Abstract: The problem of agricultural-soil pollution is closely
linked to the production of ecologically pure foodstuffs and to human health. An important task, therefore, is to rehabilitate agricultural
soils with the help of state-of-the-art biotechnologies, based on the use of metal-accumulating plants. In this work, on the basis of
literature data and the results of prior research from this laboratory, plants were selected for which the growing technology is well
developed and which are widespread locally: sugar sorghum (Sorghum saccharatum), sudangrass (Sorghum sudanense (Piper.)
Stapf.), and sunflower (Helianthus annuus L.). I report on laboratory
experiments designed to study the influence of synthetic indole-3-
acetic acid and the extracellular indole-3-acetic acid released by the
plant-growth-promoting rhizobacterium Azospirillum brasilense Sp245 on growth of and arsenic accumulation by these plants.
Abstract: The present study aimed to investigate whether
chlorophyll meter readings (SPAD) can be used as criterion of singleplant
selection in maize breeding. Experimentation was performed at
the ultra-low density of 0.74 plants/m2 in order the potential yield per
plant to be fully expressed. R-31 honeycomb experiments were
conducted in three different areas in Greece (Thessaloniki, Giannitsa
and Florina) using 30 inbred lines at well-watered and water-stressed
conditions during the 2012 growing season. The chlorophyll meter
readings had higher rates at dry conditions, except location of
Giannitsa where differences were not significant. Genotypes of
highest chlorophyll meter readings were consistent across areas,
emphasizing on the character’s stability. A positive correlation
between the chlorophyll meter readings and grain yield was
strengthening over time and culminated at the physiological maturity
stage. There was a clear sign that the chlorophyll meter readings has
the potential to be used for the selection of stress-adaptive genotypes
and may permit modern maize to be grown at wider range of
environments addressing the climate change scenarios.
Abstract: Power system stability enhancement by simultaneous tuning of a Power System Stabilizer (PSS) and a Static Var Compensator (SVC)-based controller is thoroughly investigated in this paper. The coordination among the proposed damping stabilizers and the SVC internal voltage regulators has also been taken into consideration. The design problem is formulated as an optimization problem with a time-domain simulation-based objective function and Real-Coded Genetic Algorithm (RCGA) is employed to search for optimal controller parameters. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. The nonlinear simulation results are presented to show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions and disturbances. Further, the proposed design approach is found to be robust and improves stability effectively even under small disturbance and unbalanced fault conditions.
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: Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set.
Abstract: Digital broadcasting has been an area of active
research, development, innovation and business models development
in recent years. This paper presents a survey on the characteristics of
the digital terrestrial television broadcasting (DTTB) standards, and
implementation status of DTTB worldwide showing the standards
adopted. It is clear that only the developed countries and some in the
developing ones shall be able to beat the ITU set analogue to digital
broadcasting migration deadline because of the challenges that these
countries faces in digitizing their terrestrial broadcasting. The
challenges to keep on track the DTTB migration plan are also
discussed in this paper. They include financial, technology gap,
policies alignment with DTTB technology, etc. The reported
performance comparisons for the different standards are also
presented. The interesting part is that the results for many
comparative studies depends to a large extent on the objective behind
such studies, hence counter claims are common.
Abstract: We consider the methods of construction simple
polygons for a set S of n points and applying them for searching the
minimal area polygon. In this paper we propose the approximate
algorithm, which generates the simple polygonalizations of a fixed
set of points and finds the minimal area polygon, in O (n3) time and
using O(n2) memory.
Abstract: Time series forecasting is an important and widely
popular topic in the research of system modeling. This paper
describes how to use the hybrid PSO-RLSE neuro-fuzzy learning
approach to the problem of time series forecasting. The PSO
algorithm is used to update the premise parameters of the
proposed prediction system, and the RLSE is used to update the
consequence parameters. Thanks to the hybrid learning (HL)
approach for the neuro-fuzzy system, the prediction performance
is excellent and the speed of learning convergence is much faster
than other compared approaches. In the experiments, we use the
well-known Mackey-Glass chaos time series. According to the
experimental results, the prediction performance and accuracy in
time series forecasting by the proposed approach is much better
than other compared approaches, as shown in Table IV. Excellent
prediction performance by the proposed approach has been
observed.
