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1.1. INTRODUCTION

Indoor environmental quality (IEQ) of educational buildings not only affects health,

but may also the student learning, staff productivity and attendance. Good housekeeping

protocols that thoroughly removed dust from surfaces was found to have both health and

comfort benefits (Fogarty, 2000), (Wyon, 2000). Knowledge concerning the thermal climate

parameters, their influence on the occupants and the influence of buildings and systems on

these parameters is covered in international standards. When building occupants experience

mild symptoms of distress or discomfort (dry eyes, itchy or watery eyes, dry throat, lethargy,

headaches, chest tightness), they begin to perceive a loss in performance. This performance

loss ranged from 3-8% depending on the number of symptoms (Raw, Roy.M.S, & Leaman,A,

1998). In another study (Wargocki & Wyon, 1999), it was found that the exposure to a

reservoir of dust (an old carpet) had affected subjects’ such as typing, arithmetic, logical,

reasoning, memory, and creative thinking skills by 2-6% (DanTranter:, 2008). While

motivation can overcome small burdens of environmental stress, continued environmental

stress can drain a person’s physical and mental resources, which ultimately can affect

performance. Several factors influencing thermal comfort such as the use of increased air

velocity, humidity, adaptation to higher indoor temperatures during summer in naturally

ventilated (free running) buildings, and a whole-year evaluation of the indoor thermal

environment are now included in the revision of international standards, e.g. ISO EN 7730

(2005) and ASHRAE Standard-55-2004 (2004)

Under the Occupational Safety and Health Act 1984 and the Occupational Safety and

Health Regulations 1996, employers are required to provide working environment in which

employees are not exposed to hazards and thereby maintain atmospheric quality. In few

indoor work places the temperature and air quality may be less than ideal. Faculty and staff

in such places may experience discomfort as a result. These guidelines provide information

about what can be done locally to improve comfort levels in relatively warm or cool

indoor areas whilst acknowledging the environmental and economic impact associated with

the increased ventilation, utilization or non utilization of mechanical heating, cooling and air

conditioning systems. This understanding aids to plan and improve indoor thermal comfort

and ventilation.

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Thermal comfort relates human sensation and perception with a number of

environmental and physical parameters (P.O.Fanger, 1970). It is by definition, the perception

of satisfaction a subject experiences in a given thermal environment (ANSI/ASHRAE55-

2004, 2004). Extensive studies have resulted in a number of thermal comfort equations as

proposed in some widely used design guides and standards (ANSI/ASHRAE55-2004, 2004)

(BSEN-ISO7730, 1995). Three indices have been derived based on Fanger’s comfort

equation: Predicted Mean Vote (PMV), Predicted Percentage Dissatisfied (PPD) and Lowest

Possible Percentage Dissatisfied (LPPD). The PMV and PPD indices are comparatively

common in practical applications (Fanger.P.O. 2002) (Han, Yang,W, Zhon,j, Zhang,G,

Zhang,Q, & Moschandreas,D,J, 2009). The former predicts the mean thermal comfort votes

among a large group of people; the latter, a quantitative measure of the number of thermally

dissatisfied persons in a group under particular thermal conditions. Field studies on the

thermal comfort of occupants working in an air-conditioned environment can be used to

examine the neutral temperature—a temperature associated with a neutral thermal sensation

(Oseland.N.A, 1995) (Fanger, 1995) (Mui,K.M & Wong,L.T, 2007). This temperature is a

key factor for selecting an appropriate air temperature set point for an indoor thermal

environment (Wong,L.T, Mui,K.W, Fong,N.K, & Hui,P.S, 2007). As climate, besides

occupant factors including lifestyle, economic status and adaptive behaviour, plays an

important role in affecting the indoor thermal environment (Yoshino, et al., 2006)

(Brager,G.S & Dedear,R.J, 1998), neutral temperatures for different climatic zones have been

studied (Mui,K.M & Wong,L.T, 2007), (Wang, 2005). In all the above work, the thermal

comfort and the indoor environmental quality is analysed but not optimized.

