> |bjbjss cs44wwwww$P\GA@#%#%(K%K%K%''D($AAAAAAA:CETAw*&"'**AwwK%K%A===*wK%wK%A=*A==1@@K%LQ-@AA0A@0F9/4 0F(@0Fw@3(v(T=(DA)|3(3(3(AAm8V3(3(3(A****0F3(3(3(3(3(3(3(3(3(4 =: A. NATURE OF THE AWARD
Awarding Institution: Kingston University
Programme Accredited by:
Final Award(s): BSc (Hons)
Intermediate Awards: CertHE, DipHE, Ordinary Degree
Field Title: Mathematical Sciences
FHEQ Level: Honours
Credit rating by level: 120 @ level 4, 120 @ level 5, 120 @ level 6
JACS code: G100
QAA Benchmark Statement(s): The Mathematical Sciences field described below complies with the MSOR subject benchmark statement (QAA 2007).
Minimum Registration: 3 years or 4 years with placement
Maximum Registration: 9 years
Faculty: SEC
Location: Penrhyn Road
Date Specification Produced: March 2004
Date Specification Revised: September 2011
B. FEATURES OF THE FIELD
Title: BSc Mathematical Sciences (full field only)
2. Modes of Delivery
The field is may be studied in the following alternative patterns
- Full time
- Part time
Mathematical Sciences is offered as a three year full time course, although it is possible for students to switch between full time and part time mode attendance. A sandwich option is available.
3. Features of the Field
The School of Mathematics offers a field in Mathematical Sciences, leading to the BSc degree in Mathematical Sciences, within the Undergraduate Modular Scheme (UMS). The Mathematical Sciences field draws primarily upon modules from the Mathematics, Statistics and Computing subject areas (some options from other subject areas are also available).
C. EDUCATIONAL AIMS OF THE FIELD
Field Aims
The field shares the general aims and objectives of the UMS and in addition aims to give students a sound foundation in mathematics, statistics and computing as well as assisting them in their general personal development by providing them with a coherent, academically-sound programme of study.
The Mathematical Sciences field aims will be to develop students abilities to:
increase their knowledge, understanding and skills in mathematics, statistics and computing;
apply their knowledge, understanding and skills successfully in a variety of problems, for example from business, science or technology;
identify, understand and formulate mathematical and statistical models, with the aid of appropriate computer packages when required;
seek, use and communicate relevant information effectively in oral, visual and written forms;
work both in groups and individually, and to work for and with non-specialists;
extend their knowledge in mathematics, statistics or computing by further formal study or effective use of published work.
D. LEARNING OUTCOMES (OBJECTIVES) OF THE FIELD
The learning outcomes of the Mathematical Sciences field are to produce graduates who are able to:
1. Knowledge and Understanding
1 demonstrate an appropriate mastery of theory and techniques of the mathematical sciences to be able to apply them to a variety of problems;
2. Cognitive (thinking) Skills
2A formulate problem solutions;
2B identify appropriate mathematical methods and any relevant computer applications, to assist in the solution of problems;
2C demonstrate research skills;
3. Practical Skills
There are two core level 4 (CO1000,CO1040) modules and one core level 5 (CO2040) module where students develop their programming skills.;
4. Key Skills
On completion of the field students will have acquired transferable skills to:
a. Communication Skills
receive and respond to a variety of information e.g. taking part in discussions; selecting, extracting and collating information from appropriate sources; presenting information in a variety of formats/media;
b. Numeracy
apply numerical skills and techniques to quantitative situations e.g. collecting data (where appropriate); evaluating quantitative data; performing basic calculations;
c. Information, Communication and Technology
make effective use of computer systems to aid data manipulation and presentation e.g. presenting different forms of information; searching for and storing information; on-line communication;
d. Teamwork
work effectively as a member of a team, appreciating the value of their own and others contributions;
e. Independent Learning
display self management and organisation leading to attainment of objectives within timelines and personal development e.g. developing research and information handling skills; developing self awareness; monitoring and reviewing own progress.
Table 1 below identifies the key skills associated with summative assessment components for core modules and options from the subject areas of Mathematics, Statistics and Computing. It should be recognised that, in addition, students will be developing these skills extensively away from these summative assessment exercises: in classes, in formative assessment exercises, in private study and in extra-curricula activities. Modules shown in bold text are core to the Mathematical Sciences field .