Abstract: This paper presents the methodology from machine
learning approaches for short-term rain forecasting system. Decision
Tree, Artificial Neural Network (ANN), and Support Vector Machine
(SVM) were applied to develop classification and prediction models
for rainfall forecasts. The goals of this presentation are to
demonstrate (1) how feature selection can be used to identify the
relationships between rainfall occurrences and other weather
conditions and (2) what models can be developed and deployed for
predicting the accurate rainfall estimates to support the decisions to
launch the cloud seeding operations in the northeastern part of
Thailand. Datasets collected during 2004-2006 from the
Chalermprakiat Royal Rain Making Research Center at Hua Hin,
Prachuap Khiri khan, the Chalermprakiat Royal Rain Making
Research Center at Pimai, Nakhon Ratchasima and Thai
Meteorological Department (TMD). A total of 179 records with 57
features was merged and matched by unique date. There are three
main parts in this work. Firstly, a decision tree induction algorithm
(C4.5) was used to classify the rain status into either rain or no-rain.
The overall accuracy of classification tree achieves 94.41% with the
five-fold cross validation. The C4.5 algorithm was also used to
classify the rain amount into three classes as no-rain (0-0.1 mm.),
few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall
accuracy of classification tree achieves 62.57%. Secondly, an ANN
was applied to predict the rainfall amount and the root mean square
error (RMSE) were used to measure the training and testing errors of
the ANN. It is found that the ANN yields a lower RMSE at 0.171 for
daily rainfall estimates, when compared to next-day and next-2-day
estimation. Thirdly, the ANN and SVM techniques were also used to
classify the rain amount into three classes as no-rain, few-rain, and
moderate-rain as above. The results achieved in 68.15% and 69.10%
of overall accuracy of same-day prediction for the ANN and SVM
models, respectively. The obtained results illustrated the comparison
of the predictive power of different methods for rainfall estimation.
Abstract: The paper represents a reflection on how to select proper indicators to assess the progress of regional contexts towards a knowledge-based society. Taking the first research methodologies elaborated at an international level (World Bank, OECD, etc.) as a reference point, this work intends to identify a set of indicators of the knowledge economy suitable to adequately understand in which manner and to which extent the territorial development dynamics are correlated with the knowledge-base of the considered local society. After a critical survey of the variables utilized within other approaches adopted by international or national organizations, this paper seeks to elaborate a framework of variables, named Regional Knowledge Economy Indicators (ReKEI), necessary to describe the knowledge-based relations of subnational socio-economic contexts. The realization of this framework has a double purpose: an analytical one consisting in highlighting the regional differences in the governance of knowledge based processes, and an operative one consisting in providing some reference parameters for contributing to increasing the effectiveness of those economic policies aiming at enlarging the knowledge bases of local societies.
Abstract: Cardiovascular diseases, principally atherosclerosis, are responsible for 30% of world deaths. Atherosclerosis is due to the formation of plaque. The fatty plaque may be at risk of rupture, leading typically to stroke and heart attack. The plaque is usually associated with a high degree of lumen reduction, called a stenosis.It is increasingly recognized that the initiation and progression of disease and the occurrence of clinical events is a complex interplay between the local biomechanical environment and the local vascular biology. The aim of this study is to investigate the flow behavior through a stenosed artery. A physical experiment was performed using an artery model and blood analogue fluid. An axisymmetric model constructed consists of contraction and expansion region that follow a mathematical form of cosine function. A 30% diameter reduction was used in this study. The flow field was measured using particle image velocimetry (PIV). Spherical particles with 20μm diameter were seeded in a water-glycerol-NaCl mixture. Steady flow Reynolds numbers are 250. The area of interest is the region after the stenosis where the flow separation occurs. The velocity field was measured and the velocity gradient was investigated. There was high particle concentration in the recirculation zone. High velocity gradient formed immediately after the stenosis throat created a lift force that enhanced particle migration to the flow separation area.
