Utilization of Timetable Management System to a Medium Scaled University

w w w . i j m r e t . o r g Page 8
International Journal of Modern Research in Engineering and Technology (IJMRET)
www.ijmret.org Volume 1 Issue 2 ǁ July 2016.
Utilization of Timetable Management System to a Medium Scaled
University
ChayaAndradi, SamindaPremaratne
(Faculty of IT, University of Moratuwa, Sri Lanka)
ABSTRACT : University timetable construction is hardworking and complicated task when there are large
number of course arrays and limited resources. As a result, universities and some institutes tend to solve this
issue manually even; the results may not always fully optimal. In this paper, we discuss about a framework of
utilizing timetable management system to a medium scale university for resource optimization. Our endeavor
through the overall research was to develop an automated timetable management system to the faculty of IT at
university of Moratuwa to overcome the mentioned scheduling issues. We conducted a preliminary study and
hypothesized it can be achieved by using Genetic Algorithm. In the solution, each individual called chromosome
and it was evaluated using a fitness function in the implementation process. Five great Chromosomes with
higher fitness value considered as optimal solution or timetable schedules. The timetable administrator can
further refine the most suitable timetable. Tools such as PHP, Yii with MVC architecture and MYSQL were used
in this system. Finally, this system was tested and evaluated in the university background and we suggest this
framework is more desirable for medium scale universities.
KEYWORDS -Fitness, Genetic Algorithm, medium scale, optimal, Timetable Management System, utilize
I. INTRODUCTION
Constructing error free timetable is a
strenuous and complex task for academic institutes
such as universities [1],[2],[3]. Every academic year,
faculty of IT at the University of Moratuwa faces
this rigorous task of preparing timetables. Although
the existing manually operated, timetable system is
efficient enough to carry out the courses without
clashes, it is very time consuming and resource
optimization problems occur due to insufficient lab
resources and hall facilities. As a result, University
identified the necessity of an automated timetable
management system.
Current timetable management system with
graph coloring heuristic technique[1] is efficient
enough to carry out the courses without clashes
manually. Nevertheless, problems occur due to
insufficient lab resources and hall facilities. The
problem is more complex when some batches have
more than three hundred students while the largest
hall can be allocated only two hundred and twenty
two students. As a result, this research directed to
resolve that real problems of an application
distributing the courses and labs without collisions.
The main goal was to develop a web based
Timetable Management System to optimize the
resources of the IT faculty and introduce it as a
scheduling framework for middle scaled universities.
Moreover, investigate the available lab capacity and
required resources, studying number of scheduling
algorithms, conduct a comparative study of Genetic
Algorithm used in other timetabling problems, study
the development technologies for the automated
timetabling, develop an automated web based
timetable management system were main objectives
of this research. Finally, the system shall be
automatically generate timetables and used as a
framework to solve the problem of resources
optimization of IT faculty at the university. In
particular, TMSFIT (Timetable Management System
at Faculty of IT) was developed. This new system
shall be providing the facilities of viewing the hall
reservation information on the availability of the
halls and laboratories by admin. Lectures and
students must register through the TMSFIT before
they start using the system. Hence, the security is
very high, only admin of TMSFIT can update the
timetable. There is an authenticating using the users
passwords. The students of the other faculties cannot
allow accessing the system.
Next section will review the related work,
which provides context regarding issues of
timetabling, certain algorithms and some more.
Then, we describe methodology which contains the
theory, design and implementation of the system.
After that, we discuss about the system evaluation
and its uses as a framework under discussion.
Finally, its limitation and further work are reviewed
in the conclusion.

II. RELATED WORK
According to DilipDatta and coworkers,
preparation of timetable for specific university is a
very complex task[2]. Therefore, they could
introduce multi objective Evolutionary Algorithm
based class timetable optimizer to reduce time.On
the other hand, Jonathan Lee and coworkers could
address some of the key challenges of timetabling by
an automatic software engineering process as task–
based conceptual graph (TBCG) [3]. There hard and
soft constraints can be easily inserted or removed
while the specifications are maintainable. However,
there were some drawbacks as necessity of
generalized methodology, specialists’ skills while
the problems are varying by concerning type of
institute. Further, AnujaChowdhary and his
colleagues also introduced an automated timetabling
system of handling soft and hard constraints wisely
with the limitations of mentioning the logic of the
system[4]. In a different research, NelishiaPillay
says even though there are number of researches
found in timetabling few of them only developed as
software[5].Their paper provides an overview of
methodologies such as Bee algorithm, Constraint
programming, Cyclic transfers, Evolutionary
algorithms, Integer programming, Neural networks,
Simulated annealing and so on. Yet another
research, Edmund Bruke and coworkers, could
compare and contrast some recent approaches of
scheduling problems handled by the University of
Nottingham [6]. As a result, they identified many
present effective university-timetabling systems
customized by the desired university and recent
research directions in automated timetabling.