The learning and teaching strategies of the field seek to ensure that students learn actively and effectively, thus laying the foundation for future careers and/or further study.
Table 1 - Key Skills Summary
Module CodeModule TitleCommunicationNumeracyICTTeamworkIndependent LearningCO1000Fundamental Programming Concepts I, LCI, L, TI, L, TCO1040Object-Oriented Programming with Java C, RC, RC, R, TCO2060DatabasesG(R,O)G(R,O), TG(R,O)ECO2071Operating Systems and Networking CC, ECECO2090Software Development with Java C, TC, R, TC, TC, RCO2040Fortran ProgrammingC, TC, TC, TMA1015Introduction to Mathematical AnalysisC, GC, G, T, ECGG, EMA1030Introduction to Linear AlgebraC, T, ECEMA1050Modern Techniques for MathematicsII, TI, TIMA2010Mathematical MethodsC, EEMA2020Ordinary Differential EquationsC, EEMA2030Concepts of MathematicsC, P, RC, P, RP, RPMA2040Mathematical Modelling IG(R, O, P)G(R, O, P), TG(R, O, P)G(R, O, P)G(R, O, P)MA2110Real Analysis IC, EEMA3010Partial Differential Equations & Approximation TheoryCC, ECC, EMA3090Mathematical Modelling IIC, G(R, O)C, G(R, O), TC, G(R, O)G(R, O)G(R, O)MA3200Mathematical ProgrammingG(R)G(R), T, EG(R)G(R)G(R), EMA3210Introduction to Calculus of Variations and Optimal ControlG(R, O)G(R, O), EG(R, O), EG(R, O)G(R, O), EMA3130OptimisationCC, ECEMA3170Fluid Dynamics in ActionC, EEMA3180Real Analysis IIC, EEMA3980ProjectD, O, RD, O, RD, O, RDD, O, RST1210Introduction to Probability and StatisticsC, TCST1220Introductory Statistical InferenceRR, T, ERR, EST2210Regression ModellingG(R)G(R), T, EG(R)G(R)G(R), EST2220Statistical DistributionsCC, EEST2343Medical StatisticsI(R)I(R), T, EI(R)I(R), EST2353Operational Research TechniquesCC, ECEST3310Stochastic ProcessesI(R)I(R), C, EI(R)I(R), C, EST3333Experimental DesignCC, ECC, EST3343Medical StatisticsI(R)I(R), T, EI(R)I(R), EST3353Operational Research TechniquesCC, ECEST3370Further Inference and Bayesian MethodsCC, EE
ST3820Portfolios and InvestmentsC, I(R)C, I(R), EI(R)I(R)ST3830Market Models and Derivative SecuritiesC, E
Key:
C - Coursework Assignment, D - Project Development, E Examination,
I - Individual Case Study or Self-Study/Research Exercise,
G - Group Case Study or Self-Study, L - Library Workbook,
O - Oral Presentation/Interview, P - Poster Presentation, R Report, T - In-class Test.
E. FIELD STRUCTURE
The field is intended to provide a flexible programme in mathematics, statistics and computing which allows some specialisation in one of these areas at levels 5 and 6. The emphasis throughout is on concepts and methods which may be employed to solve real-life problems whilst attention is paid to ensure that the necessary theoretical underpinning is retained. Module details can be found in the Module Directory.
The integrated and coherent nature of the field programme ensures that the students will have opportunities to gain the necessary prerequisites for all option modules. Some of the free choice and option modules at level 4 allow students the opportunity to study areas of science, technology or business. These areas have been selected as being those in which the students mathematically based problem solving skills may profitably be applied in the future, for example environmental modelling.
The sandwich year is an optional element in the programme, taken between levels 5 and 6. Students who opt for the sandwich mode will spend a minimum period of 36 weeks in an approved placement in industry or commerce.
Level 4
At Level 4, students will be introduced to a wide variety of topics, laying the necessary foundation for further work in this field. The study of mathematical methods will include calculus, linear algebra, ordinary differential equations, an introduction to numerical methods and exposure to symbolic algebra and linear algebra packages. Core statistics and computing modules are included to underpin those subjects and to provide pre-requisites for later options.
In the first Semester 1 significant proportion of the content of MA1015 is designed to overlap selected A-level material, while no previous knowledge of statistics or computing is assumed - in accordance with the aim of widening participation.
