀ ÔN TẬP VÀ PHÁT TRIỂN CÂU HỎI TRONG ĐỀ MINH HỌA THI TỐT NGHIỆP THPT ...
A Method for the Analysis of BehaviouralUncertainty in Evacu.docx
1. A Method for the Analysis of Behavioural
Uncertainty in Evacuation Modelling
Enrico Ronchi*, Department of Fire Safety Engineering and
Systems Safety,
Lund University, P.O. Box 118, 22100 Lund, Sweden
Paul A. Reneke and Richard D. Peacock, National Institute of
Standards and
Technology, Gaithersburg, MD 20899, USA
Received: 4 April 2013/Accepted: 22 June 2013
Abstract. Evacuation models generally include the use of
distributions or probabilis-
tic variables to simulate the variability of possible human
behaviours. A single model
setup of the same evacuation scenario may therefore produce a
distribution of differ-
ent occupant-evacuation time curves in the case of the use of a
random sampling
method. This creates an additional component of uncertainty
caused by the impact of
the number of simulated runs of the same scenario on
evacuation model predictions,
here named behavioural uncertainty. To date there is no
universally accepted quanti-
tative method to evaluate behavioural uncertainty and the
selection of the number of
2. runs is left to a qualitative judgement of the model user. A
simple quantitative
method using convergence criteria based on functional analysis
is presented to
address this issue. The method permits (1) the analysis of the
variability of model
predictions in relation to the number of runs of the same
evacuation scenario, i.e. the
study of behavioural uncertainty and (2) the identification of the
optimal number of
runs of the same scenario in relation to pre-defined acceptance
criteria.
Keywords: Evacuation modelling, Behavioural uncertainty,
Human behaviour in fire,
Functional analysis, Convergence criteria
1. Introduction
Uncertainty is divided into different components in the context
of fire safety engi-
neering and modelling [1]: model input uncertainty,
measurement uncertainty, and
intrinsic uncertainty.
(1) Model input uncertainty is associated with the parameters
obtained from
experimental measurements that are used as model input, i.e.
the assumptions
employed to derive model input from the experiments.
(2) Measurement uncertainty is associated with the experimental
measurement
itself, i.e., the data collection techniques employed.
3. (3) Intrinsic uncertainty is the uncertainty associated with the
physical and mathe-
matical assumptions and methods that are intrinsic to the model
formulation.
* Correspondence should be addressed to: Enrico Ronchi, E-
mail: [email protected]
Fire Technology, 50, 1545–1571, 2014
� 2013 Springer Science+Business Media New York.
Manufactured in The United States
DOI: 10.1007/s10694-013-0352-7
12
In the case of evacuation data, uncertainty includes an
additional component,
here named behavioural uncertainty. Behavioural uncertainty is
uncertainty asso-
ciated with the stochastic nature of human behaviour, i.e.
human behaviour is sto-
chastic per se [2], and a single experiment or model run may not
be representative
of a full range of the behaviours of the occupants. In fact,
‘‘evacuate the same
building with the same people starting in the same places on
consecutive days and
the answers could vary significantly’’ [2]. There is a subsequent
need for multiple
experimental data-sets to understand the possible variability of
occupant behav-
iours in each individual evacuation scenario [3]. Unfortunately,
experimental data-
4. sets on human behaviour in fire are scarce and single data-sets
are often the only
available reference for the study of an individual scenario.
Behavioural uncer-
tainty needs to be analysed in both experimental and modelling
studies. In this
context, the assessment of the variability of simulation results
in relation to
behavioural uncertainty is a key issue to be discussed. This is
reflected in the esti-
mation of the convergence of an individual evacuation
simulation scenario
towards an ‘‘average’’ predicted occupant evacuation time-
curve. It should be
noted that the term behavioural uncertainty is here introduced in
the context of
fire safety science, i.e. the term may have different meanings in
other research
fields.
Fire modellers and evacuation modellers treat uncertainty in
different ways.
Uncertainty is generally treated in fire models as a deterministic
problem, i.e., it is
studied by analysing the sensitivity of the model output in
relation to the variabil-
ity of the model input. This is driven by the fact that fire
models are generally
based on deterministic equations (e.g. [4, 5]). On the other
hand, evacuation mod-
els treat uncertainty as a stochastic problem. In fact, to address
the stochastic nat-
ure of human behaviour, evacuation models often employ
distributions or
stochastic variables to simulate people movement and
behaviours [6–10] (e.g. dis-
5. tribution of walking speeds, distribution of pre-evacuation
times, exit choice, etc.).
In fact, random numbers/seeds may be employed to solve space
conflict resolu-
tion, simulate exit choice, familiarity with the exit, queuing
behaviour, etc. When
distributions are created adopting a random sampling method,
multiple occupant-
evacuation time curves for the same scenario using the same
model inputs are
produced. Random variables may be intrinsic of the model
algorithms, and model
users may not have control/access to them (especially in closed-
source models).
This leads to the need for a study of the variability of the
results associated with
the random variables embedded in the models.
Therefore, evacuation modellers face the problem of selecting
the appropriate
number of runs to be simulated in order to be representative of
the average
model outcome. This problem arises both during the use of
evacuation models
for a fire safety design as well as during validation studies. In
fact, two main
questions can be asked during the simulation of evacuation
scenarios that
include distributions or stochastic variables: (1) Which
occupant-evacuation time
curve is representative of model predictions in a fire safety
design? (2) Which
occupant-evacuation time curve should be used as reference
during the compari-
son with experimental data in a validation study? To date, the
answers to these
6. questions are left to a qualitative judgment by the evacuation
model user. For
1546 Fire Technology 2014
instance, in the context of evacuation model validation, model
users may select
the best model prediction during the comparison with
experimental data [11] or
employ the model’s average total evacuation time (TET)
[possibly including
information on the standard deviation (SD)] as representative of
model predic-
tions. The study of the average TETs and their corresponding
SDs provides
insights only on the required safe escape time, rather than the
whole evacuation
process. There is instead a need for a method which investigates
the size of the
variation for the whole occupant-evacuation time curve.
Nevertheless, to date,
there is no universally accepted quantitative method to estimate
how these aver-
age predictions may vary over the number of runs.
In addition, complex evacuation scenarios may be
computationally expensive
to simulate. For instance, previous research on the use of
distribution curves for
Monte Carlo simulations for uncertainty analysis in evacuation
model predic-
tions have demonstrated the need for a large computational
effort [7]. Therefore
there is a need to optimize the selection of the number of runs
7. of the same sce-
nario in order to be representative of occupants’ ‘‘average
behaviour’’, and pro-
vide a quantitative and computationally inexpensive
measurement of the
variability associated with the simulated runs (and a subsequent
estimation of
the behavioural uncertainty associated with an individual
evacuation model
setup).
A useful method for the analysis of model predictions is
functional analysis.
This branch of mathematics represents curves as vectors, and
uses geometrical
operations on the curves. Functional analysis operations are
currently employed
during the comparison of fire model evaluations and
experimental data [12, 13]
and the comparison between evacuation model results and
experimental data [14].
Nevertheless, functional analysis has not been employed so far
to compare evacu-
ation model predictions against each other to analyse the
uncertainty associated
with the number of runs of the same evacuation scenario, i.e.
behavioural uncer-
tainty.
This paper proposes a set of convergence criteria for the
analysis of the vari-
ability of evacuation model predictions of the same evacuation
scenario (i.e. the
same model input which includes distributions or stochastic
variables) in relation
to the number of runs. A procedure for the definition of the
8. optimal number of
runs—in relation to the evacuation scenario, the model in use,
and the scope of
the simulations—is presented. The scope of the present work is
therefore to pro-
vide a quantitative method to assess the variability associated
with the number of
runs of the same evacuation scenario. The proposed method
allows the analysis of
behavioural uncertainty and the prediction of the average
occupant-evacuation
time curve in relation to pre-defined acceptance criteria.
A case study about the application of the method is presented.
The case study
is an explanatory example in which a fictitious data-set (i.e. a
data-set created
using a pseudo-random generator) is employed to show the
convergence criteria
and the evaluation procedure.
The last part of the paper discusses the benefits associated with
the use of the
convergence criteria and future work regarding their possible
uses.
Analysis of Behavioural Uncertainty 1547
2. Method
This section presents a proposed methodology for the analysis
of behavioural
uncertainty. It includes the definition of five convergence
criteria for the analysis
9. of the occupant-evacuation time curves produced by evacuation
models and a
procedure for the assessment of the optimal number of runs in
relation to pre-
defined acceptance criteria.
The proposed methodology is based on the definition of a set of
convergence
measures that sufficiently describe the distribution of occupant-
evacuation time
curves. This is addressed by constructing a series for each
measure and demon-
strating that the measure is sufficiently close to the expected
value, i.e. the series
converge to the average occupant-evacuation time curve.
A series S = {si,…, sn} converges to Sc if for any positive real
value e there is
an n such that Sc � snj j< e.
The series represents the evacuation time predictions of
evacuation models and
they are based on sample data. This will imply that the series
will likely not
smoothly converge, meaning that it might happen that Sc �
snþ1j j> Sc � snj j. In
order to increase the confidence that our series have sufficiently
converged, a
requirement that the last b values of the series (the convergence
measures) are
within Sc is added. For some series we might not know the
expected value Sc, i.e.,
the value to which the series is convergent. In those cases the
last current value of
the series is used as the best estimate of the value the series
converges to.
10. 2.1. Functional Analysis Concepts
Before discussion of convergence criteria, there is a need to
introduce three concepts
of functional analysis, namely the Euclidean Relative
Difference (ERD), the Euclid-
ean Projection Coefficient (EPC) and the Secant Cosine (SC).
Initial applications of
these concepts have been used in different research fields (e.g.,
mechanics [15], engi-
neering [16], etc.), including fire science (see Peacock et al.
[12] and Galea et al. [14]).
The single comparison of two individual points in a curve can
be made by find-
ing the norm of the difference between the two vectors
representing the data. A
norm represents the length of a vector. The distance between
two vectors corre-
sponds to the length of the vector resulting from the difference
of the two vectors.
