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APA Research Paper About Environmental Policies College Essay Help

3 pages


For this assignment, you will research and write an APA research paper about environmental policies, including the following:

Choose 1 U.S. environmental law. State the name of the law and the date the law was passed.
Summarize the major provisions of the law that you chose.
Describe the economic impact of the law. Provide specific economic data from credible references.
Has the law improved the environment or situation? Provide specific data from credible references.
Do you think that sound science has proven that global warming is a credible threat or not?
Should the United States adopt additional policies or laws to curb greenhouse gas emissions?

What should these policies or laws regulate or encourage?
Use specific facts to defend your position.

BOS 3571 External factors need essay help: need essay help

1) Explain how external factors can influence behavior and learning. Provide two examples that illustrate your point. Your response must be at least 200 words.

2)Explain the principle of praxis as applied to adult learning. Provide an example of how it could be applied in a safety training class. Your response must be at least 200 words

ECOENV 07 the effects of the policy devry tutorcom essay help

New: ECO-ENV-07 Two pages discussions and two responses letter. Thanks

Using the effects of the policy of the country chosen for your final project, discuss predictions and recommendations on output and demands using international evidence.

The nation with the largest percentage of publically held land essay help services: essay help services


1)Land value can generally be quantified in terms of dollars. One notable exception is



food value.


shelter value.


mineral value.


natural resource value.


intrinsic value.

2)The tendency of a shared, limited resource to become depleted because people act out of self interest for short-term gain is generally referred to as







the invisible hand.


the tragedy of the commons.


the Hardin effect.

3)In theory, a population grows most quickly at ____ % of the environments carrying capacity.












4)Cave swallows make nests out of saliva, which some people then harvest to make expensive soup. If too many nests are harvested, then the swallows wont be able to rear their young and the population will crash. When evaluating how many nests to harvest, we should focus on



market forces such as the cost of birds nest soup.


international policy.


maximum sustainable yield.


the tragedy of the commons.


cave swallow cave preference.

5)Approximately ____ % of the Earths land surface is considered protected by the United Nations.












6)The nation with the largest percentage of publically held land is



the United States


Costa Rica.







7)Most environmental law, policy, and management plans in the United States are based on



remediating externalities.


ecological principles.


economic forces.


the resource conservation ethic.


old English laws.

8)When looking at both private and public lands in the United States, the most common use is



timber production.


grazing land.







9)From an economic standpoint which forest harvesting technique is generally best?







Shelter tree


Selective cutting


Ecologically sustainable forestry

10)Which is NOT a problem with clear-cutting as a means of timber harvest?



The possibility of raising water temperatures


Increased soil erosion


Lack of cover for certain wildlife


Herbicide use prior to replanting


High cost of clear-cutting when compared to other methods

11)Before certain projects can begin in the United States, the project owner must file an EIS. The purpose of this is to



discover whether the project would be well served by multi-use zoning.


force land owners to proceed in the way that will have the least environmental impact.


force land owners to file an environmental mitigation plan.


reduce the likelihood of creating urban sprawl.


force land owners to suggest alternative approaches to the project and assess the environmental impacts of those alternatives.

12)Currently, most of the U.S. population lives in ____ areas.












13)The most common use of land in the United States is





plant agriculture for human consumption.


plant agriculture for animal consumption.




urban landscape.

14)National wildlife refuges are managed primarily for the purpose of protecting wildlife. They are usually managed by











whichever agency managed the land before the refuge was established.

15)The vast majority of wilderness areas in the United States exist in



the southeastern states of South Carolina, Georgia, and Florida.


the midwestern states of Wisconsin, Minnesota, and North Dakota.


Texas and Hawaii.


the western States (California, Oregon, and Washington).



16)Industrial Agriculture has many benefits. Which of the following is NOT a benefit associated with industrial agriculture?



The ratio of energy input to calorie output is low.


There is larger food production per hectare.


Monoculture can be more efficient.


Economy of scale can make the food less expensive.


None of the above

17)Humans started moving from hunter-gatherer societies to more agricultural societies approximately ____ years ago












18)Typically, most of the energy subsidies in modern agriculture are in the form of



solar power.


hydroelectric power.


nuclear power.


fossil fuels.


manual labor.

