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Research Proposal Example for Finance Phd

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					Research Proposal for FRGS

          Prof Dr Ku Ruhana Ku Mahamud
                          31 March 2011
Presentation Outline
 Introduction
 FRGS
   Background
   Proposal Evaluation
   Winning Strategy
 Examples
Introduction
 Types of research grant
   MOHE – FRGS, LRGS, ERGS, PRGS &
    Incentive
   MOSTI – escience, etechno, einnovative
 RM200 million budget under RMK-9
  (2006-2010) for FRGS
   Applied by researcher through institution
   Top Down
   Institution encouragement fund
 RM641 million budget under RMK-10 for
  2011 & 2012
Introduction
 Types of grant
   FRGS – output: theory, concept, new idea
          - answer the question ‘Why’ & ‘How’
   LRGS – fundamental research that needs
    more than 3 years
   ERGS – output can be expanded to applied
    research
          - answer the question ‘What’ &
    ‘Where’
   PRGS – output: product that is not yet
    commercialize
   Research Incentive
Introduction
 Amount offered and closing dates
              Fund        Ceiling      Closing        Fund
              2011         (RM)         Date          2012
             (Million)                               (Million)
 LRGS           45       3,000,000   30 Jan 2011       121
                                      (Top-down)
                                     21 Feb 2011
                                     (Bottom-up)
 FRGS           81       250,000     30 April 2011     219
 ERGS           25       300,000     15 April 2011      68
 PRGS           11       500,000     15 June 2011       30
 Incentive      11                       TBA            30


 For more information -
  http://jpt.mohe.gov.my/menupenyelidik.php
Background - FRGS
 Definition of fundamental research
   Also called basic research - scientific
    investigation for its own sake.
   The goal of fundamental research is to
    gain/discover new knowledge and
    understanding of the physical world that
    may lead to
        new theories or extending and refuting existing
         theories, models or algorithm
        policy recommendation
        improving technology at the fundamental level.
   Without regard to whether or not the
    knowledge discovered will be of any
    practical use.
Background - FRGS
  This is different from applied research, in
   which scientific investigation is carried out in
   order to discover a solution to a practical
   problem. Survey, baseline and observational
   studies alone are not fundamental research.
    Background
     Application for FRGS
         1/2010 – 20 IPTA and 19 IPTS
         2/2010 - 20 IPTA and 34 IPTS
           Area                        1/2010                 2/2010
                               Applied    Approved    Applied    Approved
Pure Science                    356             173    248             41
Applied Science                 407             133    562             69
Clinical and Health Sciences    285             84     392             38
Arts & Applied Arts              22              9     448             23
Social Sciences                 940             235    601             91
Technical & Engineering         1332            463    1057            195
Natural Sciences & Natural      148             64     249             43
Heritage
Total                           3490        1161       3557            500
                                          RM52.4 M               RM24.9 M
    Background
     MMU Application for FRGS
           Area                       1/2010                2/2010
                               Applied   Approved    Applied   Approved
Pure Science                     2             2       2             0
Applied Science                   -            -       14            0
Clinical and Health Sciences      -            -        -            -
Arts & Applied Arts               -            -       7             0
Social Sciences                  30            2       19            2
Technical & Engineering          35            11      25            5
Natural Sciences & Natural        -            -        -            -
Heritage
Total                            67            15      67            7
                                         RM547,400             RM264,890
Aplication’s Rule
 Applications will have to be reviewed by
  RIMC before the submission to MOHE
 Researcher on study leave cannot
  become PI but can be a co-researcher
 PI who left an institution can only be a
  co-researcher
 Application from branch campuses - Fund
  must be used in Malaysia
 Only one application per cycle for PI
 Amount applied must not exceed the
  stated ceiling
Proposal Evaluation
 Committee Chair appointed by MOHE
   Y. Bhg. Prof. Dato’ Dr. Muhamad Rasat
    Muhamad (UM) – Pure Science
   Y. Bhg. Prof. Dato’ Dr. Mohd Amin Jalaluddin
    (UM) – Clinical and medical sciences
   Y. Bhg. Prof. Emiritus Dato’ Dr. Md Ikram
    Mohd Said (UKM) – Applied science
   Y. Bhg. Dato’ Prof. Dr. Nik Muhamad Nik Ab.
    Majid (UPM) – Natural science and national
    heritage
   Y. Bhg. Prof. Dr. Ku Ruhana Ku Mahamud
    (UUM) – Technical and engineering
Proposal Evaluation
  Y. Bhg. Prof. Dr. Samsudin A. Rahim (UKM)
   – Arts and applied arts
  Y. Bhg. Prof. Dato’ Dr. Md. Salleh Yaapar
   (USM) – Social science
 Proposal Evaluation
 Title
   reflects the fundamental issues to be
    researched
 Example 1
  Enhanced nurse rostering algorithm based on
    intelligent evolutionary with improved crossover
 Example 2
  An enhanced dynamic job scheduling algorithm
    based on ant system in computational grid.
 Example 3
  Pattern discovery in temporal data for trend
     analysis using dynamic approach
 Proposal Evaluation -           Title
 Example 4
  Non-cement concrete composite binder from
    Malaysia’s industrial by-products and biogenic
    wastes.
 Example 5
  Incorporating knowledge from domain expert to
     improve Bayesian network learning mechanism
     for imbalanced dataset problem
 Example 6
  A new Corporate Efficiency Performance
     measurement Model Based on Data
     Envelopment Analysis Technique
 Proposal Evaluation -            Title
 Example 7
  Multi-level model in small and medium enterprise:
    The work context.
 Example 8
  Modelling the effect of governance in predicting
    financial distress.
Proposal Evaluation
 Position of leader (PI)
   Professor, Assoc. Prof. & Senior lecturer,
    Lecturer
   PI must be a Malaysian permanent academic
    staff
   If PI is a contract academic staff (citizen or
    non citizen) - Require co-researcher of
    permanent status
   Can lead one research at any one time
 Duration: 1-3 years
Proposal Evaluation
 Other researchers: combination of big
  names, experts, junior but not RA
 Previous research – related completed
  or ongoing research
 Relevant academic publication – journal
  articles or proceedings
 Executive summary
   Precise and concise
   A quarter to a third of a page
Proposal Evaluation –                  Executive Summary