For a generic vector x
*
; the norm is represented using the symbol jj x
*
jj: This con-
cept can be extended to multiple dimensions. The distance
between two generic
multi-dimensional vectors x
*
and y
11. *
is therefore the norm of the difference of the
vectors jj x
*
� y
*
jj. The ERD between two vectors can be normalized as a
relative
difference to the vector y
*
(see Equation 1).
ERD ¼
jj x
*
� y
*
jj
jj y
*
jj
¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffiffiffi
Pn
i¼1ðxi � yiÞ
12. 2
Pn
i¼1ðyiÞ
2
s
ð1Þ
The ERD represents, therefore, the overall agreement between
two curves.
Two components can be considered during the comparison of
two vectors,
namely the distance between two vectors and the angle between
the vectors.
1548 Fire Technology 2014
The concept of projection coefficient a is introduced. From a
geometric point of
view, the vector a x
*
is the projection of the vector y
*
onto the vector x
*
(see Figure 1).
13. a defines a factor which reduces the distance between two
vectors to its mini-
mum (see Figure 1). The solution of the minimum problem is
found and corre-
sponds to Equation 2.
a ¼
jj y
*
jj
jj x
*
jj
cos b ð2Þ
hx
*
; y
*
i is the inner product of two vectors, i.e., the product of the
length of the
two vectors and the cosine of the angle between them. The inner
product can be
interpreted as the standard dot product; producing Equation 3.
hx
*
; y
*
i¼
X
n
14. i¼1
ðxiyiÞ ð3Þ
The EPC is found by studying the minimum problem, i.e.,
studying when the
derivative of the function is zero (see Peacock et al. [12] for the
full solution of
the minimum) and it corresponds to:
a ¼ EPC ¼
hx
*
; y
*
i
jj y
*
jj2
¼
Pn
i¼1ðxiyiÞ
Pn
i¼1 y
2
i
ð4Þ
EPC defines a factor which when multiplied by each data point
of the vector y
*
15. reduces the distance between the vectors y
*
and x
*
to its minimum, i.e. the best
possible fit of the two curves.
The concept of SC is also introduced. It represents a measure of
the differences
of the shapes of two curves. This is investigated by analysing
the first derivative of
both curves.
For n data points, a multi-dimensional set of n - 1 vectors can
be defined to
approximate the derivative. This produces Equation 5 [12]:
SC ¼
hx
*
; y
*
i
x
*
�
�
�
�
18. Where:
t is the measure of the spacing of the data, i.e. t = 1 if there is a
data point for
each occupant;
s represents the number of data points in the interval;
n is the number of data points in the data-set;
Dxi�s ¼ xi � xi�s;
Dyi�s ¼ yi � yi�s;
Dti�1 ¼ ti � ti�1:
When the SC is equal to unity, the shapes of the two curves are
identical.
Depending on the value for s, the noise of the data is smoothed
out. An example
of the impact of different values of s on the SC is shown in
Figures 2 and 3. Fig-
ure 2 shows two hypothetical curves (obtained by 120 values for
x and y corre-
sponding to 120 arbitrary data-points) which include noise or no
noise. The
comparison between the shapes of the two curves is made using
different s values
(from s = 1 to s = 60 in this example), i.e., Figure 3 shows that
the use of higher
values for s reduces the impact of the noise in the comparison.
Nevertheless, s should not be too large, so that the natural
variations in the data
are kept. An example of this issue is provided in Figure 4,
where, considering a hypo-
thetical set of 4 data-points, different values for s generate
either SC = 1 for s = 4
(the shape of the curves appear identical) or SC „ 1 in the case
of s = 1 and s = 2.
19. 2.2. Convergence Measures
A set of variables are introduced in order to present the method
of analysis of
evacuation model predictions based on functional analysis and
convergence
criteria.
0
200
400
600
800
1000
1200
1400
1600
1800
0 20 40 60 80 100 120
A
rb
it
ra
20. ry
u
n
it
Arbitrary unit
Curve 1 (no noise) Curve 2 (including noise)
Figure 2. Hypothetical curves including noise (grey curve) and
not
including noise (black curve).
1550 Fire Technology 2014
The measured experimental data are represented using vector E
*
(see Equa-
tion 6), where Ei represents the measured evacuation time for
the ith occupant.
E
*
¼ E1; . . . ; Enð Þ ð6Þ
For example, in the case of i = 3 occupants, i.e., E
*
¼ E1; E2; E3ð Þ; E1 is the mea-
sured evacuation time corresponding to occupant 1, E2 is the
21. measured evacuation
time corresponding to occupant 2 and E3 is the measured
evacuation time corre-
sponding to occupant 3.
The simulated predicted times are represented by the vector m
*
(see Equation 7),
where mi is the simulated evacuation time for the ith occupant.
mn represents the
evacuation time corresponding to the last occupant out of the
building
m
*
¼ m1; . . . ; mnð Þ ð7Þ
Therefore, m
*
¼ m1; m2; m3ð Þ, where m1 is the simulated evacuation time
corre-
sponding to occupant 1, m2 is the simulated evacuation time
corresponding to
occupant 2 and m3 is the simulated evacuation time
corresponding to occupant 3.
0.4
0.5
0.6
0.7
22. 0.8
0.9
1.0
0 10 20 30 40 50 60
S
C
s
SC
Figure 3. SC in relation to different s values.
Figure 4. Schematic representation of the use of different values
for
s during the calculation of the SC.
Analysis of Behavioural Uncertainty 1551
Several runs of the same scenarios are simulated. The simulated
evacuation
times of each occupant i in each jth run are represented using n
vectors m
*
ij (see
Equation 8). Here, q is the total number of occupants and n is
the total number
of runs. One assumption is that occupants are ranked in
23. accordance to their evac-
uation time, i.e. occupants may evacuate the building in a
different order in differ-
ent runs.
m
*
ij ¼ m11; . . . ; mij; . . . ; mqn
� �
ð8Þ
Considering nine runs of the same evacuation scenario including
the same three
occupants, 9 vectors m
*
ij are obtained where i = 3 and j = 9, i.e., m
*
i1 ¼ m11;ð
m21; m31Þ; m
*
i2 ¼ m12; m22; m32ð Þ; . . . ; m
*
i9 ¼ m19; m29; m39ð Þ.
The next variable that is presented is associated with the
calculation of the
arithmetic mean of the values of the runs. The jth average curve
of evacuation
times produced by the model considering the arithmetic mean of
24. the values of all
runs is represented using an n dimensional vector M
*
j (see Equation 9), where
M1 ¼ 1n
Pn
j¼1 m1j;M2 ¼
1
n
Pn
j¼1 m2j; . . . ;Mn ¼
1
n
Pn
j¼1 mqj.
M
*
j ¼ M1; . . . ;Mj; . . . ;Mn
� �
ð9Þ
Considering the previous example, i.e. 3 occupants and 9 runs (i
= 3 and j = 9),
the average curve M
*
25. 1 corresponds to the values of the first run. The average curve
for a sub-set of 4 runs will generate M
*
4 which corresponds to the arithmetic
means of the values up to the fourth run. In the case of all 9
runs, M
*
9 corre-
sponds to the arithmetic means of the values of all runs.
Figure 5 presents vector M
*
j in relation to the number of runs under consider-
ation.
Hence if j ¼ 1;M
*
j ¼ðM1Þ, i.e. the average curve corresponds to the curve of
the first run. If 1 < j < n, M
*
j becomes M
*
j ¼ðM1; . . . ;MjÞ where
M1 ¼ 1j
P1 < j < n
26. j¼1 m1j; M2 ¼
1
j
P1 < j < n
j¼1 m2j; . . . ; Mj ¼
1
j
P1 < j < n
j¼1 mqj:M
*
j represents
then the average curve corresponding to 1 < j < n runs.
Considering 4 vectors m
*
ij
corresponding to the predicted evacuation times for three
occupants in j = 4 runs
out of n = 9 runs, M
*
4 ¼ M1 ¼ 14
P1 < 4 < 9
j¼1 m1j; M2 ¼
1
j
27. P1 < 4 < 9
j¼1 m2j;M3 ¼
�
1
4
P1 < 4 < 9
j¼1 m3jÞ: If j = n, M
*
j becomes M
*
n ¼ðM1; . . . ;MnÞ where M1 ¼ 1n
Pn
j¼1 m1j;
M2 ¼ 1n
Pn
j¼1 m2j; . . . ; Mj ¼
1
n
Pn
j¼1 mqj: Thus, M
*
j represents the average curve cor-
28. responding to all j = n runs. For instance, if n = 9 runs, M
*
9 ¼ðM1 ¼
1
9
P9
j¼1 m1j; M2 ¼
1
9
P9
j¼1 m2jM3 ¼
1
9
P9
j¼1 m3jÞ.
2.2.1. Convergence Measure 1: TET. The vector mn can also be
called TETj, TET
(also called Required Safe Egress Time in the context of
performance based
1552 Fire Technology 2014
design [17]), corresponding to the jth run. Therefore, there are
several simulated
TETj, each one corresponding to the jth run for a total of n
runs.
29. The jth TETs TETi for n runs of the same scenario simulated
with an evacua-
tion model can be represented using the vector TET
���*
¼ TET1; . . . ; TETnð Þ.
The arithmetic mean of the TETs for j runs can be expressed
using TETavj (see
Equation 10):
TETavj ¼
1
j
X
j
i¼1
TETi ð10Þ
The set of all n consecutive mean TETs TETavj of the same
scenario simulated
with an evacuation model is TETav = (TETav1,…, TETavn).
TETav1 is assumed to
correspond to the value in run 1, TETav2 is the average for j =
2,…, TETavn the
average for j = n.
Applying the law of large numbers, the consecutive mean TETs
TETavi can be
interpreted as a series converging to an expected value (the
mean TET). Hence, a
30. measure of the convergence of the series can be performed.
A measure of the convergence of two consecutive mean TETs
TETavj (e.g. TETav1
and TETav2) is obtained calculating TETconvj (see Equation
11). It is expressed
(in %) as the difference of two consecutive mean TETs divided
by the last mean
evacuation time. This convergence measure assumes that the
best approximation of
the expected value (the mean TET) is the last mean evacuation
time.
This produces a total of p = n-1 TETconvj.
TETconvj ¼ j
TETavj � TETavj�1
TETavj
j ð11Þ
Figure 5. Vector M
*
j in relation to the considered number of runs.