19)Synthetic fertilizers have many advantages over traditional organic animal waste fertilizers. These include all of the following EXCEPT



ease of application.


highly adjustable nutrient content.


bioavailability of nutrients.


lack of nutrient runoff problems.


highly concentrated when produced.

20)The U.S. accounts for about what percent of worldwide pesticide use?












21)After many years of applying the selective pesticide provironex, a farmer notices that the applications seem less effective. This is likely due to



the fact that provironex is fat soluble and has been bioaccumulating.


provironex is selective, so other pests are filling the niche from the exterminated ones.


provironex is persistent, and the farmer should apply less for better results.


the target species has begun to evolve resistance.


provironex is a wide-spectrum pesticide that needs to be fine-tuned for the target species.

22)A substance that kills invertebrates that feed on crops and breaks down relatively rapidly is called a



persistent herbicide.


nonpersistent herbicide.


persistent insecticide.


nonpersistent insecticide.


pesticide treadmill.

23)Scientists have inserted a gene for the production of vitamin A into rice. This practice of changing the genetic structure of agricultural products to improve desirable traits is known as



genetic engineering.




selective breeding.


natural selection.


animal husbandry.

24)Which of the following statements about sustainable agriculture is NOT true?



Sustainable agriculture is often based on traditional agriculture techniques.


A key component of sustainable agriculture is soil protection.


Sustainable agriculture is more labor intensive than conventional agriculture, and so cost is an issue in areas with high labor costs.


Sustainable agriculture does not take into account the economic viability of the farmer.


Sustainable agriculture uses techniques such as crop rotation, intercropping, and agroforestry to protect soil substrate and nutrients.

25)In order for organic farmers to make a profit (and thus be economically sustainable),



manure (organic fertilizer) must be found in abundance in close proximity to the farm.


the government must continue to provide an organic subsidy.


conventional farmers must pay a tax to subsidize the organic farmers.


consumers must be willing to pay higher costs associated with organic produce.


organic farmers cannot make a profit.


What Is The Largest Tidal Range? narrative essay help

What is the largest tidal range in Figure 3. Boston, Massachusetts?

Figure 3.Boston, Massachusetts tidal data for 11/2013 to 12/2013

Ethical decision making and corporate culture easy essay help

Unit VIII Essay
White Paper
You are an industrial hygienist for a major
pharmaceutical company. The CEO has contacted you regarding a new
product line that will be produced in your facility. The new product involves the handling and use of an engineered
nanomaterial. To date, your companys health and safety program has not ha
d to address any safety concerns associated
with handl
ing and use of these materials.
Using the NIOSH document from the required reading as your authoritative
source (


prepare a three

to five

page white paper that
provides an overview of engineered materials and includes a discussion of the following:

background and industry overview of engineered nanomaterials,

exposure control strategies,

nanotechnology processes
and engineering controls,

hazard control evaluations,

health hazards associated with exposures, and

conclusions and recommendations
As you prepare your paper, keep in mind that this should be a high

level overview that is understandable to all employees
in the organization: from upper management to production workers. All sources used, including your textbook, should be
cited and referenced properly using APA format.

The criteria for including a substance in a HazCom program chemical inventory admission college essay help: admission college essay help

Congratulations! You have just become the safety manager for Podunk University. Your position is at the campus in Podunk, Colorado, and your predecessor left the job a year and a half ago. There has been nobody in the position during that interval. The commitment of the institution to safety is dubious at best, but you are looking forward to starting your new position and making a positive change.

After introducing yourself to the secretary you share with a half dozen other, more senior, people, you decide to focus on hazardous material and hazardous waste issues since you just completed a great college course on those topics. You tour the campus and discover that the following departments and programs are yours to deal with:

The biology department has animal dissection, human dissection, a microbiology lab, and a medical laboratory education program that uses small quantities of a lot of chemicals.
The chemistry department has chemicals that have never been inventoried and a new forensics program (as in CSI, not in college debate).

BOS 3125, Hazardous Materials Management 3


The physics department has high-voltage equipment, lasers, and LEDs.
The English department has lots and lots of books and papers, as well as photocopiers.
The math department has lots of computers and whiteboards.
The automotive technology department has everything pertaining to auto repair, including solvents, asbestos

brake linings, pneumatic tools, waste oil, and cutting and grinding tools.
The Massive Arena is one of the original buildings on campus and has a variety of interesting problems, including

asbestos insulation, and the building is undergoing a massive renovation.