 Example 1
As nursing personnel or nurses is one of the key resources
   that make a hospital operable, it is wise to generate
   efficient work schedules for them, which would in turn be
   very beneficial to hospital operations. In addition, the
   complexity of the nurse rostering problem (NRP), which
   is due to combination of hard requirements and soft
   constraints is another key that motivates this research.
   Subsequently, there have been many variations in the
   approach and technique to provide efficient work
   schedules. However, we identify that meta-heuristic
   techniques have the most potential for exploration and
   improvement.
Proposal Evaluation –                    Executive Summary

Thus, a meta-heuristics, i.e. the Evolutionary Algorithm (EA)
   will be investigated and possibly, a Memetic Algorithm
   which is also a variation of EA is proposed to solve a
   highly constrained NRP. The evolutionary operators like
   crossover and mutation will be investigated for
   improvement such that they are able to build up
   intelligent algorithm. It is also expected that the
   proposed evolutionary NRP model could have qualities
   like speed, robustness, flexibility, and user-friendliness.
Proposal Evaluation –                 Executive Summary


 Example 2
Scheduling of jobs in grid computing involves matching of
   jobs with available resource. The purpose is to balance
   the entire system load while completing all the jobs at
   hand as soon as possible according to the environment
   status. Static job scheduling algorithm such as ‘first
   come first serve’ and ‘shortest job first’ may not be
   suitable for scheduling of jobs in the dynamic grid
   environment because these algorithms cannot handle
   activities such as evaluating present load of the
   resources and notifying when new resources join or drop
   from the system. A good scheduler would adjust its
   scheduling strategy according to the changing status of
   the entire environment and the types of jobs.
Proposal Evaluation –                    Executive Summary
This research seeks to construct an ant colony based
    algorithm that can dynamically schedule jobs to
    resources in the grid computing environment. Ants will
    be assumed as jobs and the algorithm will send the ants
    to search for resources. Local and global pheromone
    update functions in ant colony algorithm will be
    formulated to balance the load for each resources.
    Resource selection function will also be formulated by
    taking into account the conditions of the jobs and
    resources.       A dynamic scheduling technique in
    monitoring the activities of jobs and resources will be
    constructed followed by developing a simulation model
    that can be used to simulate the dynamic grid
    environment and evaluate the performance of the
    proposed    ant-based      job    scheduling    algorithm.
    Comparison with other approaches will be performed to
    determine the credibility of the proposed algorithm.
Proposal Evaluation
 Research background
     Introduce the research area
     Cover the main words in the title
     Relate to previous study
     State the problem statement, research
      question/hypothesis, literature review and
      references
 Objective of the research – reflects the
  fundamental solution/output to the
  problem
Proposal Evaluation -                    Objective