Analysis of Behavioural Uncertainty 1553
The last TETconvj value, corresponding to all n runs is
TETconvFIN (see Equa-
tion 12).
TETconvFIN ¼ j
TETavp � TETavp�1
31. TETavp
j ð12Þ
2.2.2. Convergence Measure 2: SD of TETs. Convergence
variables can also be
presented in terms of the SD of TETs.
The jth SD SDj for n runs of the TET of the same scenario
simulated with an
evacuation model can be represented by the vector SD
*
¼ SD1; . . . ; SDnð Þ.
Also in this case, the application of the law of large numbers
permits the inter-
pretation of the consecutive SDs of TETs SDj as a series
convergent to an expec-
ted value (the mean SDs of TETs). Therefore, a measure of the
convergence of
the series is possible.
A measure of the convergence of two consecutive SDs SDj (e.g.
SD1 and SD2)
is obtained by calculating SDconvj. It is expressed (in %) as the
difference of two
consecutive SDs divided by the last SD (see Equation 13). This
produces a total
of p = n-1 SDconvj. This convergence measure assumes that the
best approxima-
tion of the expected value (the mean SD of TETs) is the last SD
of TETs.
SDconvj ¼ j
32. SDj � SDavj�1
SDj
j ð13Þ
The last SDconvj value, corresponding to all n runs, is
SDconvFIN (see Equation 14).
SDconvFIN ¼ j
SDavp � SDavp�1
SDavp
j ð14Þ
2.2.3. Convergence Measure 3: ERD. A set of ERD can be
calculated, each one
corresponding to two consecutive pairs of vectors M
*
j representing the progressive
average occupant-evacuation time curves.
A vector ERD
���*
¼ ERD1; . . . ; ERDp
� �
is made of p consecutive ERDj where
p = j - 1, corresponding to average j runs of the same scenario
simulated with
an evacuation model. For instance, in the case of j = 4 runs,
ERD
���*
33. ¼ ERD1; ERD2; ERD3ð Þ where ERD1 is calculated from the
comparison
between M1 and M2, ERD2 is calculated from the comparison
between M2 and
M3 and ERD3 is calculated from the comparison between M3
and M4. M1 repre-
sents the curve from run 1, M2 represents the average curve
generated by the
arithmetic means of the individual occupant evacuation times
for run 1 and run 2,
M3 represents the average curve generated by the arithmetic
means of the individ-
ual occupant evacuation times for run 1, run 2 and run 3. M4
represents the
1554 Fire Technology 2014
average curve generated by the arithmetic means of the
individual occupant evac-
uation times for run 1, run 2, run 3 and run 4.
The consecutive ERDj can be interpreted as a series convergent
to the expected
value equal to 0 (the case of two curves identical in magnitude).
Hence, a measure
of the convergence of the series is possible. A measure of the
convergence of two
consecutive ERDs ERDj corresponding to two consecutive
average curves M
*
j can
34. be obtained calculating ERDconvj (see Equation 15). It is
expressed as the absolute
value of the difference of two consecutive ERDs ERDj and
ERDj-1.
ERDconvj ¼ jERDj � ERDj�1j ð15Þ
The last ERDconvj value, corresponding to the differences
between the latest aver-
age curves is ERDconvFIN (see Equation 16).
ERDconvFIN ¼ jERDp � ERDp�1j ð16Þ
Calculation of ERDconvj permits estimation of the impact of
the number of runs
on the overall differences between consecutive average curves.
ERDconvFIN repre-
sents therefore a tool to understand the behavioural uncertainty
associated with
multiple runs of an individual evacuation scenario.
2.2.4. Convergence Measure 4: EPC. The same type of
convergence measures can
be produced for the EPC.
The consecutive EPCj can be interpreted as a series convergent
to the expected
value equal to 1 (the best possible agreement between two
consecutive EPCj).
Hence, a measure of the convergence of the series can be
performed. This results
in Equations 17 and 18.
EPCconvj ¼ jEPCj � EPCj�1j ð17Þ
EPCconvFIN ¼ jEPCp � EPCp�1j ð18Þ
35. ERDconvj permits the estimation of the impact of the number of
runs on the possi-
ble agreement between two consecutive average curves.
ERDconvFIN is therefore
another indicator of the behavioural uncertainty associated with
multiple runs of
an individual evacuation scenario.
2.2.5. Convergence Measure 5: SC. Convergence measures can
be developed for
the SC. The consecutive SCj can be interpreted as a series
convergent to the
expected value equal to 1 (the case of two identical shapes of
consecutive curves).
Hence, a measure of the convergence of the series can be
performed and it is pre-
sented in Equations 19 and 20.
SCconvj ¼ jSCj � SCj�1j ð19Þ
Analysis of Behavioural Uncertainty 1555
SCconvFIN ¼ jSCp � SCp�1j ð20Þ
SCconvj allows understanding of the impact of the number of
runs on the possible
differences between the shapes of two consecutive average
curves. SCconvFIN repre-
sents therefore a variable to understand the behavioural
uncertainty associated
with the average shape of the simulated curves, given a certain
number of runs n
of the same evacuation scenario.
36. 2.3. The Evaluation Method
Five variables have been presented in the previous section,
namely TETconvFIN,
SDconvFIN, ERDconvFIN, EPCconvFIN, and SCconvFIN.
Those variables represent the
basis for a novel evaluation method. The proposed method
addresses two key
aspects of evacuation modelling:
(1) The analysis of behavioural uncertainty of an individual
evacuation scenario.
(2) The identification of the optimal number of runs to produce
a stable evacua-
tion curve of the same scenario in relation to the evacuation
scenario and the
model in use.
An iterative method is suggested for the evaluation of
evacuation model results.
The method is based on five steps (see Figure 6).
[1] Define the acceptance criteria
TRTET, TRSD, TRERD, TREPC, TRSC.
CONSIDERATIONS
Depending on the evacuation
scenarios, model in use, etc.
The users also needs to define how
many consecutive runs are needed
to satisfy the conditions.
37. [2] Simulate a finite set of n runs of
the same evacuation scenario
END
CONSIDERATIONS
The initial number of simulations is
an arbitrary number set by the model
user
[4] Compare the convergence units
with the acceptance criteria
YES
NO
[5] Simulate a set of additional runs m
so that the new set of runs for the
comparison is S=n+m
[3] Calculate the convergence units
TETconvj , Sdconvj , ERDconvj ,
EPCconvj , and SCconvj
[4bis] Are all conditions satisfied?
TETconvj < TRTET for b consecutive runs
SDconvj < TRSD for b consecutive runs
ERDconvj < TRERD for b consecutive runs
EPCconvj < TREPC for b consecutive runs
SCconvj < TRSC for b consecutive runs
38. Figure 6. Schematic flow chart of the proposed evaluation
method.
1556 Fire Technology 2014
Step 1. Define the acceptance criteria [see (1) in Figure 6].
The first step of the method consists of the identification of the
acceptable
thresholds to be achieved, i.e. the accepted behavioural
uncertainty associated
with the average curve obtained by multiple runs of the same
scenario. The aim
is to obtain an evacuation curve that is sufficiently stable given
the scope of the
analysis. For example, in the case of the use of evacuation
modelling in the con-
text of performance based design, the identification of these
acceptable thresh-
olds can be based on the estimated uncertainty during the
calculation of the
available safe escape time produced using a fire model. This
approach permits a
joint analysis of the uncertainty associated with both the fire
and evacuation sim-
ulations. Five thresholds (corresponding to the five convergence
measures) are
identified, namely TRTET, TRSD, TRERD, TREPC, TRSC. It
should be noted that
there is an additional acceptance criteria that needs to be
assessed, i.e., a finite
number of consecutive runs b for which the acceptable
thresholds must not be
39. crossed. This needs to be assessed in order to verify that the
convergence mea-
sures are stable under certain thresholds over a pre-defined
number of runs. This
requirement is based on the assumptions described in Section 2.
The larger is b,
the higher is the confidence that can be put on the fulfilment of
the acceptance
criteria.
The identification of the acceptance criteria may depend on
several factors
such as the evacuation scenario, the model in use, etc. The
selection of the accep-
tance criteria—which may or may not include all convergence
measures—may be
identified by the evacuation modeller itself or from a third
party.
Step 2. Simulate a finite set of n runs of the same evacuation
scenario [see (2) in
Figure 6].
Evacuation model users select an arbitrary initial number of
simulations of an
individual evacuation scenario, i.e., the same model input is
used. n vectors
m
*
ij ¼ m11; . . . ; mij; . . . ; mqn
� �
corresponding to the simulated evacuation times of
each occupant ith in each jth run are obtained. The occupant-
evacuation time
40. curves are produced ranking the occupants in relation to their
evacuation time.
The vector corresponding to the consecutive average curves M
*
¼ðM1; . . . ;MnÞ
is also generated.
In order to optimize the iterative process, the selection of the
initial arbitrary
number of runs may be based on a qualitative evaluation made
by the evacua-
tion modeller of the variability of the predicted outcome given
the model input
of the scenario under consideration. Nevertheless, this
judgment—which is the
current qualitative method adopted by evacuation modellers to
estimate the opti-
mal number of runs—is not mandatory, since the proposed
method permits a
quantitative study of the impact of the number of runs on the
occupant-evacua-
tion time curve produced by the model.
Step 3. Calculate the convergence measures [see (3) in Figure
6].
The convergence measures presented in the previous sections
are calculated for
all runs, i.e., TETconvj, SDconvj, ERDconvj, EPCconvj, and
SCconvj.
In order to perform the calculation of the SCs for all runs,
model users need
Analysis of Behavioural Uncertainty 1557
41. also to identify a finite set of values for s, needed for the
calculation of SCconvj.
As described in Section 2.1, the choice of the values for s relies
on the dataset
under consideration. SCconvj are calculated for all runs for as
many s values as
chosen by the model user.
Step 4-4bis. Compare the convergence measures with the
acceptance criteria [see
(4-4bis) in Figure 6].