Another aspect of automatic timetabling is defining
constraints. Ben Peachter and his colleagues could
introduce two major concepts behind them in their
research[7].
However, accomplishing the algorithm
construction phase of our system was most crucial
factor. Because, none of the above mentioned logics
matched with our requirements. Eventually, we
directed our research path through the Genetic
Algorithm.
2.1 Use of Genetic Algorithm
Alberto and coworkers used genetic
algorithm in their research[8]. They have presented a
model, a class of algorithms and a computing
program for the timetable problem, with special
reference to a real world application (the timetable
of an Italian high school). Further, they have
compared that GA-based approach with various
versions of simulated annealing and tabu search by
Hooshmand[9]. Finally, they conclude their
experiments as GAs produced better timetables than
simulated annealing, but slightly worse timetables
than tabu search. An advantage of GAs over both SA
and TS is that GAs gives the user the flexibility of
choosing within a set of different timetables. Finally,
they were identified their approach is a useful
generalization of the GA and can be applied to other
highly constrained combinatorial optimization
problems. In a different case, Moreira could
introduce a solution for problems of constructing
timetables for exams using GA[10]. According to
Branimir and colleagues, used GA in a different
manner as algorithm performance was significantly
enhanced with modification of basic genetic
operators, which restrain the creation of new
conflicts in the individual.
In view of Professor AshokaKarunanada,
applications of GA are miracles in new
technology[11]. In book of Artificial Intelligence, he
has mentioned, when there is a necessity of some
optimal solution such as timetabling, GA is
applicable. Further, some data mining issues without
having any solution and lottery games with
probabilistic theory also use this algorithm. The
major disadvantage of the GA is when the
population is large the algorithm execution time also
increasing.Chiu-Hung Chen and team workers
supplies evidence with the useGA for solving
multimodal manufacturing optimization problems
[12]in the field of Manufacturing Robots. Creating
and maintaining timetables is often a complex task
for both people and software. When consider a
Mimosa like commercial application, the technical
side of Mimosa is kept as simple and as self-
contained as possible. The technology is based on a
collection of efficient optimization algorithms[13].
Moreover, some other semi-automatic timetabling
software such as Open Course Timetabler[14]also
free stand-alone application.
2.2 Limitations of GA
GA itself takes long time to be executed
and requires a certain machine configuration. This
can be a problem for time execution.The second
limit of the algorithm is the importance of the
random part. Due to a huge set of solutions, the
algorithm cannot guaranty to get the best result or
the achievement of a certain level of fitness.

2.3 Problem Domain
Although there has been fair amount of
researches about timetabling, few of them only
considered the issues of university timetabling.
According to NelishiaPillay, there is no comparative
study on the success of different methodologies on
timetabling problems[5]. The complexity of the
timetabling is another issue. Manual scheduling
generally takes number of weeks to generate
timetables. Even today, there are many semi-
automatic applications developed such as Mimosa,
Time Tabler, still do not solve the whole
problem[15]. The increasing number of students and
the courses of universities also should take as an
issue of timetabling[16]. Another problem occurs
due to variation of constraints from one institution to
another.
2.4 Technology obtained
Before the literature survey we had two
options of directing this research path as whether use
a rulebased system or use any algorithm such as GA.
Finally, concerning the Literature review, Genetic
Algorithm and some other free softwarewere
selected to implement the timetabling problem of
Faculty at IT the University of Moratuwa.Apache
web server, MySql Database Management System,
PHP and Yii with MVC architecture were
compatible with each other.
III. METHODOLOGY
This section describes the approach, design
and implementation of the TMSFIT as a framework.
3.1 Existing Timetabling System
Usually, the courses which are going to be
offered from the faculty are approved by a senate.
