Abstract: The vast rural landscape in the southern United States
is conspicuously characterized by the hedgerow trees or groves. The
patchwork landscape of fields surrounded by high hedgerows is a
traditional and familiar feature of the American countryside.
Hedgerows are in effect linear strips of trees, groves, or woodlands,
which are often critical habitats for wildlife and important for the
visual quality of the landscape. As landscape interfaces, hedgerows
define the spaces in the landscape, give the landscape life and
meaning, and enrich ecologies and cultural heritages of the American
countryside. Although hedgerows were originally intended as fences
and to mark property and townland boundaries, they are not merely
the natural or man-made additions to the landscape--they have
gradually become “naturalized" into the landscape, deeply rooted in
the rural culture, and now formed an important component of the
southern American rural environment. However, due to the ever
expanding real estate industry and high demand for new residential
development, substantial areas of authentic hedgerow landscape in
the southern United States are being urbanized. Using Hudson Farm
as an example, this study illustrated guidelines of how hedgerows can
be integrated into town planning as green infrastructure and
landscape interface to innovate and direct sustainable land use, and
suggest ways in which such vernacular landscapes can be preserved
and integrated into new development without losing their contextual
inspiration.
Abstract: General requirements for knowledge representation in
the form of logic rules, applicable to design and control of industrial
processes, are formulated. Characteristic behavior of decision trees
(DTs) and rough sets theory (RST) in rules extraction from recorded
data is discussed and illustrated with simple examples. The
significance of the models- drawbacks was evaluated, using
simulated and industrial data sets. It is concluded that performance of
DTs may be considerably poorer in several important aspects,
compared to RST, particularly when not only a characterization of a
problem is required, but also detailed and precise rules are needed,
according to actual, specific problems to be solved.
Abstract: Much research into handwritten Thai character
recognition have been proposed, such as comparing heads of
characters, Fuzzy logic and structure trees, etc. This paper presents a
system of handwritten Thai character recognition, which is based on
the Ant-minor algorithm (data mining based on Ant colony
optimization). Zoning is initially used to determine each character.
Then three distinct features (also called attributes) of each character
in each zone are extracted. The attributes are Head zone, End point,
and Feature code. All attributes are used for construct the
classification rules by an Ant-miner algorithm in order to classify
112 Thai characters. For this experiment, the Ant-miner algorithm is
adapted, with a small change to increase the recognition rate. The
result of this experiment is a 97% recognition rate of the training set
(11200 characters) and 82.7% recognition rate of unseen data test
(22400 characters).
Abstract: In ad hoc networks, the main issue about designing of protocols is quality of service, so that in wireless sensor networks the main constraint in designing protocols is limited energy of sensors. In fact, protocols which minimize the power consumption in sensors are more considered in wireless sensor networks. One approach of reducing energy consumption in wireless sensor networks is to reduce the number of packages that are transmitted in network. The technique of collecting data that combines related data and prevent transmission of additional packages in network can be effective in the reducing of transmitted packages- number. According to this fact that information processing consumes less power than information transmitting, Data Aggregation has great importance and because of this fact this technique is used in many protocols [5]. One of the Data Aggregation techniques is to use Data Aggregation tree. But finding one optimum Data Aggregation tree to collect data in networks with one sink is a NP-hard problem. In the Data Aggregation technique, related information packages are combined in intermediate nodes and form one package. So the number of packages which are transmitted in network reduces and therefore, less energy will be consumed that at last results in improvement of longevity of network. Heuristic methods are used in order to solve the NP-hard problem that one of these optimization methods is to solve Simulated Annealing problems. In this article, we will propose new method in order to build data collection tree in wireless sensor networks by using Simulated Annealing algorithm and we will evaluate its efficiency whit Genetic Algorithm.
Abstract: In this paper we consider quantum motion integrals
depended on the algebraic reconstruction of BPHZ method for
perturbative renormalization in two different procedures. Then based
on Bogoliubov character and Baker-Campbell-Hausdorff (BCH) formula,
we show that how motion integral condition on components
of Birkhoff factorization of a Feynman rules character on Connes-
Kreimer Hopf algebra of rooted trees can determine a family of fixed
point equations.