The model user compares the calculated convergence measures
against the
acceptable thresholds defined during Step 1. This produces five
tests that need to
be accomplished:
Test 1:
TETconvj < TRTET for b consecutive number of runs ð21Þ
Test 2:
SDconvj < TRSD for b consecutive number of runs ð22Þ
Test 3:
ERDconvj < TRERD for b consecutive number of runs ð23Þ
Test 4:
EPCconvj < TREPC for b consecutive number of runs ð24Þ
42. Test 5:
SCconvj < TRSC for b consecutive number of runs ð25Þ
It should be noted that the criteria need to be satisfied for a pre-
defined finite
number of consecutive b runs (as defined during Step 1). The
values correspond-
ing to the jth run where the conditions are verified for b
consecutive runs repre-
sent TETconvFIN, SDconvFIN, ERDconvFIN, EPCconvFIN,
and SCconvFIN. If the five
conditions are all satisfied for a pre-defined number of
consecutive runs, the
curves generated by n number of runs meet the acceptance
criteria, i.e. the average
curve is estimated given an accepted behavioural uncertainty
associated with the
number of runs (based on the acceptance criteria). If one or
more of the condi-
tions are not satisfied, the model user needs to proceed with
Step 5.
Step 5. Simulate a set of additional simulations m so that the
new set of runs for
the comparison is S = n + m [see (5) in Figure 6].
1558 Fire Technology 2014
The model user sets an arbitrary number of additional
simulations to be run.
The definition of the additional runs can be set in accordance
with a qualitative
analysis of the failed tests. A new set of S ¼ n þ m S
43. *
ij vectors S
*
ij ¼
S11; . . . ; Sij; . . . ; SqS
� �
corresponding to the average simulated evacuation times of
each occupant ith in each jth run are obtained. The same
methodology of Step 2
is adopted to produce the occupant-evacuation time curves, i.e.,
the occupants
are ranked in relation to their evacuation time. The model user
can now re-start
the procedure starting from Step 3.
3. Case Study
An application of the method presented in Section 2 is
described to provide an
example of the concepts. Given the explanatory scope of the
example, data used in
this section are fictitious, i.e., they do not correspond to real
data. This choice has
been driven by the current lack of repeated experimental data,
i.e. the method has
been applied to study simulation results. Data are created in
order to be representa-
tive of the results obtained with an evacuation model for a
hypothetical evacuation
scenario. A fictitious set of numbers is produced using
Wichman and Hill’s [18]
pseudo-random generator. The pseudo-random numbers are used
44. as input to pro-
duce lognormal-distributed values. This choice was made in
order to be representa-
tive of a hypothetical evacuation scenario which is influenced
by pre-evacuation
times (which generally follow a log-normal distribution [17]).
The fictitious data are
then used to create fictitious individual evacuation times
calculated by progressively
summing the values obtained (in order to be representative of a
hypothetical real
case study where total evacuation times range approximately
between 1100 s and
1900 s). For example, if the first pseudo-random generated
number is 12.41 and the
second pseudo-random generated number is 18.18 s, the
evacuation time of the first
occupant out would correspond to 12.41 s and the evacuation
time of the second
occupant out would be 12.41 s + 18.18 s = 30.59 s. The
procedure is repeated for
all 120 occupants (see Table 1). An example of one possible
curve is provided in Fig-
ure 7. The assumed population consists of 120 occupants. The
evaluation of the
number of runs to be simulated is the unknown variable of this
example.
The steps of the evaluation method are applied as follows.
Step 1. Define the acceptance criteria.
This step deals with the definition of the five acceptable
thresholds TRTET,
TRSD, TRERD, TREPC, TRSC about the impact of the number
of runs on the
45. predicted outcome of the evacuation model for the same
evacuation scenario (see
Eqs. 26–30). The number of consecutive runs b = 10 for which
the acceptance
thresholds needs to be accomplished is also defined.
TRTET ¼ 0:5% ð26Þ
TRSD ¼ 5% ð27Þ
Analysis of Behavioural Uncertainty 1559
TRERD ¼ 1% ð28Þ
TREPC ¼ 1% ð29Þ
TRSC ¼ 1% ð30Þ
For instance, this means that the acceptance criteria are
satisfied if
TETconvj < TRTET for 10 consecutive runs, SDconvj < TRSD
for 10 consecutive
runs, etc.
It should be noted that the acceptance criteria have been
selected with the only
purpose of showing the procedure, i.e., they do not represent
recommended values
for use in real engineering analyses. Nevertheless, those criteria
represent possible
values in the context of fire safety engineering in relation to all
types of uncer-
tainty associated with modelling results. In fact, the authors
argue that thresholds
46. below 5% would permit the assessment of the required safe
egress time with a
reasonable degree of accuracy. The definition of the criteria
would be dependent
0
200
400
600
800
1000
1200
1400
1600
0 20 40 60 80 100 120
E
v
a
c
u
a
ti
o
47. n
t
im
e
(
s
)
Occupants out (n)
Figure 7. Fictitious data representing one possible curve of
evacua-
tion times.
Table 1
Example of Fictitious Data Representing One Possible
Occupant-
Evacuation Curve
Occupants out Pseudo-random generated number Evacuation
time (s)
1 12.41 12.41
2 18.18 30.59
3 20.22 50.81
4 8.43 59.24
… … …
120 … 1401.09
48. 1560 Fire Technology 2014
on several factors, such as the type of evacuation scenario, data
under consider-
ation, the scope of the analysis, etc.
Step 2. Run a finite set of n runs of the same evacuation
scenario.
An arbitrary initial number of simulations of the same scenario
is set to 35.
n = 35 vectors of 120 dimensions m
*
ij ¼ m11; . . . ; mij; . . . ; m12035
� �
corresponding
to the simulated evacuation times of each occupant ith (for a
total of 120 occu-
pants) in each jth run are obtained (for a total of 35 runs).
In the present example, the 35 fictitious curves have been
generated using the
method described at the beginning of Section 3. They result in
the 35 curves
showed in Figure 8. The curves presented in Figure 7 are
representative of a set
of repeated results of an evacuation model in the case of a
hypothetical evacua-
tion scenario for lognormal distribution of evacuation times
[19]. It should be
noted that the shape of the evacuation curves may be different
49. than the example
provided here (e.g. s-shaped occupant-evacuation curves). The
method is based
on convergence measures which are independent on the shape of
the curves, thus
it may be applicable for any type of curve.
The vector corresponding to the consecutive average curves
M
*
¼ðM1; . . . ;M35Þ is also generated.
Step 3. Calculate the convergence measures.
The convergence measures presented in the previous sections
are calculated for
all 35 runs, i.e., TETconvj, SDconvj, ERDconvj, EPCconvj, and
SCconvj in accordance
to Equations 11, 13, 15, 17, and 19, respectively. In this
example a single value
for s in Equation 19 has been used, namely s = 4. Results are
presented in
Table 2.
Step 4-4bis. Compare the convergence measures with the
acceptance criteria.
Results for 35 runs are compared with the acceptance criteria
defined in Step 1
(see also Equations 21–25). Table 3 shows the results of the
tests in relation to
the number of runs. When the box shows ‘‘FAILED’’, it means
that the test is
0
51. Figure 8. Fictitious data representing 35 runs of the same
hypotheti-
cal evacuation scenario.
Analysis of Behavioural Uncertainty 1561
failed. When the test is passed, the box is left blank. After 10
consecutive runs
(given the acceptance criteria defined in Step 1), when the test
is passed, the box
shows ‘‘OK’’, which means that the acceptance criteria have
been met.
In this example, Test 1 failed, Test 2 is passed after 25 runs,
Test 3 is accom-
plished after 26 runs, Test 4 is failed, and Test 5 is
accomplished after 15 runs.
This means that our predicted curve meet the acceptance criteria
with regards of
the SD of the TET, the ERD and the SC. Nevertheless, there are
two criteria
that have not been met (TET and EPC). It is therefore necessary
to proceed with
Step 5 by conducting additional runs.
Table 2
RESULTS Corresponding to 35 Runs of the Same Evacuation
Scenario
(Expressed in %)
Run (n) TETconvj (%) SDconvj (%) ERDjconv (%) EPCjconv
(%) SCjconv (%)
54. Step 5. Simulate a set of additional simulations m so that the
new set of runs for
the comparison is S = n + m.
Another set of runs m = 35 of the same scenario—corresponding
to addi-
tional 35 occupant-evacuation time curves—are considered for a
total of
S = n + m = 35 + 35 = 70 runs. In this example, additional
fictitious data
are produced using the same method as the first 35 curves. A
new set of
S = n + m S
*
ij vectors S
*
ij ¼ S11; . . . ; Sij; . . . ; SqS
� �
corresponding to the average
simulated evacuation times of each of the 120 occupant ith in
each of the 70 jth
run S are produced.
Table 3
Summary of the Results of the Tests in Step 4
Run Test 1 Test 2 Test 3 Test 4 Test 5
1 / / / / /
57. The evaluation method is now repeated for S = 70 runs, starting
from Step 3,
called here Step 3.2.
Step 3.2. Calculate the convergence measures.
The failing convergence measures are calculated for S = 70
runs, i.e.,
TETconvFIN, and EPCconvFIN for our case study.
Step 4.2-4.2bis. Compare the convergence measures with the
acceptance criteria.
Results for S = 70 runs are compared again with the acceptance
criteria
defined in Step 1. Table 4 shows the results of the tests that
were previously fail-
ing in relation to the number of runs.
Table 4 shows that Test 4 is accomplished after 40 runs. An
example of the
number of runs required to accomplish different criteria for
TRTET (where
TETconvj < TRTET for 10 consecutive runs) for the fictitious
data-set under consid-
eration is shown in Figure 9. The grey dashed vertical line
refers to the acceptance
criteria TRTET = 0.5% which has been selected for the analysis
of the TET in
Step 1. Test 1 is passed after 61 runs if the convergence criteria
are TETconvj <
0.5% for 10 consecutive runs. This means that our predicted
curve now meet all
acceptance criteria.