The level 4 modules contributing to the field are:
Semester 1
CodeModule TitleCore/OptionCreditsST1210Introduction to Probability & StatisticsCore15CO1000Fundamental Programming ConceptsCore15Free Choice Module15
Semester 2
CodeModule TitleCore/OptionCreditsMA1030Introduction to Linear AlgebraCore15CO1040Object Oriented Programming with Java Core15ST1220Introductory Statistical InferenceOption15CO1052HTML Programming and Internet ToolsOption15KL1902LanguageOption15
Semesters 1&2
Module TitleCodeCore/OptionCREDITSIntroduction to Mathematical AnalysisMA1015Core30
Students have a free-choice option module in Semester 1 which offers a wide range of modules from science, computing and business areas including one mathematics module, MA1050 Modern Techniques for Mathematics.
Level 5
The first semester module, Concepts of Mathematics, will ensure that students engage in logical argument, mathematical proof and the modelling cycle whilst enhancing their communication skills. The Mathematical Methods module will build on the Level 4 work, introducing additional techniques and providing a foundation for numerical and analytical treatments of ordinary and partial differential equations later in the field. The Regression Modelling module extends knowledge of simple linear regression to multiple linear and logistic regression and introduces the students to SAS statistical software. In Semester 1 students also have the opportunity to extend their knowledge of computer programming or statistical distributions.
The study of ordinary differential equations is extended to systems in semester 2 where a symbolic algebra package will be used to assist with visualisation and Mathematical Modelling is considered in greater depth based primarily on the investigation of real world problems. The Fortran programming language is introduced to broaden scientific computing skills. Students have a range of options in mathematics, statistics, and computing areas from which to choose.
The level 5 modules contributing to the field are:
Semester 1
CodeModule TitleCore/OptionCreditsMA2010Mathematical MethodsCore15MA2030Mathematical ConceptsCore15ST2210Regression ModellingCore15ST2220Statistical DistributionsOption15CO2090Software Development with JavaOption15
Semester 2
CodeModule TitleCore/OptionCreditsMA2020Ordinary Differential EquationsCore15MA2040Mathematical Modelling ICore15CO2040Fortran ProgrammingCore15MA2110Real Analysis IOption15ST2343Medical StatisticsOption 15ST2353Operational Research TechniquesOption15CO2060DatabasesOption15CO2071Operating Systems and NetworkingOption15
Level 6
At Level 6 students further develop their mathematical modelling abilities and experience and also extend their study to partial differential equations and approximation theory and optimisation. There is scope for individual preferences to be followed in the options which again include mathematics, statistics and computing. There is a project module, usually occupying 2 of the 8 modules, but there are mathematical education module alternatives to this, one theoretical and the other being essentially a work-based module in a local school or college.
The level 6 modules contributing to the field are:
Semester 1
CodeModule TitleCore/OptionCreditsMA3010Partial Differential Equations and Approximation TheoryCore15MA3090Mathematical Modelling IICore15MA3991Issues in Mathematical Education/or Project15MA3200Mathematical ProgrammingOption 15ST3310Stochastic ProcessesOption 15ST3820Portfolios and InvestmentsOption 15
Semester B
CodeModule TitleCore/OptionCreditsMA3130OptimisationCore15MA3992Mathematics in the Classroom/or Project15MA3210Introduction to Calculus of Variations and Optimal Control TheoryOption15MA3170Fluid Dynamics in ActionOption15MA3180Real Analysis IIOption15ST3333Experimental DesignOption15ST3343Medical StatisticsOption15ST3353Operational Research TechniquesOption15ST3370Further Inference & Bayesian MethodsOption15ST3830Market Models and Derivative SecuritiesOption15
F. FIELD REFERENCE POINTS
The Field has been designed to take account of QAA Subject Benchmark Statements for MSOR (QAA 2007).
The awards made to students who complete the field or who are awarded intermediate qualifications comply fully with the National Qualifications Framework.
All of the procedures associated with the field comply with the QAA Codes of Practice for Higher Education.
Module content, especially at level 6, is informed by staff research expertise, and other scholastic activities and employment experience.