Respond to each of the following questions:

Where do you start?
Where should you focus your initial HazCom efforts? In what order do you tackle the rest of the departments?
What are the HazCom issues in the automotive technology department?
What are the hazardous waste issues in the automotive technology department?
What are the HazCom issues in the chemistry department?
What are the hazardous waste issues in the chemistry department?
With the Massive Arena renovation, who are the people to whom you need to communicate hazards?
What are your main concerns with the physics department?
What are the hazardous material/waste spill response issues for the university, and how should you prepare for

Is any HazCom training needed for the English and math departments?
What are some resources for finding out how to solve the HazCom issues?
You must choose technology or trainers to do the needed training. What are some issues to consider when

selecting these?
Due to budget cuts, you have to do the training yourself, and you will use PowerPoint. What are some

considerations when developing your PowerPoint Presentation?
How can you evaluate your training to ensure that it is accomplishing your goals?
One of the chemistry professors working with some of the automotive technology faculty members, invents a new

nonflammable compound that will render obsolete the need for solvents to degrease auto parts. She wants to

market the stuff. What needs to be done before it can be marketed, and who should do it?
The University decides to partner with the chemistry professor and market this new compound. Due to the lack of

flammability, it is a great hit nationwide. They then decide to market it worldwide. What concerns need to be

It turns out that this wonderful new compound makes a really great explosion when used in conjunction with

another chemical. As the University is manufacturing the compound in large quantities and storing it on the

grounds, what concerns do you now have? What experts should you consult?
The biology department has been busy as well. The little microbiology lab is large now, and they are working with

stronger pathogens. How would you determine the new hazard communication requirements and things that you should do beyond that minimum?

After a tough five years, you have the Podunk University campus running smoothly. Everybody is trained, and your successor will not have nearly as much of a challenge as you did. Congratulations, and best wishes on your next challenge!

Your submission must be a minimum of four pages double-spaced, not including the title and reference pages, and in APA format. Support your answers to the questions with appropriate references and in-text citations.

Brief Proposal of Research of overview of the organization college admission essay help houston tx

Submit a written Brief Proposal of Research containing the following:

A brief overview of the chosen organization and your role in it
A preliminary problem statement in the form of a researchable question
Abrief narrative description of the organizational problem that you would like to research and resolve
Which Terminal Course Objective(s) your problem is related to

Conduct library research on your topic.

Identifya minimum ofsix scholarly resources for your project.
All resources for the paper must come from DeVry Library and must be of scholarly quality.
Use the librarians for assistance in accessing materials.
Review the Using EBSCO tutorial.

Please Note: Articles found online (many on consulting company websites, Internet magazines, or other blogs)will not be considered an acceptable scholarly resource.Conduct your research through a library where you can be assured that the sources are of scholarly quality

Stages of Demographic transition Discussion admission college essay help: admission college essay help

Demographic transition is the process in which a nation transitions from being a less industrialized society, with high birth and death rates, to an industrialized nation, with lower birth and death rates. Many countries have already been through this transition including the United States, England, and Canada.


The demographic transition to an industrialized society is detrimental for the environment because industrialized societies tend to use up nonrenewable resources and give off pollution. Industrialized nations have the largest ecological and carbon footprint relative to developing or nonindustrialized countries. Fortunately, there are some benefits to the process of demographic transition, including lower birth and death rates. Essentially, people in industrialized countries have fewer children and this in turn helps control the overall population size.


Demographic transition involves the following five stages:


Stage 1
High birth rate, high death rate, low population size

Stage 2
High birth rate, decreasing death rate, increasing population size

Stage 3
Decreasing birth rate, decreasing death rate, increasing population size

Stage 4
Low birth rate, low death rate, high population size

Stage 5
Low birth rate, low death rate, population size decreasing


It should be noted that stage 5 is controversial, and it is sometimes not considered to be a stage. This is partially because so few countries are at this stage.


The following graph depicts the various stages of demographic transition:



Using the stages listed above, create a demographic and environmental timeline for one industrialized country, excluding the United States. The following are a few suggested industrialized nations:




You canthe Demographic and Environmental TimelineUnited States of America to review an example of such a timeline.