Abstract: Large volumes of fingerprints are collected and stored
every day in a wide range of applications, including forensics, access
control etc. It is evident from the database of Federal Bureau of
Investigation (FBI) which contains more than 70 million finger
prints. Compression of this database is very important because of this
high Volume. The performance of existing image coding standards
generally degrades at low bit-rates because of the underlying block
based Discrete Cosine Transform (DCT) scheme. Over the past
decade, the success of wavelets in solving many different problems
has contributed to its unprecedented popularity. Due to
implementation constraints scalar wavelets do not posses all the
properties which are needed for better performance in compression.
New class of wavelets called 'Multiwavelets' which posses more
than one scaling filters overcomes this problem. The objective of this
paper is to develop an efficient compression scheme and to obtain
better quality and higher compression ratio through multiwavelet
transform and embedded coding of multiwavelet coefficients through
Set Partitioning In Hierarchical Trees algorithm (SPIHT) algorithm.
A comparison of the best known multiwavelets is made to the best
known scalar wavelets. Both quantitative and qualitative measures of
performance are examined for Fingerprints.
Abstract: The aim of the work presented here was to either use
existing forest dynamic simulation models or calibrate a new one
both within the SYMFOR framework with the purpose of examining
changes in stand level basal area and functional composition in
response to selective logging considering trees > 10 cm d.b.h for two
areas of undisturbed Amazonian non flooded tropical forest in Brazil
and one in Peru. Model biological realism was evaluated for forest in
the undisturbed and selectively logged state and it was concluded that
forest dynamics were realistically represented. Results of the logging
simulation experiments showed that in relation to undisturbed forest
simulation subject to no form of harvesting intervention there was a
significant amount of change over a 90 year simulation period that
was positively proportional to the intensity of logging. Areas which
had in the dynamic equilibrium of undisturbed forest a greater
proportion of a specific ecological guild of trees known as the light
hardwoods (LHW’s) seemed to respond more favorably in terms of
less deviation but only within a specific range of baseline forest
composition beyond which compositional diversity became more
important. These finds are in line partially with practical management
experience and partiality basic systematics theory respectively.
Abstract: Smoke from domestic wood burning has been
identified as a major contributor to air pollution, motivating detailed
emission measurements under controlled conditions. A series of
experiments was performed to characterise the emissions from wood
combustion in a fireplace and in a woodstove of two common species
of trees grown in Spain: Pyrenean oak (Quercus pyrenaica) and
black poplar (Populus nigra). Volatile organic compounds (VOCs) in
the exhaust emissions were collected in Tedlar bags, re-sampled in
sorbent tubes and analysed by thermal desorption-gas
chromatography-flame ionisation detection. Pyrenean oak presented
substantially higher emissions in the woodstove than in the fireplace,
for the majority of compounds. The opposite was observed for
poplar. Among the 45 identified species, benzene and benzenerelated
compounds represent the most abundant group, followed by
oxygenated VOCs and aliphatics. Emission factors obtained in this
study are generally of the same order than those reported for
residential experiments in the USA.
Abstract: Contamination of heavy metals in tin tailings has
caused an interest in the scientific approach of their remediation. One
of the approaches is through phytoremediation, which is using tree
species to extract the heavy metals from the contaminated soils. Tin
tailings comprise of slime and sand tailings. This paper reports only
on the finding of the four timber species namely Acacia mangium,
Hopea odorata, Intsia palembanica and Swietenia macrophylla on
the removal of cadmium (Cd) and lead (Pb) from the slime tailings.
The methods employed for sampling and soil analysis are established
methods. Six trees of each species were randomly selected from a
0.25 ha plot for extraction and determination of their heavy metals.
The soil samples were systematically collected according to 5 x 5 m
grid from each plot. Results showed that the concentration of heavy
metals in soils and trees varied according to species. Higher
concentration of heavy metals was found in the stem than the
primary roots of all the species. A. Mangium accumulated the highest
total amount of Pb per hectare basis.