G. LEARNING AND TEACHING STRATEGIES
The learning and teaching strategies reflect the field aims and learning outcomes, student background, potential employer requirements and the need to develop a broad range of technical skills, with the ability to apply them appropriately. The strategies ensure that students have a sound understanding of some important areas in mathematics and statistics and have acquired the transferable skills expected of modern-day undergraduates.
150 hours of study time is allocated to each module. Typically, this includes 55 hours of contact time per module at level 4 and 44 hours at levels 5 and 6, leaving the remainders for self-directed or guided study time. There is more contact at level 4 to provide initial academic support and students are encouraged to develop as independent learners as they progress through their degree course. Contact time can consist of lectures, tutorials, problems classes, practicals or PAL sessions, dependent on individual module requirements. Generally, subject material and corresponding techniques will be introduced in lectures; for many modules practical activities are regarded as essential to the understanding of the material and the development of relevant skills. In problems classes students typically work through formative exercises under guidance and in PAL sessions second year students help those at level 4 to develop their study skills.
Some level 4 MA modules have an associated study guide containing core material and formative exercises, the remainder have these materials distributed throughout the semester.. These materials, and worksheets in computing practical sessions, help develop self-paced learning and independent study. Most higher level modules have lecture notes available in hard-copy or on StudySpace, which is the universitys learning management system. The faculty produces KU Tables, which give basic mathematical and statistical formulae and a number of statistical tables; these may be used in lectures, problem classes, tests or examinations.
Students will be expected to develop their skills, knowledge and understanding through independent and group learning, in the form of both guided and self-directed study. In most modules students will be given regular formative exercises or practical work through which they can develop learning skills, knowledge and techniques. Further they will have the opportunity to work individually and in groups on assignments, practicals, case studies and projects. These activities and their assessment are designed to enable students to meet the specific learning outcomes of the field.
A particularly important component of the degree is the project, which develops the students confidence and ability to carry out individual pieces of scholarship or research and then communicate their results in both written and oral forms. The project may be solely mathematics-based or, preferably, involve the application of mathematical or statistical methods to the investigation of the real world.
H. ASSESSMENT STRATEGIES
Assessment enables students abilities to be measured in relation to the aims of the field; assessment also serves as a means for students to monitor their own progress at prescribed stages and enhance the learning process.
The assessment strategy has been devised to reflect the aims of the field and to complement the learning and teaching strategies described above. Throughout the field students are exposed to a range of assessment methods, thus allowing them to develop technical and key skills and enabling the effectiveness of the learning and teaching strategies to be evaluated.
The methods of assessment have been selected so as to be most appropriate for the nature of the subject material, teaching style and learning outcomes in each module. Some modules are assessed entirely by in-course work, while others have, in addition an end-of-module examination. No module is assessed by an end-of-module examination alone. In particular, the balance between the various assessment methods for each module reflects the specified learning outcomes.
The assessments are designed so that students achievements of the field learning outcomes can be measured. A wide range of assessment techniques will be used to review as accurately and comprehensively as possible the students attainments in acquiring sound factual knowledge together with the appropriate technical competence and understanding, so that they can tackle various types of problems.
Components of Assessment
In the field as a whole, the following components may be used in the assessment of the various modules:
- Multiple choice or short answer in-class tests: to assess competence in basic techniques and understanding of concepts
- Long answered structured questions in coursework assignments: to assess ability to apply learned techniques to solve simple to medium problems and which may include a limited investigative component
- Long answer structured questions in end-of-module examinations: to assess overall breadth of knowledge and technical competence to provide concise and accurate solutions within restricted time
- Practical exercises: to assess students understanding and technical competence
- Individual case studies: to assess ability to understand requirements and to provide solutions to realistic problems. The outcomes can be:
- Written report, where the ability to communicate the relevant concepts, methods, results and conclusions effectively will be assessed.
- Oral presentation, where the ability to summarise accurately and communicate clearly the key points from the work in a brief presentation will be assessed.
- Poster presentation where information and results must be succinct and eye-catching.
- Group-based case studies: contain all of the assessment objectives of individual case studies and in addition to assess ability to interact and work effectively with others as a contributing member of a team
- Project: The individual or group project module is similar to an extended case study. The problems tackled may be of a more open-ended nature, allowing students to increase their knowledge of mathematics or of the second field by studying a topic in greater depth and/or by applying techniques learned in a new situation. As such the assessment here will place a greater emphasis on ability to plan work, manage time effectively, and research background information, although students will also be expected to produce written reports and to be interviewed about their work.