58. Table 4
Results of Test 1 and Test 4 for 70 Runs
Run Test 1 Test 4 Run Test 1 Test 4 Run Test 1 Test 4
1 24 47
2 Failed 25 48
3 Failed Failed 26 Failed 49
4 Failed Failed 27 Failed 50
5 Failed Failed 28 51 Failed
6 Failed Failed 29 Failed Failed 52
7 30 Failed 53
8 Failed Failed 31 54
9 Failed Failed 32 Failed 55
10 Failed Failed 33 56
11 Failed 34 57
12 Failed 35 58
13 Failed 36 59
14 Failed 37 60
15 Failed Failed 38 61 OK
16 Failed 39 62
59. 17 Failed 40 Failed OK 63
18 Failed Failed 41 64
19 Failed 42 Failed 65
20 Failed 43 66
21 44 67
22 45 68
23 Failed 46 69
70
1564 Fire Technology 2014
4. Discussion
The analysis of the trend of the convergence measures is useful
to obtain general
information on the type of data-set under consideration. For
example, it is possi-
ble to assess behavioural uncertainty and therefore estimate the
impact of the use
of stochastic variables/distributions on evacuation model
results.
An example from the data of the case study in Section 3.1 is
presented in Fig-
ure 10 where TETconvj and SDconvj are shown as well as
Figure 11 where ERDconvj,
60. EPCconvj, and SCconvj are shown (convergence measures are
calculated for a total
of 140 consecutive average number of runs, i.e., 70 additional
runs have been ana-
lysed).
0
10
20
30
40
50
60
70
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
N
u
m
b
e
r
o
f
61. ru
n
s
(
n
)
Criteria for TRTET (%)
Figure 9. Number of required runs in relation to different
criteria for
TRTET.
0
2
4
6
8
10
12
14
16
18
0 20 40 60 80 100 120 140
62. %
Number of runs (n)
TETconvj SDconvj TRtet TRsd
Figure 10. TETconvj, SDconvj in relation to the consecutive
average
number of runs (expressed in %).
Analysis of Behavioural Uncertainty 1565
Figures 10 and 11 show that the SD of the evacuation time
SCconvj of the last
occupant is the slowest converging variable. Together with
TETconvj, those vari-
ables are useful to understand the variability of the TET in
relation to the number
of runs. An estimation of uncertainty (including behavioural
uncertainty) associ-
ated with the TET is a key aspect of fire safety engineering
analysis since it repre-
sents the required safe egress time (RSET) [19], the time needed
by all occupants
to perform a safe evacuation.
The analysis of the convergence of ERDconvj, EPCconvj, and
SCconvj is also a sig-
nificant contribution to the understanding of behavioural
uncertainty, since it per-
mits the analysis of the variability of the predicted occupant-
evacuation time
curves in relation to the number of runs. In the example
63. provided here, the con-
vergence measures are below 2.5% after 17 runs, thus
permitting the estimation of
the average occupant-evacuation time curve with an admitted
2.5% variability in
a relatively small number of runs.
The simulation of additional 70 runs (for a total of 140 runs in
Figures 10
and 11) shows that, as expected, results continue to converge
and the effect of
behavioural uncertainty on average occupant evacuation time is
progressively
reduced. Nevertheless, if the acceptance criteria include the
requirement of being
below the thresholds for a sufficient number of consecutive runs
(i.e. a critical
number that the model user should select in relation to the
scenario under consid-
eration in order to verify the stability of the convergence), the
simulation of addi-
tional runs does not provide any additional benefits to the
modeller. The selection
of the number of runs is optimised in relation to the pre-defined
acceptance crite-
ria and there is no need to simulate additional runs.
A statistical estimation of the uncertainty associated with the
use of the conver-
gence measures can be performed in relation to the number of
runs. This includes
the study of the uncertainty of the sample average TETs and the
sample SDs.
0
2
64. 4
6
8
10
12
14
16
18
0 20 40 60 80 100 120 140
%
Number of runs (n)
ERDjconv EPCjconv
SCjconv TRerd = TRepc = TRsc
Figure 11. ERDconvj, EPCconvj, and SCconvj in relation to the
consecu-
tive average number of runs (expressed in %).
1566 Fire Technology 2014
Assume that each TET in the vector TET
*
is a sum of random variables corre-
sponding to the inter-temporal times between each occupant.
Applying the central
limit theorem, the series corresponding to the vector TET
65. *
consists of normally dis-
tributed values TETj � N(l, r2), where l is the true mean value
and r2 is the true
variance. The sample variance is:
s2 ¼
Pn
j¼1 TETj � TETavj
� �2
n � 1
ð31Þ
where n is the number of runs. Applying Cochran’s theorem, s2
� r2n�1 v
2
n�1, a Chi
squared distribution with n - 1 degrees of freedom. Then, the
variance of the
sample variance, Var(s
2
), corresponds to:
Varðs2Þ¼ Var
r2
n � 1
v2n�1
� �
66. ¼
r2
n � 1
� �2
Var v2n�1
� �
¼
r2
n � 1
� �2
2 n �1ð Þ¼
2r4
n �1
ð32Þ
The sample SD s is distributed as a chi distribution with n - 1
degrees of free-
dom, i.e., s � rffiffi
n
p
�1 vn�1. Hence the variance of the SDs of the sample data
corre-
sponds to:
Var sð Þ¼ Var
68. 4
3
5
ð33Þ
where C(n) is a gamma function. Hence, it is possible to
estimate the relative SD
(the relative difference between the use of sample SDs and the
SDs corresponding
to the true distribution):
relative Std sð Þ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n � 1� 2 C
n
2ð Þ
C n�1
2ð Þ
2
" #
n � 1
v
u
69. u
u
u
t
ð34Þ
This information permits an estimation of the uncertainty
associated with the use
of the estimate SDs SDj employed in the evaluation method in
relation to the
number of runs under consideration.
It is also possible to perform an estimation of the uncertainty
associated with
the use of the estimate variance of the sample data s
2
when calculating the aver-
age sample TET TETavj. In fact, the average of the TET TETavj
is distributed as
Analysis of Behavioural Uncertainty 1567
s
ffiffi
n
p tn�1 þ l, where tn-1 is a Student t random variable with n-1
degrees of free-
70. dom.
Therefore the variance of the sample average TETavj
corresponds to:
Var TETavj
� �
¼
s2
n
n � 1
n � 3
ð35Þ
And the uncertainty of the TETavj is:
SD TETavj
� �
¼ s
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n �1
nðn �3Þ
s
ð36Þ
It is therefore possible to estimate the uncertainty associated
with the number of
runs given the use of the sample TETavj.
71. To date, behavioural uncertainty is generally treated only in a
qualitative man-
ner (performing a qualitative evaluation of the number of runs
to be simulated). It
is argued that the present work would encourage evacuation
model users to per-
form a quantitative treatment of this type of uncertainty given
the simplicity of
the method proposed.
The benefits obtained from the use of the method apply to
design studies as
well as model validation. The proposed method permits an
estimation of the con-
vergence of the simulated occupant-evacuation curve towards
the average curve,
thus increasing the understanding of the model predictions. This
is reflected in a
better understanding of the variability of RSET and the possible
estimation of the
margin of safety of a specific design in relation to behavioural
uncertainty.
From a model validation perspective, to date, two antithetical
approaches may
be used to present model comparison with experimental data,
namely (1) the use
of the best model estimation for the occupant-evacuation time
curve, or (2) the
average occupant-evacuation time curve. The method presented
in this section
increases the usability of the second approach, since it allows a
thorough quanti-
tative understanding of the average curves produced by
evacuation models. Future
work based on the presented method is therefore a definition of
72. an evacuation
model validation protocol which uses the convergence measures
to assess the dif-
ferences between model predictions and experimental data by
taking into account
behavioural uncertainty.
A possible application of the method presented in this paper
may be its use for
the comparison of model predictions produced by different
evacuation models. It
would be in fact possible to quantify the impact of the
stochastic variables and
assumptions used by different evacuation models given the same
evacuation sce-
nario.
5. Limitations
A set of limitations of the proposed method can be identified
both in terms of its
assumptions as well as its applicability.
1568 Fire Technology 2014
The first limitation of the method is that it uses the concepts of
convergence in
mean and the central limit theorem rather than a statistical
estimation of the
expected values. Hence, the choice of the requirement for the
finite number of
consecutive runs b for which the acceptable thresholds must not
be crossed should
be carefully evaluated by the modeller in relation to the data
73. under consideration.
This limitation is tempered by the simplicity of the proposed
method, i.e., it can
be applied by evacuation modellers to analyse behavioural
uncertainty without a
complex inferential statistical treatment of the data, which may
require time and
user expertise.
Another limitation of the method is associated with the
assumptions that evacu-
ation curves can be identical between model runs even in the
case of different
behaviours occurring, i.e., the arrival rates to the exits are the
same but they refer
to different occupants or different exits.
With regards of the method applicability, multiple data-sets of a
single evacua-
tion scenario are rarely available in the literature. This makes it
difficult to study
the impact of behavioural uncertainty on experimental data.
Given the current
stage of experimental evacuation research, the proposed method
is mainly applica-
ble to the analysis of behavioural uncertainty in simulation
results. Once addi-
tional experimental data on individual scenarios will be
available, researchers will
be able to use the same concepts introduced in this paper for the
analysis of
behavioural uncertainty in experimental data.
Without multiple experimental data, a single experiment often
represents the
only reference on that specific evacuation scenario, but it is not
74. clear whether it
represents average behaviour or it is a tail of the curve. In fact,
the assessment of
experimental and evacuation model results may also include the
analysis of the
tails of the distribution rather than the analysis of the peaks (i.e.
average values).
Nevertheless, the authors argue that the study of the average
model predictions
together with the variability of results around the average is
deemed to be a useful
method to analyse behavioural uncertainty. The research
community of human
behaviour in fire is aware of the lack of experimental data and
the need to fill this
gap with data collection efforts [2]. In recent years, significant
data collection
efforts have been carried out (e.g. several projects were
performed for different
aspects/conditions of the evacuation process using several tool
to aid the collec-
tion and quality of data [20, 21]). Therefore, it can be argued
that considering a
long-term perspective, it will be possible to assess behavioural
uncertainty also for
experimental data (thus making the method proposed in the
paper applicable also
for that issue).
The method is presented using a case study based on pseudo-
random generated
numbers. Future work can be based on the analysis of the
results of an evacua-
tion model from a real world case study.
6. Conclusions
75. This paper introduces a simple methodology to analyse the
variability of evacua-
tion model predictions of an individual evacuation scenario in
relation to the
Analysis of Behavioural Uncertainty 1569
number of test cases. The method allows increasing the
understanding of the
uncertainty in evacuation model results, which depend on the
stochastic nature of
human behaviour, here called behavioural uncertainty.