The lecturers in each department wish to specify
preferred time on their courses. All the courses and
course details must be given to admin of the
timetable of university who is having the
responsibility of creating near optimal timetables,
which would serve as a guide for academic activities
in the university. Timetable admin calls a meeting
and prepares a general timetable to fetch preferred
time slots from lecturers. The traditional manual
timetabling system as Fig. 1.is very time-consuming
and resource-intensive. Existingtimetabling process
contains many steps and requires re-processing and
data redundancy.
3.2 Proposed solution
Proposed solution is a website and it works
as an alternative to the current timetable
management system. We used Milestone approach
as our research methodology. As the initial step,
proper investigation could be launched about the
current timetabling system with the interviews of
timetable administrator, lecturers and students. As a
result, pros and cons of that system could be clearly
analyzed. Then, we conducted an appropriate
literature survey of others work with referred to this
subject to make an improved problem definition,
find out technology to be used and solution.
Afterwards, overall research design was constructed.
Then, our TMSFIT was developed using several
tools such as PHP, MYSQL, Yii, Wamp Server and
some more. Consequently, we implemented,
deployed and evaluated that new system using
campus promises.
3.2.1 TMSFIT
TMSFIT is an abbreviation for Timetable
Management System in Faculty of IT. This new
system will provide the facilities for the hall
reservation information on the availability of the
halls laboratories in the admins module. Lectures
and students must register through the TMSFIT
before they start using the system. Hence, the
security is very high, only admin TMSFIT can
update the timetable. There will be an authenticating
using the users passwords. The students of the other
faculties cannot allow accessing the system.Key
inputs of the system are as follows.
 The system is able to take number of inputs
from the user (Admin TMSFIT) such as Student
Fig.1. Existing System

list, Lecture list, Course list, Semester list, Hall
list, Laboratory list and Timeslots.
 Various and constraints such as lecturer
preferred time using web based forms.
Key outputs of the system are as follows.
 Display the generated timetable for a specific
semester.
 Printable timetables
 Web based system will show the availability of
the resources such as labs and courses.
Some features of the existing system are improved
and some are very significant to the proposed system
as follows.
 This proposed system provides an attractive
graphical front-end and it is the main interaction
point with user.
 The system also improves the flexibility of
timetable construction.
 It will be able to generate printouts on
timetabling.
 Upgraded versions of the timetable management
system must be introduced
 To increase the optimization, generated
timetables can be fine-tuned
 The system should save the time.
3.2.2 Application of Genetic Algorithms in
This Research
The basic technology of the timetable
problem is the use of the genetic algorithm to
optimize a function over a discrete structure with
many independent variables. Even though the
timetabling problem is treated as an optimization
problem, there is actually no fixed objective
function. Therefore, GA can be used construction of
semester based course timetables developed for the
University of Moratuwa. The genetic algorithm
employed combines two heuristic algorithms, the
first finding a non-conflicting set of courses and the
second assigning the selected course to halls and
labs. The process is repeated until 500 loops and all
courses have been scheduled with minimum
conflicts. GA can quickly produce large populations
of random feasible course timetables. Uniquely, the
process takes each subject of the batch population
and assigns it to the hall or a lab. The mutation and
crossover procedures will then be applied to the
population. The Fig.2 will illustrate the process of
Genetic Algorithm.
3.2.3 Technology Implementation
Due to available resources and the necessity
of a web based automated system by campus; we
used PHP server scripting language for coding
process. Moreover, Yii PHP framework with MVC
architecture was used to develop the system as its
ability of high performance and maintainability
features. Since, MYSQL database management
system is supportive for PHP; we used it as our
RDBMS. Further, since WAMP server contains
PHP, MYSQL and Apache web server, we used it as
our local host.Eclipse for PHP plugging was
successfully used in this research to modify the code
with regards toits professional Integrated
Development Environment (IDE).Basically, this
TMSFIT was developed and installed in a personal
computer with 2GB RAM, 2GHz or more processing
power, 500GB Hard-disk and more.
3.2.4 Hard Constraints
Hard constraints (which can’t be violated)
were used to calculate the fitness value.If breaks one
of the hard constraints the schedule is infeasible.
They are as below.