In addition to any specific criteria, the following features are expected in work that is submitted for coursework assignments:
- Technical competence: the generated system or solution performs the requirements stated in the best possible implementation.
- Completeness: all aspects of the work are attempted and full explanations of all reasoning are given.
- Clarity: all explanations are clear and concise. Arguments follow a logical sequence and are laid out in a clear format.
- Neatness: all reports are produced using a word-processor. Tables, graphs and diagrams are neat and suitably labelled. Assignments with a high mathematical content may be submitted in neat handwriting.
Assessment Procedures
It is Faculty policy that in-course work is returned to students within three working weeks. Feedback can be model solutions and/or comments on the work. All examination papers, coursework and tests are internally moderated and those for levels 5 and 6 also externally. Projects are double-marked; examination scripts are checked to ensure that all work has been marked and scores correctly totalled.
The formal assessment procedure is specified in the general regulations of the UMS.
Assessment Summary
Table 2 indicates the methods of assessment to be used in all the field modules. Core modules are given in bold text. Further details are given in the module descriptions.
Table 3 shows the distribution of the field learning outcomes across its core and option modules.
Table 2 - Assessment Summary (Indicative)
Level 4Module CodeModule TitleTestsWritten assignmentsPractical/
Case StudyExaminationMA1015Introduction to Mathematical Analysis***MA1030Introduction to Linear Algebra***MA1050Modern Techniques for Mathematics**ST1210Introduction to Probability & Statistics**ST1220Introductory Statistical Inference***CO1000Fundamental Programming Concepts**CO1040Object-Oriented Programming with Java**Level 5Module CodeModule TitleTestsWritten assignmentsPractical/
Case StudyExaminationMA2010Mathematical Methods**MA2030Mathematical Concepts*MA2020Ordinary Differential Equations**MA2040Mathematical Modelling I***(Group)MA2110Real Analysis I**ST2210Regression Modelling** (Group)*ST2220Statistical Distributions***ST2343Medical Statistics***ST2353Operational Research Techniques**CO2090Software Development with Java**CO2060Databases**(Group)*CO2071Operating Systems and Networking **CO2040Fortran Programming**Level 6Module CodeModule TitleTestsWritten assignmentsPractical/
Case StudyExaminationMA3010Partial Differential Equations and Approximation Theory**MA3090Mathematical Modelling II***(Group)MA3200Mathematical Programming**MA3210Introduction to Calculus of Variations and Optimal Control Theory**MA3130Optimisation**MA3980Project*MA3170Fluid Dynamics in Action**MA3180Real Analysis II**MA3991Issues in Mathematical Education**MA3992Mathematics in the Classroom**ST3310Stochastic Processes(((ST3333Experimental Design((ST3343Medical Statistics*((ST3353Operational Research Techniques((ST3820Portfolios and Investments**ST3830Market Models and Derivative Securities**ST3370Further Inference and Bayesian Methods((
Table 3 Field Learning Outcomes
Field Learning Outcomes12A2B2C3Level 4MA1015Introduction to Mathematical Analysis(((((MA1030Introduction to Linear Algebra(((((ST1210Introduction to Probability and Statistics((((CO1000Fundamental Programming Concepts ((((CO1040Object-Oriented Programming with Java ((((MA1050Modern Techniques for Mathematics(((((ST1220Introductory Statistical Inference((((CO1052HTML Programming and Internet Tools((((Level 5MA2010Mathematical Methods I(((MA2020Ordinary Differential Equations((((MA2030Concepts of Mathematics(((((MA2040Mathematical Modelling I(((((ST2210Regression Modelling((((CO2040Fortran Programming((((MA2110Real Analysis I(((ST2220Statistical Distributions(((ST2343Medical Statistics((((ST2353Operational Research Techniques(((CO2060Databases((((CO2090Software Development with Java((((CO2071Operating Systems & Networking(((Level 6MA3010Partial Differential Equations & Approximation Theory(((((MA3090Mathematical Modelling II(((((MA3130Optimisation(((MA3980Project (((((MA3200Mathematical Programming((((MA3210Introduction to Calculus of Variations and Optimal Control((MA3170Fluid Dynamics in Action(((MA3180Real Analysis II(((MA3991Issues in Mathematical Education(((MA3992Mathematics in the Classroom(((ST3310Stochastic Processes((((ST3333Experimental Design((((ST3343Medical Statistics((((ST3353Operational Research Techniques(((ST3820Portfolios and Investments(((((ST3830Market Models and Derivative Securities((((ST3370Further Inference & Bayesian Methods((
I. ENTRY QUALIFICATIONS
1. The minimum entry qualifications for the field are:
The general entry requirements for the field are those applicable to all programmes within the UMS.
