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Writing and Defence of Research Proposal

On 23rd Dec, I have required attending the PhD deference proposal. This is my first-time experience to do the assessment. As the examiner, I also google myself to find out what criteria that examiner wants to evaluate the proposal defend. Here are some good tips that I obtained from the internet on how to writing and also defend your PhD proposal
Hints for Writing a PhD Proposal
By Angelos Keromytis (April 2010)

  • a description of the problem in enough detail to clearly state the thesis proposition (next item)
  • a proper, concise thesis proposition; this is not an abstract statement like “we’re going to investigate the insider problem”, but something along the lines of “our hypothesis is that the use of XYZ technology in environment Z under constraints Q can identify insider attackers with probability Z” — obviously, the fewer qualifiers the better, but you also need to be accurate; since this is a thesis proposal, we will cut you some slack — but it’s in your best interest to think hard about this, since it is the anchor point of your whole thesis (and the next few years’ worth of work for you)
  • a description of the related work, how it does not solve the problem, and how your hypothesis has not been tested before
  • preliminary results (if any) that indicate that you have reason to believe that the hypothesis holds
  • additional experiments that you will run to prove that the hypothesis holds
  • what you’ll need to build to run said experiments (and what you’ve already built)
  • what happens if you can’t run some of these experiments, or if they give you “bad” results — what’s your failover?
  • what are the expected contributions, keeping in mind that each major contribution must demonstrate novelty, non-triviality, and usefulness (so, “first”, “best”, “only” are good adjectives here)
  • how long you expect all this to take

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Example of Bootstrapping

In a statistical analysis, bootstrapping is a popular technique, especially useful when the sample size is small. It is involved with resampling and the technique is assume nothing about the distribution of our data. It is noted that a small sample (<40), we can assuming a normal or a t-distributions.

Bootstrapping can be run in many statistical software in the market. For me, I like to use SPSS, since the software has integrating bootstrapping in each analysis. Many codes that implementing bootstrapping can be found in Matlab and R-language.

The following example is how this technique works that been obtained from this source:

Example Sample

We begin with a statistical sample from a population that we know nothing about. Our goal will be a 90% confidence interval about the mean of the sample. Although other statistical techniques used to determine confidence intervals assume that we know the mean or standard deviation of our population, bootstrapping does not require anything other than the sample.

For purposes of our example, we will assume that the sample is 1, 2, 4, 4, 10.

Example – Bootstrap Sample

We now resample with replacement from our sample to form what are known as bootstrap samples. Each bootstrap sample will have a size of five, just like our original sample. Since we randomly selecting and then are replacing each value, the bootstrap samples may be different from the original sample and from each other.

For examples that we would run into in the real world we would do this resampling hundreds if not thousands of times. In what follows below, we will see an example of 20 bootstrap samples:

2, 1, 10, 4, 2
 4, 10, 10, 2, 4
 1, 4, 1, 4, 4
 4, 1, 1, 4, 10
 4, 4, 1, 4, 2
 4, 10, 10, 10, 4
 2, 4, 4, 2, 1
 2, 4, 1, 10, 4
 1, 10, 2, 10, 10
 4, 1, 10, 1, 10
 4, 4, 4, 4, 1
 1, 2, 4, 4, 2
 4, 4, 10, 10, 2
 4, 2, 1, 4, 4
 4, 4, 4, 4, 4
 4, 2, 4, 1, 1
 4, 4, 4, 2, 4
 10, 4, 1, 4, 4
 4, 2, 1, 1, 2
 10, 2, 2, 1, 1

Example – Mean

Since we are using bootstrapping to calculate a confidence interval about the population mean, we now calculate the means of each of our bootstrap samples. These means, arranged in ascending order are: 2, 2.4, 2.6, 2.6, 2.8, 3, 3, 3.2, 3.4, 3.6, 3.8, 4, 4, 4.2, 4.6, 5.2, 6, 6, 6.6, 7.6.

Example – Confidence Interval

We now obtain from our list of bootstrap sample means a confidence interval. Since we want a 90% confidence interval, we use the 95th and 5th percentiles as the endpoints of the intervals. The reason for this is that we split 100% - 90% = 10% in half so that we will have the middle 90% of all of the bootstrap sample means.

For our example above we have a confidence interval of 2.4 to 6.6.
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What is the difference between Fanning and Moody friction factors?