Include the following points in your timeline in order to examine the advantages and drawbacks of demographic transition in your selected country:


Major historical changes that caused the shift from one stage to another (if available).
Changing population size through time (increasing or decreasing).
Increase or decrease of birth and death rates through timeparticularly when considering the process of industrialization.
Environmental impact of this transition.
Dates (if available), series of events, and scholarly references for these items.


Support your timeline with appropriate examples and a minimum of three credible resources.

Present your timeline in a media that best displays the information you researched. This can be in Microsoft Word, or Microsoft PowerPoint. Apply APA standards to citation of sources


Assignment 2 Grading Criteria

Maximum Points

Created a detailed, demographic and environmental timeline for a selected industrialized country, focusing on the shift from one stage to the next and the environmental consequences that were an outcome of this change.


Supported statements with appropriate examples and at least three credible sources.


Wrote in a clear, concise, and organized manner; demonstrated ethical scholarship in accurate representation and attribution of sources; displayed accurate spelling, grammar, and punctuation.




BOS 4601 Accident Investigation university essay help: university essay help

BOS 4601, Accident Investigation 1

Course Learning Outcomes for Unit VIII Upon completion of this unit, students should be able to:

2. Describe the accident investigation process. 2.1 Identify the key elements of an accident investigation report.

6. Examine the relationship between accident investigation and hazard prevention.


Reading Assignment Chapter 14: Reporting and Follow-up Chapter 15: Learning from Accidents In order to access the resource below, you must first log into the myCSU Student Portal and access the Business Source Complete database within the CSU Online Library. Geller, E. S. (2014). Are you a safety bully? Professional Safety, 59(1), 39-44. Access the resource below, and read Reporting the Results (pp. 2-92 to 2.110): U.S. Department of Energy. (2012). Accident and operational safety analysis: Volume I: Accident analysis

techniques. Retrieved from 2012_VOL1_update_1.pdf


Unit Lesson Accident investigations can take days, weeks, or months to complete, depending on the complexity of the accident and the organizations approach to the accident investigation process. That means a comprehensive investigation takes resources to completeresources that could be used for more productive pursuits. However, if organizations are diligent and implement the corrective actions identified through accident investigation, they will gain in the long run by not having to investigate the same accidents repeatedly. Corrective actions eliminate hazards, and eliminating hazards reduces the probability of accidents. However, cost avoidance is not always easy to sell. Safety practitioners need to keep the cost of accidents visible. Production delays, cleanup, investigation, and training are all significant hidden costs related to accidents, and they should all be tracked. We have said that accident investigation is a reactive process. When we implement corrective actions, the process becomes proactive. Information about accidents and corrective actions should be communicated to all levels of an organization. Organizational managers need to see the cost of accidents, and employees need to see that actions to protect them from injury have been taken. Communicating accident information begins with the accident investigation report. What this report will look like may depend on the organizations philosophy concerning accidents, the seriousness of the accident, or the resources available. The Occupational Safety & Health Administration (OSHA) requires most organizations to keep a log of injuries and illnesses (OSHA, 2001). The OSHA 300 log is a basic description of the who, what, and where of injuries and illnesses. Some organizations expand the OSHA log to include causal factors and corrective actions. Accident forms are reports that contain more room for detail about an accident, but they still follow a fill-in-the-blank format. Logs and forms have their place and are useful for