 Room Overlap – Check if there are two lectures
in one room
 Room not enough – No of students of a class is
> seats of room
 Required resource not available - Does the lab
have required no of Computers?
Start
End
Input data
Selection, Crossover, Mutation
Initial population generation
Evaluation
Fatal
Condition
Yes
No
Fig.2. Process of Genetic Algorithm

 Lecturer Overlap – One lecturer can’t be in two
rooms at the same time
 Student Overlap - One student can’t be in two
rooms at the same time
3.3 Design of the TMSFIT
We will be discussing here, what the
TMSFIT does and what are the relationships among
each module or level.
The system design was categorized as First level,
Second level and Third level as shown in Top level
design diagram in Fig. 3as below.
3.3.1 First Level Module
This contains sub modules of Admin,
Lecturer, Degree (course), Student, Subject,
Resources and the batch. Those are in ttms database.
It interacts with Timetabling Enginewhich generates
timetables.
3.3.2 Timetabling Engine
The timetabling engine is primarily a web
server which connects the database. It should
maintain admin profile, student profile, lecturer
profile, process queries; prepare outputs in various
formats and so on. This is also responsible for
accuracy and up-to-date information in the database.
It is basically designed for maintain the system
integrity, security and the privacy.This cooperates
with the second level of the system.
3.3.3 Database Design of Timetable
Management System
The Timetable Management System
Database abbreviated as ttms. It stores data of
students, lecturers, users, degree programs, subjects,
timetables and some more. Student data, resources
data, lecturer data, batch data, subject data and
timetable data which can retrieve from the database.
Admin has the authority of modifying and deleting
data. Student, lecturer and course details were taken
from the faculty of Information Technology at
University of Moratuwa.
3.3.4 Second Level Module
This interact with the first level of the
system and includes the logic of the timetable,
constraints or rules, verification, timetable
generation, view, delete and edit. Algorithms usually
kept in this level.
3.3.5 Third Level Module
This level producescreen views of
generated timetable, view the timetable on the web
and print the timetable.
3.3.6 Modeling the system
Use case diagramas Fig. 4describe what the
system does from the standpoint of an external
observer. It shows the interactions between users of
the system and the system.
3.4 Implementation of TMSFIT
Waterfall model was used as the system
development methodology of this TMSFIT.
Because, it was having precise requirements and
well understood milestones. Detail requirement
Timetable
Screen View
Rules / Constraints
Edit
Timetabling
Engine
DeleteRevie
w
Download
Web Site
Timetabl
e
Print
Timeta
ble
View
Lecturer
s
Hall
s
Degree
Labs
Departments
Semester
s
Admin Batch
Student
Subject
Verification
1st
Level
2nd
Level
3rd
Level
Fig.3. Top Level Design Diagram
Fig.4. Main Use Case Design

analysis was conducted at each different user
category getting help of admin of the current
timetable management system.
After the system study, the Software Requirement
Specification (SRS) for the proposed system was
prepared.
3.4.1 Interface Implementation
There were fifty interfaces included in this
TMSFIT system. Those classes are residing in model
and controller of the MVC (Model, View and
Controller) architecture.Some of the classeswere
associated with particular interfaces as follows.
 LoginForm, SiteController and User classes
used for login interface.
 DashboardController class used for dashboard
interface as Fig. 5.
 UserController class used all the interfaces with
CRUD operators.
 StudentController, Student and
Studentenrolsubject classes used for Create
Student, Update Student, Delete Student,
Manage Student, View Student Timetable
3.4.2 Database Implementation
MYSQL was used as the back end of this
system. Because it includes number of engines and
delivers SQL commands to operate database.As the
first step of developing this system, a proper
database was constructed in phpMyAdmin. In this
system, it was called ttms.InnoDB engine use for
foreign keys, support transactions and row level
locking.According to the ER diagram in design, with
main entities such as student, subject, timeslot,
lecturer, degree and department main tables were
generated. Moreover, batch, degree, department,
employee, employeesubject, preferredtime, resource,
student, studentenrolsubject, subject, timeslot,
timetabletimeslot and user tables also
implemented.Primary keys were assigned properly
to avoid duplicate fields.In addition, user also can
back up database, import database and export
database any time.