Which is a correct equation of head loss, Equations 1 or 2?

hL = 4fLV2/2gD         (Equation 1)
hL = flv2/2gd         (Equation 2)

Equation 1 is applied Fanning equation and Equation 2 is applied Moody equation. For old books and tutorial, they like to use the Fanning equation. But, the recent books, they love to applied Moody equation. 😉

A good explanation for this issue as follows (source):

What is the difference between Fanning and Moody friction factors?

Many folks calculate 4 times greater head loss (or 4 times less) than the actual friction loss. This comes from confusion between Moody and Fanning Friction factors. Some friction factor graphs are for Moody Friction factor, which is 4 times Fanning friction factor. That is, f = 64/Re is Moody and f = 16/Re is Fanning.

Be careful with your hydraulic calcs. It is easy to mix the two and calculate 400% greater (or 25% less) head loss. The calculation for head loss in feet is:

using Moody Friction factor -
 h(friction) = f(M) * (L/D) * v^2 / (2 * g)

using Fanning Friction factor -
 h(friction) = 4*f(F) * (L/D) * v^2 / (2 * g)

 h(friction) = head loss by friction in feet
 f(M) = Moody Friction factor
 f(F) = Fanning Friction factor
 L = length in feet
 D = pipe inside diameter in feet
 v = velocity in ft/s
 g = 32.174 ft/s^2, acceleration due to gravity

The Colebrook-White equation is an iterative method that calculates Fanning friction factor.
 f(F)^2 = 1 / ( -4 * Log(eps / (3.7 * D) + 1.256 / (Re * √f(F) )

 eps = pipe roughness in feet
 Re = Reynold's number
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Climate Extremes Indices (Re-post)

I obtained a good post (link) on the climate extremes indices. The post as follows:

Currently, the ET recommends 27 core indices (the ‘ETCCDI’ indices) that are calculated for the globe using the  RClimDex/FClimDex software.

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GIS Tutorials

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Cheap printing dissertation services in Melaka

Cheap printing dissertation services in Melaka

I want to promote a good shop for printing a dissertation/thesis service in Melaka. Each paper, cost only 5sen (more than 100papers) and photostat is around 4sen. Very cheap, it is?

The shop near to Multimemedia University (MMU) Melaka, Bukit Beruang. Below are detailed and some picture of the shop:

Address of the shop: 

No.13 Jalan Bukit Beruang Utama 2, Taman Bukit Beruang Utama 75450
Melaka, Malaysia
016-710 0050

Picture of the front shop and their rate of services:

12033185_508554862653159_8606001636575672581_n 12036768_508554882653157_4575379643839928874_n

Printing murah utk thesis di Melaka

Saya ingin berkongsi mengenai kedai murah untuk print thesis di kawasan Melaka. Setiap helai, harga hanya 5sen (lebih 100 kertas) dan fotokopi hanya 4sen. Murah bukan?

Kedai ini berhampiran dengan Universiti Multimedia (MMU) Melaka di Bukit Beruang. Berikut adalah huraian dan gambar mengenai kedai berkenaan:

Note: Have a cheap printing shop around Melaka? Please share with me. 😉

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Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: A case study in the northern area of Peninsular Malaysia.



The increase in global surface temperature in response to the changing composition of the atmosphere will significantly impact upon local hydrological regimes and water resources. This situation will then lead to the need for an assessment of regional climate change impacts. The objectives of this study are to determine current and future climate change scenarios using statistical downscaling model (SDSM) and to assess climate change impact on river runoff using artificial neural network (ANN) and identification of unit hydrographs and component flows from rainfall, evaporation and streamflow data (IHACRES) models, respectively. This study investigates the potential of ANN to project future runoff influenced by large-scale atmospheric variables for selected watershed in Peninsular Malaysia. In this study, simulations of general circulation models from Hadley Centre 3rd generation with A2 and B2 scenarios have been used. According to the SDSM projection, daily rainfall and temperature during the 2080s will increase by up to 2.23 mm and 2.02 °C, respectively. Moreover, river runoff corresponding to downscaled future projections presented a maximum increase in daily river runoff of 52 m3/s. The result revealed that the ANN was able to capture the observed runoff, as well as the IHACRES. However, compared to the IHACRES model, the ANN model was unable to provide an identical trend for daily and annual runoff series.

Download full manuscript:

Springer or Research_Gate

Cite as:

Hassan, Z., Shamsudin, S., Harun, S., Malek, M. A., and Hamidon, N. (2015). Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: A case study in the northern area of Peninsular Malaysia. Environmental Earth Sciences, 74(1), 463-477. DOI: 10.1007/s12665-015-4054-y

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