Reporting and Follow-Up




BOS 4601, Accident Investigation 2



establishing trends or tracking corrective actions, but they are not a substitute for the accident investigation process. A compete accident investigation report should contain all the facts obtained during the investigation, copies of interviews and statements, photographs, discussion of the analytical process used to develop the causal factors and corrective actions, and discussion of all causal factors and proposed corrective actions (Oakley, 2012). In other words, it documents the entire process. Realistically, most organizations do not have the resources to conduct an in-depth investigation for every near miss, minor injury and major injury. They choose to put more resources into the accidents with more serious consequences but require reporting using logs or forms for all accidents, regardless of the severity. Regardless of the format, accident investigation reports have little value if actions are not taken to implement the corrective actions. This is where the process becomes proactive and justifies the resources expended in the investigation. Accident causal factors represent hazards or workplace conditions that may cause illness or injury. They are no different than hazards identified through workplace compliance inspections, job hazard analysis, or risk assessment; therefore, they should become a part of whatever hazard tracking system is in use (presuming there is such a system. If not, the problems go much deeper than accidents). Each corrective action should be clearly assigned to a specific individual or group who is then held accountable for completion of the necessary tasks. Periodic follow-up is necessary to ensure established timelines are met and that the corrective actions are working as intended. Learning from accidents is important. Even if our corrective actions are implemented and are effective in preventing a recurrence, is there anything revealed by the investigation that can be applied to other parts of the organization, even if different processes are involved? Looking back one last time at our accident scenario involving Bob slipping in the water on the floor, we identified that supervisors in the valve department were not aware of their responsibility to submit maintenance requests. Does the same problem exist in other departments? Perhaps the overall preventative maintenance program is inadequate? Likewise, we identified a communication problem between supervisors and employees. Is this an indicator of a systemic problem? It takes practice to become an effective accident investigator. Large organizations may have a team dedicated to accident investigations. There are government agencies, such as the National Transportation Safety Board and the U.S. Chemical Safety Board, whose sole purpose is accident investigation. For most employers, the opportunities to conduct a thorough investigation are not frequent enough to provide the needed practice. On one hand, not having accidents to investigate is a good thing and may indicate the presence of an effective safety program (or a string of good luck). On the other hand, the lack of practice might result in a poor-quality investigation. If we understand the theories of accident causation, there is no reason we cannot apply those theories proactively to reveal potential accident causes. Accident prevention is much more than eliminating hazards from the workplace. It requires an examination of systems and the interactions among workers, equipment, and processes. It is not hard to brainstorm the types of accidents that might happen in a given workplace. Using an imagined accident or an accident that happened in another organization, you can work backwards and examine the conditions that might contribute to such an accident. Applying the various domino theories will help focus on unsafe actions and unsafe conditions or basic and immediate causes. We can use the Haddon matrix to help identify human, equipment, and environment factors. We can use change analysis to identify what alterations in a process or procedure might result in an accident, and we can use barrier analysis to determine if the barriers in place are sufficient and to decipher what might happen should they fail. Fault tree analysis can be used to examine complex processes for potential paths to an accident (Oakley, 2012). Accidents happen. They happen in organizations with no active safety programs, and they happen in organizations with large staffs of credentialed safety professionals. They are elusive because they involve complex interactions of human behavior, equipment, and the environments in which they operate. No one can accurately predict when or where an accident will happen, but we can, and should, learn at least something from every accident. The accident investigation process is the conduit for this learning. The more we learn, the more we can reduce the probability of another accident. We can also be proactive and apply accident theories to identify vulnerabilities in processes and procedures. No one is happy when an accident happens, but each accident should be viewed as a unique opportunity for improvement. Not taking advantage of these opportunities does a great disservice to those workers who were adversely affected. We owe them, their families, and their colleagues our best efforts.




BOS 4601, Accident Investigation 3




Oakley, J. S. (2012). Accident investigation techniques: Basic theories, analytical methods, and applications (2nd ed.). Des Plaines, IL: American Society of Safety Engineers.

Occupational Safety & Health Administration. (2001). 29 CFR 1904.7, general recording criteria. Retrieved



Suggested Reading In order to access the resources below, you must first log into the myCSU Student Portal and access the Business Source Complete database within the CSU Online Library. To reduce the amount of results you receive, it is recommended to search for the article by title and author. The Royal Society for the Prevention of Accidents (RoSPA) believes that there are many opportunities for learning in the field of safety prevention. This article focuses on RoSPAs key theme that understanding accidents is important in preventing them. This article also contains other interesting information about RoSPA and its investigation practices. Bibbings, R. (2010). Learning from accidents. RoSPA Occupational Safety & Health Journal, 40(7), 35-36. The article below explores the Chemical Safety and Hazard Investigation Board and how this organization is trying to prevent chemical accidents. Their five core goals are identified and discussed in the article, as well. Bergeson, L. L. (2006). The Chemical Safety and Hazard Investigation Board: Thinking strategically in

investigating (and preventing) chemical accidents. Environmental Quality Management, 16(2), 81-88.