This paper presents a step forward towards a more accurate
interpretation of
evacuation model results, because it introduces a novel method
to perform quanti-
tative estimation of the convergence of evacuation model
predictions. In fact, the
use of functional analysis operators permitted the analysis of
the entire occupant-
evacuation time curves, rather than a study focused on the
average and SDs of
evacuation times only.
This paper represents the starting point for a better quantitative
interpretation
of evacuation behaviour. In fact, future applications are
associated with the inter-
pretation of behavioural experimental data as well as the
development of a stan-
dard model validation protocol.
76. Acknowledgments
The authors wish to acknowledge Erica Kuligowski, Craig
Weinschenk, Thomas
Cleary, Anthony Hamins, and Rita Fahy for a comprehensive
review of the paper
before publication. Enrico Ronchi thanks Daniel Nilsson for the
fruitful discus-
sions which have led to the idea of this paper. Finally, the
authors wish to thank
Blaza Toman for her valuable suggestions for the assessment of
the uncertainty
associated with the proposed criteria.
References
1. Hamins A, McGrattan K (2007) Verification and validation of
selected fire models for
nuclear power plant applications. Final Report NUREG-1824.
U.S. Nuclear Regula-
tory Commission
2. Averill JD (2011) Five grand challenges in pedestrian and
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c.10694_2013_Article_352.pdfA Method for the Analysis of
Behavioural Uncertainty in Evacuation
ModellingAbstractMethodFunctional Analysis
ConceptsConvergence MeasuresConvergence Measure 1:
TETConvergence Measure 2: SD of TETsConvergence Measure
3: ERDConvergence Measure 4: EPCConvergence Measure 5:
SCThe Evaluation MethodCase
StudyDiscussionLimitationsConclusionsAcknowledgmentsRefer
ences
Escape behavior in factory workshop fire emergencies:
a multi-agent simulation
Kefan Xie • Jia Liu • Yun Chen • Yong Chen
Published online: 24 May 2014
� Springer Science+Business Media New York 2014
Abstract In this study, a multi-agent simulation is con-
ducted to explore the relationship between fire escape
survival rate and occupants’ risk preferences and stress
capacity. The results indicate that, the escape survival rates
for occupants with different risk preferences and stress
capacities can be significantly different. More specifically,
the simulation shows that the smaller the number of
81. occupants is in a fire, the higher the survival rate can be
expected. In addition, the simulation shows that the larger
the number of individuals with stronger stress capacities is
in a group, the higher the escape survival rate the group
has. Moreover, the simulation shows that the more disperse
the individuals’ risk preferences is in a group, the higher
the escape survival rate the group has. Based on the sim-
ulation results, the paper proposes a framework of
E-evacuation system to guide the rational escape and
evacuation when enterprise workshop fire occurs. Sugges-
tions for increasing escape survival rates during fires are
provided.
Keywords E-evacuation � Risk preference � Stress
ability � Multi-agent system � Fire � Evacuation � Escape
survival rate
1 Introduction
Fire is one of the most common emergencies. It may cause
huge property damages and casualties, especially in places
such as cinemas and factories. In such places, it is extre-
82. mely hard for occupants to escape. If occupants have not
received any escape training before or a well prepared
escape plan is not available, they can hardly survive. This
is simply because in such emergent conditions, occupants
have to make a correct decision right away and take proper
actions immediately. The correct escape decision and
proper actions in scene of a fire means the difference
between life and death.
In the past decades, scholars have explored occupants’
decision making and escape behaviors in emergencies. For
example, Kelley and Condry [13] find that the more the
losses are, the lower the escape survival rate is; and that the
bigger a group is, the fewer group numbers escape suc-
cessfully. They also find that if the behaviors of group
members are guided by each other, the escape results will
change; and that a positive expression of a group’s confi-
dence will substantially increase the group’s success
escape rate. Helbing et al. [10] explore the irrational
83. characteristics of the group panic escape behaviors in
emergencies. Saloma et al. [23] study the self-organized
queuing and scale-free behavior in real escape panic. Joo
et al. [11] conducted agent-based simulation of affordance-
based human behaviors in emergency evacuation. Lv et al.
[16] examine evacuation decision-making behaviors and
risk analysis under multiple uncertainties in emergencies.
Ozbay et al. [18] model the emergency evacuation in
Northern New Jersey based on a regional transportation
planning tool. Pereira et al. [19] employ a finite automata
approach to simulate the evacuation of a congested popu-
lation in emergency. Sun et al. [29] develop an emergency
K. Xie � J. Liu � Y. Chen (&)
School of Management, Wuhan University of Technology,
Hubei 430070, People’s Republic of China
e-mail: [email protected]
Y. Chen
Old Dominion University, Norfolk, VA 23529, USA
123
84. Inf Technol Manag (2014) 15:141–149
DOI 10.1007/s10799-014-0185-1
evacuation information system in order to make emergency
decision-making more effective. Numerous other authors
have studied emergency situations [5, 6, 24, 25, 33, 34, 36].
All of these studies focus on observing occupants’
escape behavior characteristics directly. However, they did
not pay attention to how occupants’ risk preferences and
stress capacity impact their escape behaviors. More spe-
cifically, few researches explore how occupants’ risk
preferences and stress capacity impact escape survival rate
in fires. Risk preference represents an individual’s attitude
to risks. Engelmann and Tamir [4] prove that individuals’
risk preferences have influences on their decision-making
processes with neuroscience methods. Risk preferences can
be divided into risk loving and risk avoiding. According to
Abrahamsson and Johansson [1], the majority of social
85. emergencies resulting in death are related with risk seeding
behaviors. Their further study points out that individual
risk preferences are influenced by circumstances and that
group risk preferences exist. Stress capacity refers to an
individual’s ability to deal with stress when he/she
encounters critical, complex, and difficult situations [31].
Stress capacity can impact the quality of decision [3] and
decision-making process [14, 17]. As such, this paper
adopts a multi-agent simulation to simulate occupants’
emergency escape decision-making process and actions
during a fire in the context of labor-intensive factory
workshops. The goal of this paper is to explore the rela-
tionship between escape survival rate and occupants’ risk
preferences and stress capacity.
The rest of this paper is organized as follows: In Sect. 2,
background of fires occurring in factory workshops is
provided. Occupants’ escape behaviors and the main
challenges for their decision-making during a fire are dis-
86. cussed as well. Section 3 provides an overview of the
multi-agent programming language and modeling envi-
ronment that this paper adopts to simulate factory work-
shop fires. Parameters settings are introduced in this
section. Section 4 lists the behavior rules for agents in this
simulation. In Sect. 5, the operation process of the per-
formed simulation is introduced. Results of the simulation
are discussed in Sect. 6. Based on the simulation, a
framework of E-evacuation system for enterprise workshop
fire emergency is proposed in Sect. 7. At the end, Sect. 8
concludes the whole paper and provides suggestions for
increasing escape survival rates during fires.
2 Background
Occupants in factory workshops must have certain risk
preferences and stress capacities in order to make the
correct decision and take proper actions to escape because
fires occurring in factory workshops are very dangerous
and might cause unexpected losses. The texture and
87. quantity of materials stored in workshops will affect the
speed at which a fire spreads. Specifically, whether the
materials in a workshop are flammable and the degree of
flammability play important roles in fire spreading speeds:
the higher the degree of flammability is, the quicker a fire
spreads. For example, textile workshops are usually full of
combustible materials. Once a textile workshop is on fire,
these materials will cause the fire to spread quickly. As a
result, workers have very little time to escape and suffer
great pressure. Fires occurring in chemical workshops are
more dangerous because other than flammable materials,
toxic substances and explosives are usually stored in these
places. The opportunity for workers to escape from such
fires is pretty slim. In addition, the amount of materials in a
workshop can directly affect the spreading speed of a fire
and occupants escaping routes. Large amount of materials
means many flammable things, long burning time, and
large scale of fire. Meanwhile, the stacked material can
88. block occupants escaping routes. This will slow down the
escaping speed. Furthermore, the logistic model chosen by
a company will affect the inventory in a workshop. Spe-
cifically, the options of first-party, second-party or third-
party logistics by a company will significantly influence the
changing speed of workshop inventory per unit time, which
thereby affects the patency of occupants escaping routes.
Finally, workshop building design is an important factor
that impact workers’ escape. The lack of exits will cause
congestion during the evacuation in a fire.
According to Chu and Sun [2], how long occupant
evacuation last depends on ‘‘fire detection and alarm,
occupant characteristics (such as age, sex, physical and
mental ability, sleeping or waking, and population density),
human behavior in fire (such as seeking information,
informing others, collecting belongings, and choosing an
exit) and building characteristics (such as corridor width,
exit numbers and widths), etc.’’ (p. 1126). Occupants’
89. escape behaviors vary in factory workshop fires. For
example, on August 27, 2011, Bao Dongsheng Plastic
Products Factory in Longgang District, Shenzhen, China
was on fire. Disconcerted workers looked for ways to
escape. In the chaos, two flustered workers jumped from
the workshop building, one dead and another seriously
injured. On December 14, 2009, a workshop at a third floor
with 45 workers in Runsen Shoe Factory in FuZhou, China
caught fire. Because the fire blocked the emergency exit,
workers had to go upstairs and jumped from the 4th floor,
14 seriously injured. In contrast, workers in a workshop fire
breaking out in Ningbo China escaped successfully by
hitting a hole in the wall when the emergency exits was
blocked by the fire.
Fire escape behaviors in factory workshops are the
results of occupants’ decision making that are influenced
142 Inf Technol Manag (2014) 15:141–149
123
90. by many variables, such as interactions between the
occupants, the building, and the developing fire [22]. Dif-
ferent people have different levels of awareness of what is
happening in their surrounding environment [20]. Once
they realize that a fire occurs, occupants make their deci-
sions based on the result of their risk assessment. For
example, they can put out the fire first and then escape, or
escape first and come back later with help to put out the
fire, or take an active but risky way to rush out of the fire
scene, or just wait for rescue. Table 1 shows the main
problems in occupants’ decision making process in a fac-
tory workshop fire.
The key point is that occupants must make their deci-
sions immediately. Sime [28] points out that delays in
occupants starting to move and movement other than
escape could be a major feature of human behavior in fires.