Abstract: In this paper, acoustic techniques are used to detect hidden insect infestations of date palm tress (Phoenix dactylifera L.). In particular, we use an acoustic instrument for early discovery of the presence of a destructive insect pest commonly known as the Red Date Palm Weevil (RDPW) and scientifically as Rhynchophorus ferrugineus (Olivier). This type of insect attacks date palm tress and causes irreversible damages at late stages. As a result, the infected trees must be destroyed. Therefore, early presence detection is a major part in controlling the spread and economic damage caused by this type of infestation. Furthermore monitoring and early detection of the disease can asses in taking appropriate measures such as isolating or treating the infected trees. The acoustic system is evaluated in terms of its ability for early discovery of hidden bests inside the tested tree. When signal acquisitions is completed for a number of date palms, a signal processing technique known as time-frequency analysis is evaluated in terms of providing an estimate that can be visually used to recognize the acoustic signature of the RDPW. The testing instrument was tested in the laboratory first then; it was used on suspected or infested tress in the field. The final results indicate that the acoustic monitoring approach along with signal processing techniques are very promising for the early detection of presence of the larva as well as the adult pest in the date palms.
Abstract: An improved processing description to be employed in biosonar signal processing in a cochlea model is proposed and examined. It is compared to conventional models using a modified discrimination analysis and both are tested. Their performances are evaluated with echo data captured from natural targets (trees).Results indicate that the phase characteristics of low-pass filters employed in the echo processing have a significant effect on class separability for this data.
Abstract: Distributed Computing Systems are usually considered the most suitable model for practical solutions of many parallel algorithms. In this paper an enhanced distributed system is presented to improve the time complexity of Binary Indexed Trees (BIT). The proposed system uses multi-uniform processors with identical architectures and a specially designed distributed memory system. The analysis of this system has shown that it has reduced the time complexity of the read query to O(Log(Log(N))), and the update query to constant complexity, while the naive solution has a time complexity of O(Log(N)) for both queries. The system was implemented and simulated using VHDL and Verilog Hardware Description Languages, with xilinx ISE 10.1, as the development environment and ModelSim 6.1c, similarly as the simulation tool. The simulation has shown that the overhead resulting by the wiring and communication between the system fragments could be fairly neglected, which makes it applicable to practically reach the maximum speed up offered by the proposed model.
Abstract: The main purpose of this research was to study how to communicate the identity of the Bangpoo, Samu tPrakan province for ecotourism. The qualitative data was collected through studying related materials, exploring the area, in-depth interviews with three groups of people: three directly responsible officers who were key informants of the district, twenty foreign tourists and five Thai tourist guides. A content analysis was used to analyze the qualitative data. The two main findings of the study were as follows:
The identity of Bangpoo, Samut Prakan province. This establishment was near the Mouth of the Gulf of Thailand for normal people and tourists, consisting of rest accommodations. There are restaurants where food and drinks are served, rich mangrove forests, Banpoo seaside resort and mangrove trees. Bangpoo seaside resort is characterized by muddy beacheswhere the greatest number of seagulls can be seen from March to May each year.
The communication of the identity of Bangpoo, Samut Prakan province which the researcher could find and design to present in English materials can be summed up in 3 items: 1) The history of Bangpoo, Samut Prakan province 2) The Learning center of Ecotourism: Seagulls and Mangrove forest 3) How to keep Banpoo, Samut Prakran province for ecotourism.
Abstract: Many supervised induction algorithms require discrete
data, even while real data often comes in a discrete
and continuous formats. Quality discretization of continuous
attributes is an important problem that has effects on speed,
accuracy and understandability of the induction models. Usually,
discretization and other types of statistical processes are applied
to subsets of the population as the entire population is practically
inaccessible. For this reason we argue that the discretization
performed on a sample of the population is only an estimate of
the entire population. Most of the existing discretization methods,
partition the attribute range into two or several intervals using
a single or a set of cut points. In this paper, we introduce a
technique by using resampling (such as bootstrap) to generate
a set of candidate discretization points and thus, improving the
discretization quality by providing a better estimation towards
the entire population. Thus, the goal of this paper is to observe
whether the resampling technique can lead to better discretization
points, which opens up a new paradigm to construction of
soft decision trees.
Abstract: Recommender systems are usually regarded as an
important marketing tool in the e-commerce. They use important
information about users to facilitate accurate recommendation. The
information includes user context such as location, time and interest
for personalization of mobile users. We can easily collect information
about location and time because mobile devices communicate with the
base station of the service provider. However, information about user
interest can-t be easily collected because user interest can not be
captured automatically without user-s approval process. User interest
usually represented as a need. In this study, we classify needs into two
types according to prior research. This study investigates the
usefulness of data mining techniques for classifying user need type for
recommendation systems. We employ several data mining techniques
including artificial neural networks, decision trees, case-based
reasoning, and multivariate discriminant analysis. Experimental
results show that CHAID algorithm outperforms other models for
classifying user need type. This study performs McNemar test to
examine the statistical significance of the differences of classification
results. The results of McNemar test also show that CHAID performs
better than the other models with statistical significance.
