Abstract: The purposes of this study are 1) to study the over 20-year attempt of Mahakan fort community to negotiate with Bangkok Metropolitan Administration (BMA) to remain in their residential area belonging to the state, and 2) to apply the new social and cultural dimension between the state and the community as an alternative for local participation in keeping their residential area. This is a qualitative research, and the findings reveal that the community claimed their ancestors’ right as owners of this piece of land for over 200 years. The community, therefore, requested to take part in the preservation of land, culture and local intellect and the area management in terms of being a learning resource on the cultural road in Rattanakosin Island. However, BMA imposed the law concerning the community area relocation in Rattanakosin Island. The result of law enforcement led to the failure of the area relocation, and the hard hit on physical structure of the area including the overall deterioration of the cultural road renovated in the year 1982, the 200 years’ celebration of Bangkok. The enforcement of law by the state required the move of the community, and the landscape improvement based on the capital city plan. However, this enforcement resulted in the unending conflicts between the community and the state, and the solution of this problem was unclear. At the same time the community has spent a long time opposing the state’s action, and preparing themselves by administrating the community behind Mahakan fortress with community administrative committee under the suggestion of external organization by registering all community members, providing funds for community administration. At the meantime the state lacked the continuation of the enforcement due to political problem and BMA’s administration problem. It is, therefore, suggested that an alternative solution to this problem lie at the negotiation between the state and the community with the purpose of the collaboration between the two to develop the area under the protective law of each side.
Abstract: In this paper, the Tabu search algorithm is used to
solve a transportation problem which consists of determining the
shortest routes with the appropriate vehicle capacity to facilitate the
travel of the students attending the University of Mauritius. The aim
of this work is to minimize the total cost of the distance travelled by
the vehicles in serving all the customers. An initial solution is
obtained by the TOUR algorithm which basically constructs a giant
tour containing all the customers and partitions it in an optimal way
so as to produce a set of feasible routes. The Tabu search algorithm
then makes use of a search procedure, a swapping procedure and the
intensification and diversification mechanism to find the best set of
feasible routes.
Abstract: Collateralized Debt Obligations are not as widely used
nowadays as they were before 2007 Subprime crisis. Nonetheless
there remains an enthralling challenge to optimize cash flows
associated with synthetic CDOs. A Gaussian-based model is used
here in which default correlation and unconditional probabilities of
default are highlighted. Then numerous simulations are performed
based on this model for different scenarios in order to evaluate the
associated cash flows given a specific number of defaults at different
periods of time. Cash flows are not solely calculated on a single
bought or sold tranche but rather on a combination of bought and
sold tranches. With some assumptions, the simplex algorithm gives
a way to find the maximum cash flow according to correlation of
defaults and maturities. The used Gaussian model is not realistic in
crisis situations. Besides present system does not handle buying or
selling a portion of a tranche but only the whole tranche. However the
work provides the investor with relevant elements on how to know
what and when to buy and sell.
Abstract: Many algorithms are available for sorting the unordered elements. Most important of them are Bubble sort, Heap sort, Insertion sort and Shell sort. These algorithms have their own pros and cons. Shell Sort which is an enhanced version of insertion sort, reduces the number of swaps of the elements being sorted to minimize the complexity and time as compared to insertion sort. Shell sort improves the efficiency of insertion sort by quickly shifting values to their destination. Average sort time is O(n1.25), while worst-case time is O(n1.5). It performs certain iterations. In each iteration it swaps some elements of the array in such a way that in last iteration when the value of h is one, the number of swaps will be reduced. Donald L. Shell invented a formula to calculate the value of ?h?. this work focuses to identify some improvement in the conventional Shell sort algorithm. ''Enhanced Shell Sort algorithm'' is an improvement in the algorithm to calculate the value of 'h'. It has been observed that by applying this algorithm, number of swaps can be reduced up to 60 percent as compared to the existing algorithm. In some other cases this enhancement was found faster than the existing algorithms available.
Abstract: The Block Sorting problem is to sort a given
permutation moving blocks. A block is defined as a substring
of the given permutation, which is also a substring of the
identity permutation. Block Sorting has been proved to be
NP-Hard. Until now two different 2-Approximation algorithms
have been presented for block sorting. These are the best known
algorithms for Block Sorting till date. In this work we present
a different characterization of Block Sorting in terms of a
transposition cycle graph. Then we suggest a heuristic,
which we show to exhibit a 2-approximation performance
guarantee for most permutations.
Abstract: A complex valued neural network is a neural network
which consists of complex valued input and/or weights and/or thresholds
and/or activation functions. Complex-valued neural networks
have been widening the scope of applications not only in electronics
and informatics, but also in social systems. One of the most important
applications of the complex valued neural network is in signal
processing. In Neural networks, generalized mean neuron model
(GMN) is often discussed and studied. The GMN includes a new
aggregation function based on the concept of generalized mean of all
the inputs to the neuron. This paper aims to present exhaustive results
of using Generalized Mean Neuron model in a complex-valued neural
network model that uses the back-propagation algorithm (called
-Complex-BP-) for learning. Our experiments results demonstrate the
effectiveness of a Generalized Mean Neuron Model in a complex
plane for signal processing over a real valued neural network. We
have studied and stated various observations like effect of learning
rates, ranges of the initial weights randomly selected, error functions
used and number of iterations for the convergence of error required on
a Generalized Mean neural network model. Some inherent properties
of this complex back propagation algorithm are also studied and
discussed.