Then,Process of Mapping Database tables
with Model class were done. In that case,we logged
on toYii code generator and used the model
generator, which generates a model class for the
specified database table Eg batch. Then, it generated
all the appropriate user interfaces mapping with
tables of the database. Later, using CURD generator,
we could generate a view script file which displays a
form to collect input for the specified model class.
That CURD generator was important to generate
controller and views.
3.4.3 Logic Implementation
Implementation of the logic of the timetable
(Algorithm), constraints or rules, verification,
timetable generation, view, delete and edit
operations were in this second level module.Yii used
AlgoritmController class and Algorithm class for the
algorithm development. Hard constraints which
can’t be violated were reside in the calFiness() and
always use to evaluate the fitness value of the
timetable schedule.
3.4.3.1 Genetic Algorithm Implementation
In the initialization process of the GA,
Chromosome or a class schedule must be defined
first. Eg public $_chromosomes. Then, initial
population was created and it is usually randomly
generated 100 chromosomes as the gene or pool.In
the evaluation process of the GA used to, find better
individuals in each generation using fitness function
as the main goal. The fitness value was calculated by
calFitness()how well it fits with our desired
requirements. The main operations of GA such as
selection, crossover and mutation were evaluated
against by fitness function.
Chromosome as Fig.5.or Schedule Evaluation done
with calFitness(). If no room overlapping then
increase the score by 1
If it has enough room space then increase the score
by 1
If required resources are there, then increase score
by 1
Check lecturer overlapping then increase score by 1
Check student overlapping then increase score by 1
Finally score of criteria should be $ci += 5;
FunctioninitObject($numberOfCrossoverPoints,$mutatio
nSize,$crossoverProbability,$mutationProbability,$fitnes
s,$subjectClass)
{
// reserve space for time-space
slots in chromosomes code
$this->_slots = new
SplFixedArray(Schedule::DAYS_NUM *
Schedule::DAY_HOURS * count(Resource::model()-
>findAll()));
$this->_criteria = newSplFixedArray( 5 *
count($subjectClass));
$this->_mutationSize = $mutationSize;
$this->_numberOfCrossoverPoints =
$numberOfCrossoverPoints;
$this->_crossoverProbability = $crossoverProbability;
$this->_mutationProbability = $mutationProbability;
$this->_fitness = $fitness;
$this->_subjectClass = $subjectClass;
$labs = Resource::model()->findAll("type = 'lab'");
$lHalls = Resource::model()->findAll("type != 'lab'");
$this->_allClassRoom = array_merge($lHalls,$labs);
$this->_noOfClassRoom = count($this-
>_allClassRoom);
$this->_noOfLabs = count($labs);
Fig.5.Code segment of Chromosome

3.4.3.2 Selection, Crossover and Mutation
operations
Selection chooses superior individuals in
every generation. This discards the bad designs and
keeps only the best individuals in the population.
Use 100 Chromosomes as this pool (gene). Then,
calculate fitness value for chromosome by using
calFitness() and that function uses the hard
constraints and increase the score one by one 0 to 5.
Then select the most suitable 5 chromosomes as
timetable schedules.
Crossover operations of GA create new
individuals by combining attributes of our selected
individuals. As a result, crossover operator chooses
two individuals from current population (parents)
and creates a new individual (child) based on
parents’ genetic material.Here we considered no of
crossover points as 2, crossover probability as 80%.
Using crossover ($parent2) function, made new
offspring by combining parent codes. Then, checked
the fitness again using the calFitness(). If found a
fitter chromosome than a previous selected one
change it to new schedule.
Mutation typically works by making very
small changes at random to an individual’s
genome.The mutation operator changes the value of
some genes in an individual and helps to search
other parts of problem space. With regards to this
solution, our mutation probability is 3 and
usedmutation() to generate new chromosomes. Then
again check their fitness with previous 5 best
chromosomes using calFitness() and If found a fitter
chromosome than a previous selected one change it
to new one. These steps, selection, crossover, and
mutation, achieved in a 500 while loop.Then, best
five chromosomes (Timetable Schedules)
constructedand put them in to the flag as _bestflags
($chromosomeIndex).
3.5 Timetable Generation
Timetabling System Administrator has the
authority of constructing timetables. Generating
100% optimal solution from GA is not a reality,for
an automated timetable management system.
Therefore, admin has to manually changethe
schedule to make it more accurate.Fig.6. will show
the automate TMSFIT with constraints to be
changed.
