Abstract: This paper discusses the landscape design that could
increase energy efficiency in a house. By planting trees in a house
compound, the tree shades prevent direct sunlight from heating up
the building, and it enables cooling off the surrounding air. The
requirement for air-conditioning could be minimized and the air
quality could be improved. During the life time of a tree, the saving
cost from the mentioned benefits could be up to US $ 200 for each
tree. The project intends to visually describe the landscape design in
a house compound that could enhance energy efficiency and
consequently lead to energy saving. The house compound model was
developed in three dimensions by using AutoCAD 2005, the
animation was programmed by using LightWave 3D softwares i.e.
Modeler and Layout to display the tree shadings in the wall. The
visualization was executed on a VRML Pad platform and
implemented on a web environment.
Abstract: The spiral angle of the elementary cellulose fibril in
the wood cell wall, often called microfibril angle, (MFA). Microfibril
angle in hardwood is one of the key determinants of solid timber
performance due to its strong influence on the stiffness, strength,
shrinkage, swelling, thermal-dynamics mechanical properties and
dimensional stability of wood. Variation of MFA (degree) in the S2
layer of the cell walls among Acacia mangium trees was determined
using small-angle X-ray scattering (SAXS). The length and
orientation of the microfibrils of the cell walls in the irradiated
volume of the thin samples are measured using SAXS and optical
microscope for 3D surface measurement. The undetermined
parameters in the analysis are the MFA, (M) and the standard
deviation (σФ) of the intensity distribution arising from the wandering
of the fibril orientation about the mean value. Nine separate pairs of
values are determined for nine different values of the angle of the
incidence of the X-ray beam relative to the normal to the radial
direction in the sample. The results show good agreement. The
curve distribution of scattered intensity for the real cell wall structure
is compared with that calculated with that assembly of rectangular
cells with the same ratio of transverse to radial cell wall length. It is
demonstrated that for β = 45°, the peaks in the curve intensity
distribution for the real and the rectangular cells coincide. If this
peak position is Ф45, then the MFA can be determined from the
relation M = tan-1 (tan Ф45 / cos 45°), which is precise for rectangular
cells. It was found that 92.93% of the variation of MFA can be
attributed to the distance from pith to bark. Here we shall present our
results of the MFA in the cell wall with respect to its shape, structure
and the distance from pith to park as an important fast check and yet
accurate towards the quality of wood, its uses and application.
Abstract: In this paper we present a method for gene ranking
from DNA microarray data. More precisely, we calculate the correlation
networks, which are unweighted and undirected graphs, from
microarray data of cervical cancer whereas each network represents
a tissue of a certain tumor stage and each node in the network
represents a gene. From these networks we extract one tree for
each gene by a local decomposition of the correlation network. The
interpretation of a tree is that it represents the n-nearest neighbor
genes on the n-th level of a tree, measured by the Dijkstra distance,
and, hence, gives the local embedding of a gene within the correlation
network. For the obtained trees we measure the pairwise similarity
between trees rooted by the same gene from normal to cancerous
tissues. This evaluates the modification of the tree topology due to
progression of the tumor. Finally, we rank the obtained similarity
values from all tissue comparisons and select the top ranked genes.
For these genes the local neighborhood in the correlation networks
changes most between normal and cancerous tissues. As a result
we find that the top ranked genes are candidates suspected to be
involved in tumor growth and, hence, indicates that our method
captures essential information from the underlying DNA microarray
data of cervical cancer.
Abstract: The Taiwan government has started to promote the “Plain Landscape Afforestation and Greening Program" since 2002. A key task of the program was the payment for environmental services (PES), entitled the “Plain Landscape Afforestation Policy" (PLAP), which was certificated by the Executive Yuan on August 31, 2001 and enacted on January 1, 2002. According to the policy, it is estimated that the total area of afforestation will be 25,100 hectares by December 31, 2007. Until the end of 2007, the policy had been enacted for six years in total and the actual area of afforestation was 8,919.18 hectares. Among them, Taiwan Sugar Corporation (TSC) was accounted for 7,960 hectares (with 2,450.83 hectares as public service area) which occupied 86.22% of the total afforestation area; the private farmland promoted by local governments was accounted for 869.18 hectares which occupied 9.75% of the total afforestation area. Based on the above, we observe that most of the afforestation area in this policy is executed by TSC, and the achievement ratio by TSC is better than by others. It implies that the success of the PLAP is seriously related to the execution of TSC. The objective of this study is to analyze the relevant policy planning of TSC-s participation in the PLAP, suggest complementary measures, and draw up effective adjustment mechanisms, so as to improve the effectiveness of executing the policy. Our main conclusions and suggestions are summarized as follows: 1. The main reason for TSC-s participation in the PLAP is based on their passive cooperation with the central government or company policy. Prior to TSC-s participation in the PLAP, their lands were mainly used for growing sugarcane. 2. The main factors of TSC-s consideration on the selection of tree species are based on the suitability of land and species. The largest proportion of tree species is allocated to economic forests, and the lack of technical instruction was the main problem during afforestation. Moreover, the method of improving TSC-s future development in leisure agriculture and landscape business becomes a key topic. 3. TSC has developed short and long-term plans on participating in the PLAP for the future. However, there is no great willingness or incentive on budgeting for such detailed planning. 4. Most people from TSC interviewed consider the requirements on PLAP unreasonable. Among them, an unreasonable requirement on the number of trees accounted for the greatest proportion; furthermore, most interviewees suggested that the government should continue to provide incentives even after 20 years. 5. Since the government shares the same goals as TSC, there should be sufficient cooperation and communication that support the technical instruction and reduction of afforestation cost, which will also help to improve effectiveness of the policy.
Abstract: It is well known that Logistic Regression is the gold
standard method for predicting clinical outcome, especially
predicting risk of mortality. In this paper, the Decision Tree method
has been proposed to solve specific problems that commonly use
Logistic Regression as a solution. The Biochemistry and
Haematology Outcome Model (BHOM) dataset obtained from
Portsmouth NHS Hospital from 1 January to 31 December 2001 was
divided into four subsets. One subset of training data was used to
generate a model, and the model obtained was then applied to three
testing datasets. The performance of each model from both methods
was then compared using calibration (the χ2 test or chi-test) and
discrimination (area under ROC curve or c-index). The experiment
presented that both methods have reasonable results in the case of the
c-index. However, in some cases the calibration value (χ2) obtained
quite a high result. After conducting experiments and investigating
the advantages and disadvantages of each method, we can conclude
that Decision Trees can be seen as a worthy alternative to Logistic
Regression in the area of Data Mining.