Thus there is a need for this study seeking to fill this gap by providing empirical

thermal comfort data from Karunya University which is situated near Siruvani hills of

Coimbatore in Tamil Nadu. Data for Occupants’ perception about the present indoor

Environment were collected by four different types of questionnaire and the required

parameter values collected using various instruments.

Questionnaires were prepared to collect the data and the informants were asked to

provide their personal details like name, age, sex and the number of years of their living in

this place and other details pertaining to the study of thermal comfort and indoor

environmental quality. The questionnaire contained dichotomous assessment scale; when a

question has two possible responses, it is called dichotomous. This scale was used for a

direct feedback of acceptability with the question ‘Is the thermal environment / indoor air

quality/noise level/illumination level being perceived in the office and residence environment

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acceptable to you?’ being asked. The ranks ‘(1) Yes, acceptable’ and ‘(0) No, not acceptable’

were self-explanatory. In order to confirm the validity of their responses, each respondent had

to use a semantic differential evaluation scale for the subjective assessment of the first two

aspects, and a visual analogue assessment scale for the evaluation of the aural and visual

comfort. At the end of the survey, occupants’ present status was identified.

A Visual Analogue Scale (VAS) is a tool that tries to measure a characteristic or

attitude that is believed to range across a continuum of values and cannot easily be directly

measured. Operationally a VAS is usually a horizontal line, 100 mm in length, anchored by

word descriptors at each end. The patient marks on the line the point that they feel represents

their perception of their current state. The VAS score was determined by measuring in

millimeters from the left hand end of the line to the point that the patient had marked.

Natural ventilation is a ventilation strategy that uses wind, heat and pressure

differential to move fresh outside air through an interior space. Using operable windows as

well as other façade elements to provide ventilation air is an intuitive manner of improving

the atmosphere within built environment. There are several ways to provide natural

ventilation and minimize the use of mechanical force, thereby conserving electrical energy.

1.2. SIGNIFICANCE OF THE PROJECT

1.2.1. Thermal comfort

Thermal comfort for a human being is one of the major problems in present. Thermal

comfort is important both for one's well-being and for productivity. It can be achieved only

when the air temperature, humidity and air movement are within the specified range often

referred to as the "comfort zone". To have "thermal comfort" means that a person wearing a

normal amount of clothing feels neither too cold nor too warm. Providing thermal comfort in

buildings is really a challenging task. The environmental parameters that constitute the

thermal environment are: temperature (air, radiant, surface), humidity, air velocity and

personal parameters (clothing together with activity level), these entire six variables play a

major role in providing thermal comfort. A criterion for an acceptable thermal climate is

dependent on PMV-PPD index.

Temperature preferences vary greatly among individuals and there is no one

temperature that can satisfy everyone. Nevertheless, an office which is too warm makes its

occupants feel tired; on the other hand, one that is too cold causes the occupants' attention to

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drift, making them restless and easily distracted. Maintaining constant thermal conditions in

the offices is important. Even minor deviation from comfort may be stressful and affect

performance and safety. Workers already under stress are less tolerant of uncomfortable

conditions. Many large-scale building studies show that increased volumes of outdoor air,

natural ventilation, air ventilation rates, and filtration of air and improved cleaning and

maintenance of systems are correlated with reduced sick building syndrome (SBS) symptoms

(Brightman & Moss, 2000)..Lawrence Berkeley National Laboratory, Berkeley,. Performance

assessments in work settings are rare because of the difficulty of capturing actual

performance measures and linking them to specific environmental features. Nonetheless, a

field experiment in Denmark shows that workers performed better on a typing task and

perceived themselves as able to think more clearly with increased vent (Wargocki, Wyon, &

Fanger, 2000). A field experiment looked at the link between SBS symptoms associated with

ventilation rates and work performance. They found that workers reporting SBS symptoms

worked 7.2% more slowly on a vigilance task and made 30% more errors on a numerical

task. Elevated temperatures are associated with increases in illness symptoms (Wyon, 1996).