FIR 4306 Human Behavior in Fire my essay help uk

FIR 4306, Human Behavior in Fire 1

Course Learning Outcomes for Unit VIII Upon completion of this unit, students should be able to:

1. Select research appropriate for training fire safety educators about human factors. 2. Analyze research appropriate for designing a training program for model behavior in fires at a nursing

home. 3. Evaluate research appropriate for learning about the designing fire drill for a college campus

dormitory. 4. Create a presentation using suitable research material to inform an audience of fire behavior issues.


Reading Assignment In order to access the resources below, you must first log into the myCSU Student Portal and access the ABI/Inform Complete database within the CSU Online Library.

Ronchi, E., Reneke, P. A., & Peacock, R. D. (2014, 11). A method for the analysis of behavioural uncertainty

in evacuation modelling. Fire Technology, 50, 1545-1571. Xie, K., Liu, J., Chen, Y., & Chen, Y. (2014). Escape behavior in factory workshop fire emergencies: A multi-

agent simulation. Information Technology and Management, 15(2), 141-149.

Unit Lesson Hannah and Jane are excited to move into their freshman dorm rooms at State University. Like many freshman, they are required to live on campus and have been assigned to one of the older dorms. They have spent their summer planning their dcor. When they arrive at their assigned room on the third floor they are a bit dismayed to realize they have very few outlets in the room. They have brought with them a mini refrigerator and microwave. They have computers, phones, and tablets to help with their studies. They have lamps and alarm clocks. And of course, these girls cannot live without their blow dryers, curling irons, flat irons, and hot wax machines. Jane brought a TV, and Hannah brought her brothers old game system. Although the girls are not big gamers, they hope the video games will attract some of the cute freshman boys from the second floor. The girls did remember to purchase a power strip, but do not want to spend the money to buy any more; instead, they buy a couple of cheap extension cords that do not have built-in breakers. They do not want the ugly power cords to show, so Hannah moves her bed in front of the outlet so that the bed and blankets will cover the cords. What problems do you see with this scenario? As a fire service professional, what responsibilities do you if you fall within State Universitys jurisdiction? Research of fire and human behavior topics is an on-going task for fire service program developers, training officers, and fire inspectors. Fire safety educators conduct traditional and non-traditional programs to internal and external customers. Provided basic information about human behaviors or human factors is included in the programs, these presentations are likely to be much more effective and appeal more to the audience. Many fire inspectors prefer to gain compliance rather than depend on enforcement. Gaining compliance requires the customer to willingly comply with the requirements of the local fire department. Fire training officers being charged with the responsibility of delivering training and education must understand the behavior of their students, whether in the classroom or during hands-on training. Classroom behavior can be difficult to analyze. Depending on the audience, the subject, the instructor, and the purpose of the training, the audience may display a variety of behaviors. Conducting live fire training requires the training officer to constantly observe the behaviors of the students, other instructors, and any observers. Although the girls in the above scenario would probably be concerned about safety, discussing peer pressure of others finding out


Fire and Human Behavior Research




FIR 4306, Human Behavior in Fire 2

how many electrical gadgets they have plugged into their room, and the loss of the money they spent on their dcor if a fire were to break out in their room, might go a long way in deterring these girls from using all of the electronics they have brought with them, or encourage them in purchasing the appropriate surge protectors. Fire investigators are charged with the challenge of determining the cause of fires and sometimes the motive of the fire setter. Determining the cause of the fire may involve interviewing first-arriving fire fighters and fire fighters who battled the fire inside the structure. The fire investigator must analyze the statements of the fire fighters, as well as their behavior, to get an accurate account of the fire fighters experience with the fire. As fire investigators interview suspects and potential witnesses, an understanding of human behavior is a must. Individuals statements may be totally opposite of what the body language in expressing. Unlike other residential occupancies, nursing home residents and workers may present a unique set of behavior during a fire incident. Many of the workers may become attached to the residents and may find it difficult to control emotions during a fire incident. Many of the residents may also be attached to the workers and become more dependent on the workers during fire incident. While some residents are ambulatory, many are confined to a bed. Even the ambulatory residents may not be coherent or maintain emotional control during fire incident. College campus dormitories are always a concern for fire incidents. Many colleges and universities now employ resident assistants or a dormitory manager to serve as the watch person and maintain other duties. The resident assistant is often a college student as well and may lack a maturity level needed to control human behavior during a fire incident. Controlling the behavior also includes monitoring student behaviors and actions to prevent fire incidents. Todays fire service is benefitting from human behavior research. However, tomorrows fire service may be dependent on the research just to accomplish its mission and meet its customers needs.