2. Typical entry qualifications set for entrants to the field are:
For the Mathematical Science field 260 points, including two 6-unit awards, with an A-Level in mathematics are normally required.
A foundation year is available for students without formal entry qualifications. Mature applicants and those with qualifications not specified above will be considered individually.
J. CAREER OPPORTUNITIES
In addition to providing a route to studying for higher degrees, the Mathematical Sciences field graduate will be equipped for employment, for example, as:
Commercial, industrial and public sector managers
Business and finance associate professionals
Actuaries
Chartered accountants
Statisticians
Business analysts
Scientific and engineering professionals
Marketing, sales and advertising professionals
Teaching professionals.
K. INDICATORS OF QUALITY
External examiners report, reviewed by Faculty Course Quality Assurance Committee (annual)
Field validation event panel (2002)
QAA MSOR Subject Review (2000).
L. APPROVED VARIANTS FROM THE UMS/PCF
No variations from UMS required.
BSc (HONOURS) MATHEMATICAL SCIENCES CFMAT
LEVEL 4LEVEL 5OPTIONALLEVEL 6Introduction to Mathematical Analysis
MA1015 (A)Mathematical Methods
MA2010 (D)
MA1015, MA1030Ordinary Differential Equations
MA2020 (C)
MA2010Industrial Placement YearPDEs & Approximation Theory
MA3010 (F)
MA2010, MA2020Optimisation
MA3130 (B)
MA2010, MA2020Introduction to Probability and Statistics
ST1210 (E)Introduction to Linear Algebra
MA1030 (A)
MA1030Mathematical Concepts
MA2030 (G)
CO1000,MA1015Mathematical Modelling I
MA2040 (B)
CO1000, MA2030Mathematical Modelling II
MA3090 (E)
MA2020, MA2040
Option 3BFundamental Programming Concepts
CO1000 (B)Object-Oriented Programming with Java
CO1040 (F)
CO1000Regression Modelling
ST2210 (B)
ST1210,MA1030Fortran Programming
CO2040 (E)
CO1000
Option 3A
Option 3B
Free Choice
Option 1B
Option 2A
Option 2BProject MA3980
or
Issues in Mathematics Education MA3991 (D)
A-Level Maths Level 5Project MA3980
or
Mathematics in the Classroom MA3992
A-Level Maths Level 5Note 1: Free choice module chosen from a wide range of science/language/business modules.
Option 1B: Choose ONE from:
ST1220 Introductory Statistical Inference (C)
CO1052 HTML Programming and Internet Tools(E)
KL1902 Language (G)
Option 2A: Choose ONE from:
ST2220 Statistical Distributions (E)
CO2090 Software Development with Java (A)
Option 2B: Choose ONE from:
MA2110 Real Analysis I (F)
ST2343 Medical Statistics (F)
ST2353 Operational Research Techniques (F)
CO2060 Databases (G)
CO2071 Operating Systems & Networking (D)
Option 3A: Choose ONE from:
MA3200 Mathematical Programming (B)
ST3820 Portfolios and Investments (B) ST1220, MA1015
ST3310 Stochastic Processes (C) ST2220
Option 3B: Choose TWO:
MA3170 Fluid Dynamics in Action (A)
MA3180 Real Analysis II (F) MA2110, MA3010
MA3210 Introduction to Calculus of Variations and Optimal Control Theory (C)
ST3830 Market Models and Derivative Securities(A) ST3820, MA3010
ST3333 Experimental Design (E)
ST3343 Medical Statistics (F)
ST3353 Operational Research Techniques (F)
ST3370 Further Inference & Bayesian Methods (G) ST2220
PROGRAMME SPECIFICATION KINGSTON UNIVERSITY
Mathematical Sciences, BSc (Hons), 2011-2012
Page PAGE 2 of NUMPAGES 15
August 2010
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