As researchers systematically increased indoor temperatures from 68ºF to 76.2ºF, they found

increased incidents of headache and other SBS symptoms. Wyon (1996) also reports

increased incidence of headache and fatigue as indoor temperatures increase from 68o

F to

76o

F. At 76o

F, 60% of the workers experienced headache compared with 10% at 68o

F. A

field study of lighting conditions in a government office building in England found that

headache incidence decreased significantly with increased access to daylight (Wilkins &

Grois, 1989). People who suffer from Seasonal Affective Disorder (SAD) may also benefit

from access to daylight. Because people with SAD prefer more brightly lighted spaces than

people who do not suffer from seasonal variation in mood and well-being, being adjacent to a

window where light levels are higher than interior spaces may have therapeutic effects

(Heerwagen, 1990).

1.2.2. PMV – PPD Index

Predicted mean vote (PMV): An index that predicts the mean value of the votes of a large

group of persons on the seven point thermal sensation scale. PMV model uses heat balance

principles to relate the six key factors for thermal comfort to the average response of

people on a seven point scale.

Predicted percentage of dissatisfied (PPD): An index that establishes a quantitative

prediction of the percentage of thermally dissatisfied people determined as a function of

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PMV.In general thermal comfort can be calculated by an equation called Fanger’s ‘Predicted

Mean Vote’ (PMV) given by Fanger and this equation gives the optimal thermal comfort for

any environment

Thermal comfort is vogue and cannot get any crisp values, so a new approach based

on fuzzy logic is introduced to estimate the thermal comfort level depending on the state of

the following six variables: the air temperature, the mean radiant temperature, the relative

humidity, the air velocity, the activity level of occupants and their clothing insulation.

PMV= (0.028 + 0.3033e-0.036m

). {M-W} – 0.000699(M-W) - Pa] –

0.42[(M-W) – 58.15] – 0.0173M (5.867 – PA) – 0.0014M (34 – Ta) –

3.96.10-8

fcl [(Tcl + 273)4 – (Tmrt + 273)

4] – fcl. hc (Tcl –Ta)}

Tcl = 35.7 – 0.28(M – W)-0.155Icl [3.9610-3

fcl [(Tcl + 2273)4 - (Tmrt + 273)

4]

- fcl.hc (Tcl – Ta)

hc= 2.38(Tcl-Ta)0.25

for 2.38(Tcl –Ta)0.25

≥12.1 √vair

12.1 √vair for 2.38(Tcl –Ta) 0.25

≤ 12.1 √vair

The parameters are defined as follows:

PMV: Predicted mean vote.

M: Metabolism (W/m2).

W: External work, equal to zero for most activity (W/ m2).

Icl: Thermal resistance of clothing (Clo).

Fcl: Ratio of body’s surface area when fully clothed to body’s surface area when

nude.

Ta: Air temperature (0C).

Tmrt: Mean radiant temperature (0C).

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Vair: Relative air velocity (m/s).

Pa: Partial water vapour pressure (Pa).

hc: Convection heat transfer coefficient (W/m2 k)

Tcl: Surface temperature of clothing (0C).

New fuzzy thermal sensation index is calculated implicitly as the consequence of

linguistic rules that describe human’s comfort level as the result of the interaction of the

environmental variables with the occupant’s personal parameters. The fuzzy comfort model is

deduced on the basis of learning Fanger’s ‘Predicted Mean Vote’ (PMV) equation. The new

fuzzy PMV calculation does not require an iterative solution like Fanger’s PMV and can be

easily adjusted depending on the specific thermal sensation of users.

For most thermal parameters it has been possible to establish a relationship between

the parameter and PPD. People may be dissatisfied due to general thermal comfort and/or

local thermal comfort parameters.

1.2.3. ASHRAE Thermal Sensation Scale

The ASHRAE thermal sensation scale, which was developed for use in quantifying

people's thermal sensation, is defined as follows

Table 1.1 − The meanings of the 7-point thermal sensation scale

It is based on the assumption that people voting +2, +3, –2, or –3 on the thermal sensation

scale are dissatisfied, and the simplification that PPD is symmetric around a neutral PMV.