Shields and Boyce [26] indicate that a primary factor
91. contributing to fire deaths is not travel distance to exits, but
delays in warning occupants and extended times before
movement commenced. According to Proulx and Sime [21]
and Sime [28], the delay in starting positive evacuation
actions can be much longer than the time to travel the
distances to and through exits. Occupants do not have
much time to think over the options listed in Table 1. The
condition of workshop can easily boost quick spread fires.
Panic occupants will worsen the chaos at fire scene. Even
worse, emergency exits might be blocked by fires. In such
emergent situations, occupants’ individual risk preferences
and stress capacities play important roles in their decision-
making processes.
Prior research has proved that training, more specifically
risk preference and stress capacity trainings, impact indi-
viduals’ decision making and behaviors in a fire. Fire drill
can help occupants make correct decisions and take proper
actions during a fire. For example, on April 1, 2010, a
92. 2000 m
2
workshop with 1,078 workers in YierKang Shoe
Company in Zhejiang, China was on fire. Thanks to the
regular fire evacuation drills, these workers covered wet
towels on their mouths, followed the scheduled escape
route, and fled the fire in an orderly manner. Fortunately,
the fire did not cause any casualty.
3 Simulation and parameters setting
This study conducts a simulation based on Netlogo to
explore the relationship between escape survival rate and
occupants’ risk preferences and stress capacities. NetLogo
is a multi-agent programming language and modeling
environment for simulating natural and social phenomena
[32]. It allows researchers to give instructions to huge
number of independent ‘‘agents’’ who are operating con-
currently. In this regard, NetLogo can help researchers
‘‘explore connections between micro-level behaviors of
93. individuals and macro-level patterns that emerge from their
interactions’’ [32]. Figure 1 shows the interface of the
established multi-agent simulation applied in this study.
The simulation consists of machinery, equipment, walk-
ways, workshop facades, workshop exits, and safety zones.
Suppose that workshop exterior walls, machinery, and
equipment are taller than an ordinary person’s height and
that no one can stand or walk on the top of the machinery,
equipment or workshop exterior walls.
The simulated event is a fire that occurred at KPT Fit-
ness Equipment Co., Ltd. in Dongguan China. Established
in 1993, KPT has solid experiences in indoor fitness
equipment R&D and manufacturing. It specializes in pro-
ducing electric treadmills, exercise bikes, and elliptical
machines. On March 6, 2012, welding sparks ignited a fire
in one of its workshop on a second floor. Very soon the fire
burned down all windows. Raw materials, such as foam,
burned quickly and stuck together. This caused the work-
94. shop full of a pungent odor. Although 256 occupants were
trapped, they were well organized and escaped in order.
Fortunately, they all survived in this terrible fire.
The multi-agent simulation accepts different parameters
within a certain range. Therefore, various scenarios in
factory workshop fires can be simulated. For example, the
position of the start point of a fire is controlled by hori-
zontal axis (Fire-Start-Point-X [ [-16, 16]) and vertical
axis (Fire-Start-Point-Y [ [-16, 16]). The speed at which a
fire spreads is controlled by fire spread speed (Fire-Spread-
Speed [ [1, 20]). People count (People-count [ [1, 300])
Table 1 Main problem in occupants’ decision making process in
a
factory workshop fire
Decision making
problems
Initial decision Subsequent decision
General decision 1. Fight against
the fire
2. Escape and
95. evacuation
1. Choose fire fight first, when
fire cannot be weakened,
even bigger and more
dangerous, choose to escape
2. Escape successful and
come back to fight against
the fire
Self-save and
waiting for
rescue decision
1. Be active to
escape
2. Wait for the
rescue
1. Not successful in escape,
then wait for the rescue
2. No result in the external
96. rescue, then seek for self-
rescue
Channel (exit)
selection
decision
1. Follow other
occupants to
escape
2. Choose a
nearby exit to
flee
When exit is blocked, choose
some means, such as
smashing windows, hitting
hole in walls, to flee through
the broken exits
Inf Technol Manag (2014) 15:141–149 143
123
97. represents that the number of occupants that are evenly
distributed in the walkways. Stress capacity proportion
(Stress-Capacity-Proportion [ [1, 10]) is the ratio between
the number of occupants with strong stress capacities and
the reciprocal of the value of Stress-Capacity-Proportion.
Risk preference proportion (Risk-Preference-Proportion [
[1, 10]) is the ratio between the number of occupants who
are risk loving and the reciprocal of the value of Risk-
Preference-Proportion. Results vary if different parameters
are set.
4 Behavior regulations for agents
Each unit area in a factory workshop, such as safety zone,
exterior wall, exit, walkway, machine, equipment, and other
fixed facilities, is set as Patch, whereas each occupant is set as
Turtle in this simulation. A Turtle has four attributes, namely
life value, risk preference, stress capacity, and escape direc-
tion. Risk preference has two values: ‘‘0’’ and ‘‘1’’. The former
represents risk loving while the latter means risk avoiding.
98. Similarly, stress capacity has two values: ‘‘0’’ and ‘‘1’’ as well.
The former means weak stress capacity and the latter repre-
sents strong stress capacity. Escape direction have four values,
namely ‘‘1’’, ‘‘2’’, ‘‘3’’, ‘‘4’’. These four values indicate the
directions of the exits that occupants head for and they rep-
resent northwest, northeast, southeast, and southwest respec-
tively. During a factory workshop fire, occupants’ eyesight
might be blocked by smoke or equipment easily. In such an
urgent situation, it is very hard for them to make correct
judgments about what is the shortest way to the nearest exit. In
Netlogo simulation, one Patch usually accommodates one
Turtle. But in factory workshop fires crowded occupants are
trapped in limited spaces. Therefore, in this simulation, a
Patch is set to accommodate two Turtles.
Gwynne et al. [9] and Sime [27] point out that instead of
heading towards the nearest exit, occupants prefer to move
toward other more distant exits with which they have had
previous experience and with which they feel more confi-
99. dent. However, in this simulation, if occupants are familiar
with all formal exits, those who have strong stress capacity
are more likely to calmly observe their surroundings and to
find the shortest way to the nearest exit, whereas those who
have weak stress capacity tend to run about aimlessly
because they randomly choose one exit to escape. In such a
case, stress capacity impact occupants’ decision processes.
However, risk preferences do not have any impact. There-
fore, the study first sets the following rules for Turtles’
decision making in the simulation:
Rule A1: If Turtles are familiar with all formal exits,
those with strong stress abilities choose to escape from
the nearest one;
Rule A2: If Turtles are familiar with all formal exits,
those with weak stress abilities randomly choose one to
escape.
Fig. 1 The interface of system
simulation
144 Inf Technol Manag (2014) 15:141–149
100. 123
In some cases, alternative exits exist but occupants do
not know where they are. Since those with strong stress
abilities prefer to find a nearest exit and those with weak
stress abilities randomly select an exit, all of them have to
face a challenge if their preference is an alternative exit.
Alternative exits are not used often and few occupants
know how to access them and how they work, so it is very
risky to pick an alternative exit during a factory workshop
fire whether this exit is a nearest one or a randomly selected
one. Due to the risks, alternative exits are not crowded as
formal ones. In such a case, risk loving occupants tend to
choose alternative exits, whereas risk avoiding occupants
prefer formal ones. Combining the effects of stress abilities
and risk preferences, the paper continues to set the fol-
lowing rules for Turtles’ decision making in the simulation:
Rule B1: If Turtles only are familiar with formal exits,
101. those risk loving Turtles with high stress capacities
choose to escape from the nearest alternative exit;
Rule B2: If Turtles only are familiar with formal exits,
those risk avoiding Turtles with high stress capacities
choose to escape from the nearest formal exit;
Rule B3: If Turtles only are familiar with formal exits,
those risk loving Turtles with low stress capacities
randomly choose an alternative exit to escape.
Rule B4: If Turtles only are familiar with formal exits,
those risk avoiding Turtles with low stress capacities
randomly choose a formal exit to escape.
5 Simulating operation process
Once the parameters are set, the initialization of the sim-
ulation is done. In the initialization stage, Patches are built
up, including machinery, equipment, walkways, workshop
facades, workshop exits, and scenes of safety zones. Preset
Turtles are randomly distributed to the walkways. In
addition, the attributes of each Turtle are configured based
102. on preset risk preference proportion and stress ability
proportion values.
Next, the simulation moves to the running stage. Turtles
follow the established rules and select an exit first. On one
hand, they need to calculate whether the space in front of a
Patch is big enough to accommodate them. If yes, they can
move on to the next step. If no, their next movement
depends on their positions. For example, if a Turtle is at a
crossing, it can choose a different direction. Otherwise, it
has to take a step backward. When a Turtle arrives at a
target exit, the system will automatically set its location as
a safe area. Accordingly, observation on this Turtle is done.
On the other hand, Turtles need to figure out the intensity
of the fire. The fire will spread at a preset speed, so in a
given period of time a Patch will catch fire. When the fire
reaches a Turtle’s Patch, the turtle’s life value will minus 1.
If a Turtle’s life value is less than 0, the Turtle is dead. The
fire can reach any place inside the workshop other than safe
103. areas. When all areas inside the workshop are covered by
fire, the simulation ends (Fig. 2).
6 Analysis of simulation results
The simulation has three phases. In phase 1, the goal is to
explore how fast the spread speed of fire (FSS) is and the total
number of occupants (PA) that impacts the final escape sur-
vival rate (APA). So risk preference proportion (RPP) and
stress ability proportion (SAP) are both set as 2. Figure 3
shows results of four typical cases, in which FSS are set as 2, 5,
20, and 5 respectively and PA are set as 300, 300, 300, and 150
respectively. All four diagrams indicate that as time goes by
during fires, the number of survival occupants declines. This
means that the longer fires last, the fewer occupants can sur-
vive. According to diagram (1), diagram (2), and diagram (3),
lower speeds of fire cause fewer numbers of deaths. Diagram
(2) and diagram (4) show that the smaller the total number of
occupants is, the higher the survival rate is. Fewer occupants
will cause less congestion, so occupants have less troubles and
104. can escape faster. This result proves the finding reported by
Kelley and Condry [13].