As discussed, timetabling system
administrator has all the responsibilities of the
Timetable Management System including manual
changes to the generated timetable. Further, he or
she has authority to register lecturers and students to
the system.By using valid username and a password,
Lecturer can log on to the system, view timetable,
change password, view allocated resources and
mention the preferred time. Student also view
timetable and change he password asrequired.
IV. EVALUATIONANDDISCUSSION
4.1 Evaluation
The main goal of this evaluation was to
discuss whether the system meets the objectives
defined earlier.The significance on evaluating the
system was described through this system by
examines the expected output and the actual
output.If it satisfied our expectations, we considered
that the system was behaving well.Therefore, we
evaluated the system with black box testing with test
data and white box testing with sample test
casesusing specific software such as understanding
tool. Further, we assessed the performance and
robustness of the TMFIT.
For this process, we used actual student
data and resources details of the faculty for the
testing process. In that sense, the answers for the
following questions introduced through an
evaluation strategy with evaluation techniques such
as interviews, observation and questionnaires could
be used.Several interviews were conducted with
Admin of the timetable management system, some
of the lecturers and some of the undergraduate
students. System deployed in parallel way and
Admin staff gave their direct feedback about the
system functionalities. There were more than three
face-to-face interviews conducted with admin staff
and some of the lecturers and during the interview
their feedback about the system were noted down.
System observed through sample input data for each
interface, their anticipated outputs and their definite
output results in the evaluation phase of the TMSFIT
system. Several browser capabilities such as Google
Chrome, Firefox, and Opera also successfully tested.
If the system takes too much loading time, users may
not satisfy about it. Therefore, loading time for all
Fig.6. Generated timetable from TMSFIT

the interfaces had to be considered. Until it was
hosted on a server of the campus, timetable
component had to be given to admin staff. It was
installed their personal computers with WAMP
server. Finally, TMSFIT was further evaluated
through a questionnaire by supplying that to
timetable admin staff, some of the lecturers and
some undergraduate students. From that, we could
discover their satisfaction level of the system.
4.2. Discussion
From the admin’s point of view, overall
system is success. Further, this TMSFIT is time
effective and user friendly. There are four batches
running through the year, and we have to input all
the details of them. When the quantity system data
increasing, timetable generation process also
gradually increasing and fitness value of the
generated timetable is decreasing. As a result,
additional manual work has to be handled. However,
resource optimization phase is satisfied.
On the other hand, even, it’s set up under
probability theory; sometimes it supplies not the
optimal but the best timetable schedulewhich
reached the fitness value as 1. Further, we could
evolve the system with customer feedback such as
adding advanced searching options, view available
resources and printing option. Therefore, after
deploying our system at the university, continues
system evaluation had to be done for use it as a
framework by other universities.
The key research question raised in this
work was can an Automated Timetable Management
System solve the problem of resources optimization
of IT faculty? Prior literature survey suggested there
isn’t a general way of solving these types of
scheduling issues. Further, since we used only
requirements of IT facultyour research area was
limited. Even this is workable as a
framework;application of this system to other
faculties may slightly different.
V. CONCLUSIONAND FURTHER
WORK
In this paper we have introduced a model or
a prototype for timetabling issues of middle scaled
university in Sri Lanka. Even though the timetabling
problem treated as an optimization issue, there is
actually no fixed objective function to solve it.
Therefore, after a proper literature survey, GA was
selected to construction of course timetables
developed for the University of Moratuwa. This
timetabling project seeks to generate near optimal
timetables using the principles of genetic algorithm
(selection, mutation and crossover) and it is easily
understandable, less paper work,efficient and
automated system, which helpful for authorities of
the IT faculty.
Major limitation of this TMSFIT are, the
proposed system can only generate timetables based
on a few hard constraints, it gives only optimal
solutions not the best solution and it only generates
timetables for courses and the execution time of GA
itself is high.In future, this concept can be adapted to
fit the construction of examination timetables also.
We suggest this timetablingsystem can be used as a
framework and it will be more appropriate for
medium scale universities.
VI. Acknowledgements
This effort was a research in utilization of timetable
management system to faculty of it at University of Moratuwa,
Sri Lanka. We acknowledge to all the senior lecturers and the
staff members of the IT department at the university.Thanks also
to great comments of the reviewers.
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