Predicted Percentage of Dissatisfied (PPD) <10%

Predicted Mean Vote (PMV) >-0.5 to <+0.5

−3 Cold

−2 Cool

−1 Slightly cool

0 Neutral

1 Slightly warm

2 Warm

3 Hot

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This defines the recommended PPD and PMV range for typical applications

PMV equation and a fitted curve to Fanger’s PPD data, is illustrated in Figure 2.1., have been

widely accepted by ASHRAE and ISO as a standard for the thermal satisfaction of human

occupancy. If the resulting PMV value generated by the model is within the recommended

range, the conditions are within the comfort zone.

Figure 1.1 – Predicted Percentage of Dissatisfied as a function of Predicted Mean Vote

1.2.4. Indoor environmental quality

The overall IEQ acceptance for an office and resident environment perceived by an occupant

expressed by a multivariate logistic regression model was proposed by (Wong, Mui,K.W, &

Hui,P.S, 2008) (Lai,A.C.K, Mui,K.W, Wong,L.T, & Law,L.Y, 2009). A range of acceptance

in typical office and resident environmental conditions and its dependence on the four

parameters are determined. The proposed overall IEQ acceptance can be used as a

quantitative assessment criterion for these environments.

The overall IEQ acceptance (φ0) can be expressed by a multivariate logistic regression model

as shown below

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where C0,0 and Ci,0 are the regression constants which can be determined from field

measurements, φi is the occupant acceptance correlated with the thermal sensation vote ξ1 ,

the Co2 concentration ξ2 (ppm), equivalent sound pressure level ξ3 (dBA)and the horizontal

illumination equivalent sound pressure level ξ4 (lux)

The thermal environment acceptance Ø1(ξ1) is given with regression coefficients C0,0 and Ci,0

The acceptance Ø2(ξ2), Ø3(ξ3) and Ø4(ξ4) are expressed by logistic regression models with

regression coefficients C0,j and C1,j , where the Logistic regression models is explained in the

chapter II.

The report based on the survey summarizes a project on the health by optimizing the Thermal

comfort and indoor environmental quality of an office and residential building by using 4

different types of questionnaires among faculty working in the University and also from

people staying in the university quarters addressing the quality of the environment.

1.3. COIMBATORE CITY:

Coimbatore City is one of the top 10 fastest growing cities of India. Coimbatore district has a

population of about 42.25 Lakhs (Census in 2001).

Coimbatore is also a district capital. There are more than

25,000 small, medium, large scale industries and textile mills.

Coimbatore District has six taluks and two revenue divisions

of Coimbatore and Pollachi.

Coimbatore has a well-developed educational infrastructure,

with 7 Universities, 2 medical colleges and over 54

Engineering Colleges and 70 Arts and Science colleges

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(www.wikipedia.com). The city is also a major health care and an emerging medical tourism

destination with many super specialty hospitals and as many as 750 hospitals and medical

centers in total. The hill stations of Ooty, Coonoor and Valparai are close to the city, making

it a good tourist attraction throughout the year. The city is situated about 500 kilometers

(311 miles) southwest of state capital Chennai, on the banks of the Noyyal River and is close

to the Siruvani Waterfalls.

1.4. ABOUT KARUNYA UNIVERSITY

Karunya University (declared under section 3 of the UGC Act, 1956 Vide Notification No. 9-

3-2000-U3 dated. 26.6.2004 of the Govt. of India) was founded in 1986. Located about 30

Kms drive from Coimbatore city in a picturesque 700-acre campus in Western Ghates

Siruvani foot hills. Today, more than 8500 students (both boys and girls) study in

Karunya, hailing from various parts of the country and abroad. The University recognizes the

need for a friendly residential atmosphere for the students

Fig 1.2. The landscape of office buildings of Karunya University

Karunya University has ranked seventh in infrastructure in all over India. The weather

is cool and breezy.

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Karunya is thus set in a natural environment ideal for a residential institution.