Abstract: Computing and maintaining network structures for efficient
data aggregation incurs high overhead for dynamic events
where the set of nodes sensing an event changes with time. Moreover,
structured approaches are sensitive to the waiting time that is used
by nodes to wait for packets from their children before forwarding
the packet to the sink. An optimal routing and data aggregation
scheme for wireless sensor networks is proposed in this paper. We
propose Tree on DAG (ToD), a semistructured approach that uses
Dynamic Forwarding on an implicitly constructed structure composed
of multiple shortest path trees to support network scalability. The key
principle behind ToD is that adjacent nodes in a graph will have
low stretch in one of these trees in ToD, thus resulting in early
aggregation of packets. Based on simulations on a 2,000-node Mica2-
based network, we conclude that efficient aggregation in large-scale
networks can be achieved by our semistructured approach.
Abstract: Phylogenetic tree is a graphical representation of the
evolutionary relationship among three or more genes or organisms.
These trees show relatedness of data sets, species or genes
divergence time and nature of their common ancestors. Quality of a
phylogenetic tree requires parsimony criterion. Various approaches
have been proposed for constructing most parsimonious trees. This
paper is concerned about calculating and optimizing the changes of
state that are needed called Small Parsimony Algorithms. This paper
has proposed enhanced small parsimony algorithm to give better
score based on number of evolutionary changes needed to produce
the observed sequence changes tree and also give the ancestor of the
given input.
Abstract: This paper investigates the issue of building decision
trees from data with imprecise class values where imprecision is
encoded in the form of possibility distributions. The Information
Affinity similarity measure is introduced into the well-known gain
ratio criterion in order to assess the homogeneity of a set of
possibility distributions representing instances-s classes belonging to
a given training partition. For the experimental study, we proposed an
information affinity based performance criterion which we have used
in order to show the performance of the approach on well-known
benchmarks.
Abstract: The major objective of this paper is to introduce a new method to select genes from DNA microarray data. As criterion to select genes we suggest to measure the local changes in the correlation graph of each gene and to select those genes whose local changes are largest. More precisely, we calculate the correlation networks from DNA 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 tumor progression. 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. This indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.
Abstract: Influence of octane and benzene on plant cell
ultrastructure and enzymes of basic metabolism, such as nitrogen
assimilation and energy generation have been studied. Different
plants: perennial ryegrass (Lolium perenne) and alfalfa (Medicago
sativa); crops- maize (Zea mays L.) and bean (Phaseolus vulgaris);
shrubs – privet (Ligustrum sempervirens) and trifoliate orange
(Poncirus trifoliate); trees - poplar (Populus deltoides) and white
mulberry (Morus alba L.) were exposed to hydrocarbons of different
concentrations (1, 10 and 100 mM). Destructive changes in bean and
maize leaves cells ultrastructure under the influence of benzene
vapour were revealed at the level of photosynthetic and energy
generation subcellular organells. Different deviations at the level of
subcellular organelles structure and distribution were observed in
alfalfa and ryegrass root cells under the influence of benzene and
octane, absorbed through roots. The level of destructive changes is
concentration dependent. Benzene at low 1 and 10 mM concentration
caused the increase in glutamate dehydrogenase (GDH) activity in
maize roots and leaves and in poplar and mulberry shoots, though to
higher extent in case of lower, 1mM concentration. The induction
was more intensive in plant roots. The highest tested 100mM
concentration of benzene was inhibitory to the enzyme in all plants.
Octane caused induction of GDH in all grassy plants at all tested
concentrations; however the rate of induction decreased parallel to
increase of the hydrocarbon concentration. Octane at concentration 1
mM caused induction of GDH in privet, trifoliate and white mulberry
shoots. The highest, 100mM octane was characterized by inhibitory
effect to GDH activity in all plants. Octane had inductive effect on
malate dehydrogenase in almost all plants and tested concentrations,
indicating the intensification of Trycarboxylic Acid Cycle.
The data could be suggested for elaboration of criteria for plant
selection for phytoremediation of oil hydrocarbons contaminated
soils.