Suggested Reading Bryan, J. L. (1977). Smoke as a determinant of human behavior in fire situations (project people) (Rep. No.

NBS-GCR-77-94). U.S. Department of Commerce National Bureau of Standards. Retrieved from

Fahy, R. F., & Proulx, G. (1997). Human behavior in the World Trade Center evacuation. In Y. Hasemi (Ed.),

Fire Safety Science Proceedings of the Fifth International Symposium (pp. 713-724). Retrieved from

Keating, J. P., & Loftus, E. F. (1977). Vocal alarm systems for high-rise buildings A case study. Mass

Emergencies, 2, pp. 25-34. Retrieved from Pezoldt, V. J., & Van Cott, H. P. (1978). Arousal from sleep by emergency alarms: Implications from the

Scientific Literature (Rep. No. NBSIR-78-1484 (HEW)). U.S. Department of Commerce National Bureau of Standards. Retrieved from

Proulx, G. (2000). Strategies for ensuring appropriate occupant response to fire alarm signals. Construction

Technology Update, (43)1-6.

Learning Activities (Non-Graded) Go back to your resume that you created/updated in Unit I. Is there additional information you can now add to it based on what you have learned during this course. Add the presentation you created at the end of the course to your resume. Make it a goal to actually present the information to a group in your community. Non-graded Learning Activities are provided to aid students in their course of study. You do not have to submit them. If you have questions, contact your instructor for further guidance and information.

A Method for the Analysis of Behavioural Uncertainty best essay help: best essay help

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, 15451571, 2014

2013 Springer Science+Business Media New York. Manufactured in The United States DOI: 10.1007/s10694-013-0352-7




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- 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 [610] (e.g. dis- 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 questions are left to a qualitative judgment by the evacuation model user. For

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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 models 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 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 optimal number of runsin relation to the evacuation scenario, the model in use, and the scope of the simulationsis 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 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 sn1j 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.

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

* 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 jj x * y* jj jj y* jj


Pn i1xi yi


Pn i1yi




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.

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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).

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 jj y * jj jj x* jj

cos b 2

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*i X


i1 xiyi 3

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 EPC hx * ; y *i

jj y* jj2 Pn

i1xiyi Pn

i1 y 2 i


EPC defines a factor which when multiplied by each data point of the vector y *

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 hx * ; y *i

x *

y *


is1 Dxis Dyis

s2 Dti1 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

Pn is1

Dxis 2 s2 Dti1

Pn is1

Dyis 2 s2 Dti1

q 5

Figure 1. The projection coefficient for two vectors.

Analysis of Behavioural Uncertainty 1549



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;

Dxis xi xis; Dyis yi yis; Dti1 ti ti1:

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.

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 20 40 60 80 100 120

A rb

it ra

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).

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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 *

E1; . . . ;En 6

For example, in the case of i = 3 occupants, i.e., E *

E1;E2;E3 ; E1 is the mea- sured evacuation time corresponding to occupant 1, E2 is the 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 * m1; . . . ;mn 7

Therefore, m * m1;m2;m3 , 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 10 20 30 40 50 60




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 accordance to their evac- uation time, i.e. occupants may evacuate the building in a different order in differ- ent runs.

m *

ij m11; . . . ;mij; . . . ;mqn


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 m11;

m21;m31; m *

i2 m12;m22;m32 ; . . . ; m *

i9 m19;m29;m39 . 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 the values of all runs is represented using an n dimensional vector M


j (see Equation 9), where M1 1n

Pn j1 m1j;M2 1n

Pn j1 m2j; . . . ;Mn 1n

Pn j1 mqj.