Fig.1.3. The landscape of Residential buildings of Karunya University

The purpose of this study is to evaluate the thermal comfort and Indoor environmental

quality of both office and residential buildings of the Karunya University, whether it is

optimum or within the acceptable range. If it is not in the acceptable range, suggestions can

be given for better thermal comfort.

1.5. DATA COLLECTION:

The basic study is in the office rooms of the faculty working in the Karunya

University and faculty staying on the campus quarters. Four different questionnaires were

for Thermal comfort in the office and residences and Indoor environmental quality for office

and residences. The office consists of faculty belonging to

Mechanical and Aero space,

ECE and media,

EEE and EIE,

BIO Tech, Bio Info and Food processing,

Science and Humanities

Civil,

MBA,

and CST

Departments of the Karunya University.

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The residences were Alpha, Bethel, Elim, Cannan, Camel, Kidron, Pat Robertson,

Hebron, Frankinson, Sinai and Tabor

Table 1.2. Details about the department, residence and area

Office Area(m2) Residence Area

MECHANICAL 233.3 ALPHA 726.87

ECE/ MEDIA 684.64 BETHEL 628.55

EIE/EEE 285.6 ELIM 330

BIO TECH, BIO INFO AND

FOOD PROCESSING 726.57

CANNAN 429.27

CST 629.69 CARMEL 1053.46

S & H 947.77 KIDRON 885.24

MBA 291.02 PAT ROBERTSON 616.54

CIVIL 296.2 FRANKINSON 833

HEBRON 538.37

SINAI 1053.6

TABOR 1336.17

This document summarizes a review of office and residential building of our University

with 220 from office and 102 people from our residences. The presented information is

drawn from expert contributions and discussions.

1.6. OBJECTIVES

The objectives identified are:

a) To observe and analyze the existing environmental quality of offices and residential

buildings in Karunya University with a focus on thermal comfort and indoor

environmental quality due to natural ventilation.

b) To optimize the encoded values of variables obtained from Karunya University using

fuzzy and nontraditional optimization techniques.

c) To analyse and arrive at an effective optimization technique that could yield an

appropriate indoor environmental variable value for optimum comfort. The variables

that the indoor environmental quality depends on are the following

Thermal comfort

Indoor air temperature or carbon dioxide

Lighting

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sound

Thermal comfort is dependent on the combination of environmental and personal factors.

Environmental factors include:

o air temperature

o humidity

o radiant temperature

o air movement

o level of activity

o clothing worn

Individual responses also vary depending on:

o perception

o physical fitness

o clothing

o occupant’s activity level

o Acclimatization.

1.7. CHOSEN OPTIMIZATION TECHNIQUES

There are many optimization techniques available both traditional and non-traditional. But in

this study only non-traditional optimization techniques are considered. The traditional

optimization techniques fail to converge on a feasible solution in many cases

(Prof.Dr.B.V.Babu, 1992). The Non-traditional optimization techniques differ from the

conventional traditional optimization techniques in that it produces optimal results in a short

period of time (Kokilavani,T & Dr.George Amalarethinam, 2010). Most of the traditional

optimization techniques based on gradient methods have the possibility of getting trapped at

local optimum depending upon the degree of non-linearity (Prof.Dr.B.V.Babu, 1992). Hence,

these traditional optimization techniques do not ensure global optimum and also have limited

applications. The problems considered are Non Linear optimization which is single objective

where the constraints are only bounds for environmental variables and the problems are

continuous. So the following non-traditional optimization methods are used to solve the

problems, as these techniques are used widely in technical computation.

(www.mathworks.com)

The methods are

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1. Genetic algorithm

2. Simulated annealing

3. Pattern search

4. Particle swarm optimization

5. GODLIKE- hybrid

6. Fmincon (Non Linear optimization)

7. Differential evolution (Numerical optimization method)

8. Lipschitz global optimisation (LGO)

9. glcCluster

10. glcSolve

In these problems to find the optimum value of thermal comfort and indoor environmental

quality of a building, ten solvers are used. The characteristics lead to different solutions and

run times. The results are compared on various bases and appropriate solver is identified.