Abstract: Bioinformatics and computational biology involve
the use of techniques including applied mathematics,
informatics, statistics, computer science, artificial intelligence,
chemistry, and biochemistry to solve biological problems
usually on the molecular level. Research in computational
biology often overlaps with systems biology. Major research
efforts in the field include sequence alignment, gene finding,
genome assembly, protein structure alignment, protein structure
prediction, prediction of gene expression and proteinprotein
interactions, and the modeling of evolution. Various
global rearrangements of permutations, such as reversals and
transpositions,have recently become of interest because of their
applications in computational molecular biology. A reversal is
an operation that reverses the order of a substring of a permutation.
A transposition is an operation that swaps two adjacent
substrings of a permutation. The problem of determining the
smallest number of reversals required to transform a given
permutation into the identity permutation is called sorting by
reversals. Similar problems can be defined for transpositions
and other global rearrangements. In this work we perform a
study about some genome rearrangement primitives. We show
how a genome is modelled by a permutation, introduce some
of the existing primitives and the lower and upper bounds
on them. We then provide a comparison of the introduced
primitives.
Abstract: One of the disadvantages of using OFDM is the larger
peak to averaged power ratio (PAPR) in its time domain signal. The
larger PAPR signal would course the fatal degradation of bit error
rate performance (BER) due to the inter-modulation noise in the nonlinear
channel. This paper proposes an improved DSI (Dummy
Sequence Insertion) method, which can achieve the better PAPR and
BER performances. The feature of proposed method is to optimize
the phase of each dummy sub-carrier so as to reduce the PAPR
performance by changing all predetermined phase coefficients in the
time domain signal, which is calculated for data sub-carriers and
dummy sub-carriers separately. To achieve the better PAPR
performance, this paper also proposes to employ the time-frequency
domain swapping algorithm for fine adjustment of phase coefficient
of the dummy subcarriers, which can achieve the less complexity of
processing and achieves the better PAPR and BER performances
than those for the conventional DSI method. This paper presents
various computer simulation results to verify the effectiveness of
proposed method as comparing with the conventional methods in the
non-linear channel.
Abstract: Thailand is one of the world-s leaders of rice
producers and exporters. Farmers have to increase the rice cultivation
frequency for serving the national increasing of export-s demand. It
leads to an elimination of rice residues by open burning which is the
quickest and costless management method. The open burning of rice
residue is one of the major causes of air pollutants and greenhouse
gas (GHG) emission. Under ASEAN agreement on trans-boundary
haze, Thailand set the master plan to mitigate air pollutant emission
from open burning of agricultural residues. In this master plan,
residues incorporation is promoted as alternative management
method to open burning. However, the assessment of both options in
term of GHG emission in order to investigate their contribution to
long-term global warming is still scarce or inexistent. In this study, a
method on rice residues assessment was first developed in order to
estimate and compare GHG emissions from rice cultivation under
rice residues open burning and the case with incorporation of the
same amount of rice residues, using 2006 IPCC guidelines for
emission estimation and Life Cycle Analysis technique. The
emission from rice cultivation in different preparing area practice
was also discussed.
Abstract: Computation of facility location problem for every
location in the country is not easy simultaneously. Solving the
problem is described by using cluster computing. A technique is to
design parallel algorithm by using local search with single swap
method in order to solve that problem on clusters. Parallel
implementation is done by the use of portable parallel programming,
Message Passing Interface (MPI), on Microsoft Windows Compute
Cluster. In this paper, it presents the algorithm that used local search
with single swap method and implementation of the system of a
facility to be opened by using MPI on cluster. If large datasets are
considered, the process of calculating a reasonable cost for a facility
becomes time consuming. The result shows parallel computation of
facility location problem on cluster speedups and scales well as
problem size increases.
Abstract: The use of renewable energy sources becomes more
necessary and interesting. As wider applications of renewable energy
devices at domestic, commercial and industrial levels has not only
resulted in greater awareness, but also significantly installed
capacities. In addition, biomass principally is in the form of woods,
which is a form of energy by humans for a long time. Gasification is
a process of conversion of solid carbonaceous fuel into combustible
gas by partial combustion. Many gasifier models have various
operating conditions; the parameters kept in each model are different.
This study applied experimental data, which has three inputs, which
are; biomass consumption, temperature at combustion zone and ash
discharge rate. One output is gas flow rate. For this paper, neural
network was used to identify the gasifier system suitable for the
experimental data. In the result,neural networkis usable to attain the
answer.
Abstract: This conference paper discusses a risk allocation problem for subprime investing banks involving investment in subprime structured mortgage products (SMPs) and Treasuries. In order to solve this problem, we develop a L'evy process-based model of jump diffusion-type for investment choice in subprime SMPs and Treasuries. This model incorporates subprime SMP losses for which credit default insurance in the form of credit default swaps (CDSs) can be purchased. In essence, we solve a mean swap-at-risk (SaR) optimization problem for investment which determines optimal allocation between SMPs and Treasuries subject to credit risk protection via CDSs. In this regard, SaR is indicative of how much protection investors must purchase from swap protection sellers in order to cover possible losses from SMP default. Here, SaR is defined in terms of value-at-risk (VaR). Finally, we provide an analysis of the aforementioned optimization problem and its connections with the subprime mortgage crisis (SMC).