In phase 2, the goal is to explore how stress capacity
ratio impacts the final escape survival rate (APA). Risk
preference ratio is the proportion of the risk preferences of
individuals in a group. Stress capacity ratio refers to the
proportion of the stress capacities of individuals in a group.
The two ratios can be calculated after risk preference
proportion (RPP), stress ability proportion (SAP), and total
number of occupants (PA) are set. In this phase, fire-caught
point is set as the start point (0, 0) and spread speed of fire
(FSS) is set as 5.
Tables 2, 3 and 4 show three cases, in which SAP are set
as 0.1, 0.5, and 1.0 respectively and PA are set as 100, 200,
and 300 respectively. Suppose that occupants are familiar
with all four formal exits, and that they show the analog
value of the escape survival ratio in the group with dif-
ferent individual stress capacity. In each case, the simula-
105. tion runs ten times and generates ten different values of
escape survival rate. The final escape survival rate is the
arithmetic average of these ten values. The three tables
show that the more individuals with strong stress capacities
are in a group, the higher escape survival rate this group
has. This is because individuals with strong stress capaci-
ties are capable of making independent judgments and
choosing a nearest exit, while those with weak stress
capacities are not able do so.
When high stress ability proportion of individuals is
close to 100 %, the highest ratio of escape survival can be
Inf Technol Manag (2014) 15:141–149 145
123
achieved. This is because in such a case everyone escapes
from the nearest exit. When all occupants are evenly dis-
tributed to exits, the selection of each exit is approximately
same. This is the way to minimize congestion and to
106. achieve the best escape survival rate. Meanwhile, the tables
also show that the larger the number of occupants is, the
smaller the escape survival ratio will be eventually.
In phase 3, the goal is to explore how risk preference
ratio and stress ability ratio interactively impact the final
escape survival rate (APA). Most settings are the same as
those in phase 2. The only difference is that phase 3 adds
risk preference proportion (RPP) to the setting list. In three
cases showed in Tables 5, 6, and 7, RPP are set as 0.1, 0.5,
and 1.0 respectively. Suppose that occupants are familiar
with both two formal exits, and that they show the analog
value of the escape survival ratio in the group with
different individual stress ability. In each case, the simu-
lation runs ten times and generates ten different values of
escape survival rate. The final escape survival rate is the
arithmetic average of these ten values.
Tables 5, 6, and 7 demonstrate that when alternative
exits exist, the finding in phase 2 is still valid. The more
107. individuals with strong stress capacities are in a group, the
higher escape survival rate this group has. In addition,
when the group risk preference ratio is set as 0.5, the
highest escape survival rate is achieved. This is because in
this setting the number of risk loving occupants is the same
as the number of risk avoiding ones. Risk loving occupants
choose alternative exits while risk avoiding ones chooses
formal exits. Occupants’ selections drive them to different
exits and congestions are minimized, so the final escape
survival rate rises. This result proves the findings reported
Establish
Circumstances
Patch
Randomly
Distributed Turtle
Setting Turtle’s
Atributes
Low stress ability
Turtle choose exits
at random
108. T
urtle
fam
il iar
w
ith
all
exi ts?
High stress ability
Turtle choose the
nearest exit
Turtle Choose
Escape Exit
High stress ability
and risk loving
Turtle choose the
nearest spare exit
Low stress ability
and risk averse
Turtle choose the
familiar exit at
random
Low stress ability
109. and risk loving
Turtle choose the
spare exit at random
High stress ability
and risk averse
Turtle choose the
nearest familiar exit
The fire spread to an
additional area
The fire not spread
The intensity of fire
gets more serious?
Turtle’s action is
according to the
rules
Full of fire
everywhere
End of Simulation
Turtle is in
walkway?
Turtle, no action
Life - 1
110. Turtle’s walkway
caught fire?
Life, no change
Turtle’s life is
less than 0?
Turtle is dead
Previous Patch has
Two Turtle?
Go one step further
Change to another
direction
Turtle is at the
crossing?
Draw one step back
Turtle is at the exit?
Configure Turtle to a
safe area at random
Y N
Y
N
Y N
111. Y N
N
Y N
Y N
Y
Y
Y
N
T
urtle’s
A
cti o n
R
ule
T
urtle
E
s cape
D
ecision
R
ul es
113. in
S
ystem
O
p erat io n
Purchase amount
Logistics category
Cargo flammability
Fires preading
speed
Logistics category
Fig. 2 Simulation operation process
146 Inf Technol Manag (2014) 15:141–149
123
by Abrahamsson and Johansson [1]. When stress capacity
ratio is set as 1, and risk preference proportion is set as 0.5,
a comparatively high escape survival rate can be achieved.
But it is still lower than the rate when all occupants know
all formal exits. This is because when risk loving/avoiding
114. occupants with high stress capacities choose a formal/
alternative exit that is nearest to them, it is better for them
to know all exits. If they do not, the one they choose might
not be the nearest one.
7 A framework of E-evacuation system for enterprise
workshop fire emergency
Risk management of enterprise workshop fire emergency
covers each stage of fire management, namely beforehand,
concurrent, and afterwards. The key part is how to prepare,
organize, and conduct an orderly evacuation. Information
technology plays an important role in evacuation prepara-
tion, organization, and implementation. As such, this paper
proposes an enterprise workshop fire E-evacuation system
Fig. 3 Some typical simulation
results
Table 2 The 100 occupants
case (all occupants are familiar
with four exits)
SAP 0.1 0.5 1.0
115. APA 78.1 87.1 99.5
Table 3 The 200 occupants
case (all occupants are familiar
with four exits)
SAP 0.1 0.5 1.0
APA 153.0 174.2 197.5
Table 4 The 300 occupants
case (all occupants are familiar
with four exits)
SAP 0.1 0.5 1.0
APA 181.4 221.4 271.3
Table 5 The 100 occupants
case (all occupants are familiar
with two exits)
RPP SAP
0.1 0.5 1.0
0.1 77.2 84.8 88.7
0.5 80.6 87.1 94.8
1.0 79.9 81.8 89.9
116. Table 6 The 200 occupants
case (all occupants are familiar
with two exits)
RPP SAP
0.1 0.5 1.0
0.1 106.5 124.5 139.5
0.5 144.8 150.9 156.7
1.0 102.3 112.1 126.7
Table 7 The 300 occupants
case (all occupants are familiar
with two exits)
RPP SAP
0.1 0.5 1.0
0.1 135.6 135.9 164.7
0.5 164.8 183.7 187.4
1.0 119.2 130.1 145.9
Inf Technol Manag (2014) 15:141–149 147
123
117. shown in Fig. 4. First, enterprises should conduct regular
workshop fire risk evaluation and identify potential risks. A
workshop fire risk early-warning system should be estab-
lished. Once a workshop fire potential risk is found, an in-
time warning should be available. Second, when a work-
shop fire occurs, the E-evacuation system should send out a
fire alarm immediately, start automatic spraying systems,
start electronic contingency plans, and mobilize timely the
fire brigade to put out the fire. Meanwhile, workshop
occupants should make correct decisions and take proper
actions to evacuate. When a fire alarm rings, safe exit signs
should be on.
8 Conclusions
Modeling and simulation has many application domains [7,
8, 12, 15, 30, 35]. This paper conducts a multi-agent simu-
lation to explore the relationship between escape survival
rate and occupants’ risk preferences and stress capacities.
The results verify that the escape survival rates of occupants
118. with different risk preferences and stress capacities are sig-
nificantly different. More specifically, the simulation proves
the finding in Kelley and Condry [13] that the smaller the
total number of occupants is in a fire, the higher the survival
rate is. In addition, the simulation shows that the more
individuals with strong stress abilities are in a group, the
higher escape survival rate this group has. Moreover, the
simulation shows that the more disperse the individuals’ risk
preferences is in a group, the higher the escape survival rate
this group has. These results prove the findings in Abra-
hamsson and Johansson [1] and Kelley and Condry [13].
The following suggestions can be made from the simu-
lation results to increase escape survival rates during fires.
First, make sure that every exit works properly and that
occupants are familiar all available exits. Second, provide
occupants fire escape training so that their stress abilities can
be improved. Third, if alternative exits are available, move
risk-loving workers to work close to these exits. Fourth,
119. conduct fire drills and let occupants be familiar with evac-
uation process. These suggestions can be applied not only to
enterprise workshop fire prevention and evacuation, but also
to emergency management in other densely populated
places.
In addition, enterprises can take the following measures to
manage their fire evacuation process: (1) Help employees
identify safe exit signs. Safe exit signs usually are in green
lights indicating directions in walkways. By following the
green lights, employees can find the exits; (2) Maintain
smooth communication. When fire occurs, emergency notice
should be immediately broadcasted to every corner of the
workshop. The start point of fire should be identified
immediately and reported to the manager. If a fire is out of
workers’ control, emergency call should be made right away
for outside help. The manager(s) should assign workers
immediately to shut down equipment and fight against fire.
Meanwhile, the manager(s) should move important docu-
120. ments and data to safe places; (3) Organize workers to escape
orderly. When a workshop is on fire, the team leaders should
keep calm, count team members, and let them stay where
they are. Then each team should find the nearest safe exit sign
and follow the directions to escape. Workers on the first floor
can jump out of windows directly if the way to exit is
blocked. Workers on the second floor need to hang on the
windowsill first to minimize the height to ground and then
jump. Workers on the third floor and above should close
doors and irrigate them so that the fire can be separated. They
should use a damp cloth mask to prevent inhalation of toxic
gas, and make noises let firefighters know where they are.
They also need to lower their bodies close to the floor
because air in higher position is not good for breath. Occu-
pants should not consider jumping to the ground because it is
easy to get serious hurt or even die. If the condition allows,
occupants can help themselves escape. Otherwise, they
should remain in a safe place and wait for help. When a fire
121. occurs, elevators should not be taken because elevators are
very easy to get stuck due to power outage. Furthermore,
elevator shafts often become chimneys during fires. It is very
risky and dangerous to use elevators during fires. If there are
ladders, occupants can use them and climb to the roof sur-
faces waiting help there.
Acknowledgments This study was supported by National
Natural
Science Foundation of China (90924010) and Independent
Innovation
Research Fund of Wuhan University of Technology (2013-lv-
002).
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Start the Fire Brigade
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