1.8. DATA PROCESSING AND ANALYSIS

The collected data was processed and classified on the basis of the thermal comfort

and indoor environmental quality.

The programs were coded using MATLAB with TOMLAB programming and all the

experiments executed in a 2.2 GHz Intel laptop machine. The performances of the

techniques were compared and results were analysed.

1.9. LIMITATION OF THE STUDY

As the present study has its wide range of application, the area is restricted only to

faculty working in office and staying in the quarters alone.

1.10. METHODLOGY

The performance of the non-traditional algorithms will vary for every run and it is

assured that the solution is global optimum. So for every problem twenty trial runs were

performed in all the algorithms and the average value of the solution was obtained from all

the trials (Elbeltagi, Tarek Hegazy, & Grierson, (2005) ).

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1.11. DIVISION OF THE RESEARCH WORK

The rest of this dissertation is organized as follows:

Chapter II gives brief description of the Multivariate Logistic Regression mathematical

model. A detailed and descriptive account of the optimization techniques methods are fuzzy

logic, Genetic algorithm, Simulated annealing, Pattern Search, Particle swarm optimization

and a hybrid algorithm known as GODLIKE algorithm and Non-Linear optimization,

Differential Evolution, and solvers like LGO ( Lipschitz-Continuous Global Optimizer),

glcCluster and glcSolve which are based on Direct algorithm.

Chapter III gives the available literature on Thermal fuzzy, Thermal comfort of office and

residential buildings separately. Summarizes the evaluation thermal comfort using Fuzzy

logic technique and optimizing both thermal comfort of office and residential building using

the optimization techniques described in Chapter II. For every problem twenty trial runs

were performed in all the algorithms and the average value of the solution was obtained

from all the trials. All the results are tabulated with the comparative graph.

The PMV index which is found using fuzzy PMV varies from -3 to +3 as Fanger’s PMV

index without any iteration. It will be acceptable only when PMV lies between -0.5 and +0.5

(ANSI/ASHRAE55-2004, 2004). The bibliography used for the thermal comfort is attached.

Chapter IV gives the available literature on Indoor environmental quality of office and

residential buildings. The equations are derived using multivariate logistic regression model

and optimized using the nontraditional optimization methods described in Chapter II. For

every problem twenty trial runs were performed in all the algorithms and the average value

of the solution is obtained from all the trials. All the results are tabulated and the solutions

are compared in a graph. The bibliography referred for IEQ is attached.

Chapter V presents the consolidated optimized results for both thermal and indoor

environmental quality of the University buildings. The entire coding of the optimization

methods for different problems is furnished in the Annexure.

1.12. Future scope of work

While there are many research needs related to the subjects discussed here, the following are

suggested as high priority research needs which can be taken as future work

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In continuation, optimization of thermal comfort and indoor environmental quality of

class room and hostels for students of Karunya University could be calculated using

questionnaire and checked whether it is optimum otherwise improvement can be

suggested.

The relationship between performance of the employees and students of the

University and indoor environmental quality should be assessed.

1.13. Conclusion

(i) Thermal comfort or sensation (PMV) is considered to be acceptable if the value lies

between -0.5 to + 0.5. In the experiment conducted using fuzzy logic and ten non-

traditional optimization techniques the thermal sensation takes the value 0.129 and -0.5

respectively. Hence the thermal comfort of the office and residential buildings of Karunya

University is found to be optimum. The percentage of people dissatisfied (PPD) should be

less than 10. The experiment shows that the PPD is 5 in all the optimization techniques

and 5.3447 in the fuzzy logic. The indoor environmental quality of a building is

acceptable if its value is 1. In the problem of both office and residence, the IEQ value is

1. From the above we can conclude that the thermal sensation, the percentage of people

dissatisfied and indoor environmental quality is within the acceptable range. All the

buildings are only naturally ventilated. So the natural ventilation is more than sufficient to

maintain good health of the inmates who are working and living here in the Karunya

University. It is concluded that among the ten algorithms, the appropriate algorithm, for

optimization of thermal comfort as well as IEQ, is Direct search algorithm & the solver

is PATTERN SEARCH.

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