M *

j M1; . . . ;Mj; . . . ;Mn


Considering the previous example, i.e. 3 occupants and 9 runs (i = 3 and j = 9),

the average curve M *

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 1;M *

j M1, i.e. the average curve corresponds to the curve of the first run. If 1< j< n, M


j becomes M *

j M1; . . . ;Mj where M1 1j

P1< j < n j1 m1j;M2 1j

P1< j < n j1 m2j; . . . ;Mj 1j

P1< j < n j1 mqj:M


j represents

then the average curve corresponding to 1< j< n runs. Considering 4 vectors m *


corresponding to the predicted evacuation times for three occupants in j = 4 runs out of n = 9 runs, M


4 M1 14 P1< 4< 9

j1 m1j; M2 1j P1< 4< 9

j1 m2j;M3

1 4

P1< 4< 9 j1 m3j: If j = n, M


j becomes M *

n M1; . . . ;Mn where M1 1n Pn

j1 m1j;

M2 1n Pn

j1 m2j; . . . ; Mj 1n Pn

j1 mqj: Thus, M *

j represents the average curve cor-

responding to all j = n runs. For instance, if n = 9 runs, M *

9 M1 1 9

P9 j1 m1j; M2 19

P9 j1 m2jM3 19

P9 j1 m3j.

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

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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.

The jth TETs TETi for n runs of the same scenario simulated with an evacua-

tion model can be represented using the vector TET *

TET1; . . . ; TETn . The arithmetic mean of the TETs for j runs can be expressed using TETavj (see

Equation 10):

TETavj 1




i1 TETi 10

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 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 j TETavj TETavj1

TETavj j 11

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 j TETavp TETavp1

TETavp j 12

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


SD1; . . . ; SDn . 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 j SDj SDavj1

SDj j 13

The last SDconvj value, corresponding to all n runs, is SDconvFIN (see Equation 14).

SDconvFIN j SDavp SDavp1

SDavp j 14

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 *

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,


ERD1;ERD2;ERD3 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

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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 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 jERDj ERDj1j 15

The last ERDconvj value, corresponding to the differences between the latest aver- age curves is ERDconvFIN (see Equation 16).

ERDconvFIN jERDp ERDp1j 16

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 jEPCj EPCj1j 17

EPCconvFIN jEPCp EPCp1j 18

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 jSCj SCj1j 19

Analysis of Behavioural Uncertainty 1555



SCconvFIN jSCp SCp1j 20

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.

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.

[2] Simulate a finite set of n runs of the same evacuation scenario


CONSIDERATIONS The initial number of simulations is

an arbitrary number set by the model user

[4] Compare the convergence units with the acceptance criteria



[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

Figure 6. Schematic flow chart of the proposed evaluation method.

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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 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 criteriawhich may or may not include all convergence measuresmay 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 m11; . . . ;mij; . . . ;mqn

corresponding to the simulated evacuation times of each occupant ith in each jth run are obtained. The occupant-evacuation time curves are produced ranking the occupants in relation to their evacuation time.

The vector corresponding to the consecutive average curves M *

M1; . . . ;Mn 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 judgmentwhich is the current qualitative method adopted by evacuation modellers to estimate the opti- mal number of runsis 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



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 21

Test 2:

SDconvj <TRSD for b consecutive number of runs 22

Test 3:

ERDconvj <TRERD for b consecutive number of runs 23

Test 4:

EPCconvj <TREPC for b consecutive number of runs 24

Test 5:

SCconvj <TRSC for b consecutive number of runs 25

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].

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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 n m S


ij vectors S *

ij 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 Hills [18] pseudo-random generator. The pseudo-random numbers are used 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 predicted outcome of the evacuation model for the same evacuation scenario (see Eqs. 2630). The number of consecutive runs b = 10 for which the acceptance thresholds needs to be accomplished is also defined.

TRTET 0:5% 26

TRSD 5% 27

Analysis of Behavioural Uncertainty 1559



TRERD 1% 28

TREPC 1% 29

TRSC 1% 30

For instance, this means that the acceptance crit

How cultural and literacy issues can impact the effectiveness of a HazCom training program narrative essay help

1)With the adoption of GHS by OSHA, the problems associated with Material Safety Data Sheets (MSDSs) in many different formats will be solved. What other problems with MSDSs are likely to remain despite the standardized formatting?

Your response must be at least 400 words in length.APA Format


2) Discuss how cultural and literacy issues can impact the effectiveness of a HazCom training program. Suggest ways that these issues can be addressed.

Your response must be at least 400 words in length. APA Format