 Example 1
The main objective is to develop a model for a nurse
     rostering problem (NRP) investigating the potentiality of
     the evolutionary algorithm. In achieving the main
     objective, there are some specific objectives that have to
     be fulfilled, which are:
i) To determine the constraints and parameters relevant to
      all hard rules and work preferences from the nurses’
      perspective in order to better understand the soft
      constraints that need to be taken into account,
ii) To construct an improved crossover and mutation
      mechanisms as part of the evolutionary algorithm such
      that rostering activities are more efficient,
iii) To develop a prototype for the NRP and to evaluate the
      performance of the enhanced algorithm.
Proposal Evaluation -                  Objective

 Example 2
To develop an enhanced ant-based optimization algorithm
   for dynamic scheduling of jobs in the computational grid
   environment.
    Specific objectives of the research are:
    To formulate the local and global pheromone update
       functions
    To formulate the resource selection function
    To construct a dynamic scheduling algorithm that
       consists of the pheromone update functions and
       resource selection function
    To develop a simulation model that can be used to
       simulate the dynamic grid environment and evaluate
       the performance of the proposed ant-based job
       scheduling algorithm
Proposal Evaluation
 Methodology
   Detail description
   Divide the study into several phases
   Provide the flow chart and Gantt chart of the
    research activities
   Milestones and dates
     Highlights the output of the research activities
     Provided in tabular form
Proposal Evaluation
 Expected result
   Novel theory – in line with the objective
   State the expected number of academic
    publication
   State the specific or potential application
   Number of research students
     3 years – 1 PhD or 2 Master
     2 years – 1 Master
     1 year – 1 Master
Proposal Evaluation
 Budget – total <= RM250,000
   Vote 11000 (salary and wages)
     up to 3 GRA for one project
     RM2000 for PhD and RM1500 for Master
   Vote 21000 (travelling expenses and
    subsistence)
     Data collection and meeting within the
      country
     For national and international conferences
      as presenter
     Lodging, food, flight and taxi fares
Proposal Evaluation -        budget


  Vote 24000 (rental): building, equipment,
   transport, etc.
  Vote 27000 (research materials and
   supplies): books, journals, papers,
   chemicals, animals, etc.
  Vote 28000 (maintenance and minor
   repair): sand, cement, building, lab,
   equipment, etc.
Proposal Evaluation -         budget

  Vote 29000 (professional services): printing,
   honorarium, consultation, training (<3
   months), fees
  Vote 35000 (equipment): special equipment
   (e.g. camera, tape recorder), upgrading of
   existing equipment, computer, printer
Winning Strategy
 3 types

                       GENERAL
                       STRATEGY




             SMART                  GOOD
            STRATEGY              STRATEGY
Winning Strategy
 General strategy

                                TECHNOFUND
                      SCIENCEFUND
               FRGS


       POTENTIAL
       RESEARCH

  PHD
  STUDENTS                Plan for your research
                           group/SIG
                          Each group member leads
                           FRGS
                          Help each other
                          Have more PhD student
Winning Strategy
 Good strategy
   Research proposal is in line with national
    research agenda
     Wireless sensor network, predictive
       analytics, 3-D internet (multimedia digital
       content)
     Agriculture – precision farming, halal food,
       bioinformatics
     Services – Islamic finance, logistics,
       tourism, healthcare
     Manufacturing – automotive, nano
       technology
Winning Strategy
 Smart strategy
     Bring big person in
     Bring various expertise in
     If possible, all level of academics are in
     Good publication backup
     Good relevant research
     Straight forward mention how your research
      in-line with National Research Agenda
 Very smart strategy
   Use your 1st year PhD Student’s potential
    research work
Thank you
            ruhana@uum.edu.my

				
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