Abstract: This research was conducted to determine responses
of chickpeas to drought in different periods (early period, late period,
no-irrigation, two times irrigation as control). The trial was made in
“Randomized Complete Block Design" with three replications on
2010 and 2011 years in Konya-Turkey. Genotypes were consisted
from 7 lines of ICARDA, 2 certified lines and 1 local population. The
results showed that; as means of years and genotypes, early period
stress showed highest (207.47 kg da-1) seed yield and it was followed
by control (202.33 kg da-1), late period (144.64 kg da-1) and normal
(106.93 kg da-1) stress applications. The genotypes were affected too
much by drought and, the lowest seed was taken from non-irrigated
plots. As the means of years and stress applications, the highest
(196.01 kg da-1) yield was taken from genotype 22255. The reason of
yield variation could be derived from different responses of
genotypes to drought.
Abstract: Although lots of research work has been done for
human pose recognition, the view-point of cameras is still critical
problem of overall recognition system. In this paper, view-point
insensitive human pose recognition is proposed. The aims of the
proposed system are view-point insensitivity and real-time processing.
Recognition system consists of feature extraction module, neural
network and real-time feed forward calculation. First, histogram-based
method is used to extract feature from silhouette image and it is
suitable for represent the shape of human pose. To reduce the
dimension of feature vector, Principle Component Analysis(PCA) is
used. Second, real-time processing is implemented by using Compute
Unified Device Architecture(CUDA) and this architecture improves
the speed of feed-forward calculation of neural network. We
demonstrate the effectiveness of our approach with experiments on
real environment.
Abstract: Increasing demand on the performance of Subsea
Production Systems (SPS) suggests a need for more detailed
investigation of fluid behavior taking place in subsea equipment.
Complete CFD cool down analyses of subsea equipment are very
time demanding. The objective of this paper is to investigate a
Locked CFD approach, which enables significant reduction of the
computational time and at the same time maintains sufficient
accuracy during thermal cool down simulations. The result
comparison of a dead leg simulation using the Full CFD and the three
LCFD-methods confirms the validity of the locked flow field
assumption for the selected case. For the tested case the LCFD
simulation speed up by factor of 200 results in the absolute thermal
error of 0.5 °C (3% relative error), speed up by factor of 10 keeps the
LCFD results within 0.1 °C (0.5 % relative error) comparing to the
Full CFD.
Abstract: In this paper, a new approach for quality assessment
tasks in lossy compressed digital video is proposed. The research
activity is based on the visual fixation data recorded by an eye
tracker. The method involved both a new paradigm for subjective
quality evaluation and the subsequent statistical analysis to match
subjective scores provided by the observer to the data obtained from
the eye tracker experiments. The study brings improvements to the
state of the art, as it solves some problems highlighted in literature.
The experiments prove that data obtained from an eye tracker can be
used to classify videos according to the level of impairment due to
compression. The paper presents the methodology, the experimental
results and their interpretation. Conclusions suggest that the eye
tracker can be useful in quality assessment, if data are collected and
analyzed in a proper way.
Abstract: One of the important tropical diseases is
Chikunkunya. This disease is transmitted between the human by the
insect-borne virus, of the genus Alphavirus. It occurs in Africa, Asia
and the Indian subcontinent. In Thailand, the incidences due to this
disease are increasing every year. In this study, the transmission of
this disease is studied through dynamical model analysis.
Abstract: This research was conducted in the Lower Namkam
Irrigation Project situated in the Namkam River Basin in Thailand.
Degradation of groundwater quality in some areas is caused by saline
soil spots beneath ground surface. However, the tail regulated gate
structure on the Namkam River, a lateral stream of the Mekong
River. It is aimed for maintaining water level in the river at +137.5 to
+138.5 m (MSL) and flow to the irrigation canals based on a gravity
system since July 2009. It might leach some saline soil spots from
underground to soil surface if lack of understanding of the
conjunctive surface water and groundwater behaviors. This research
has been conducted by continuously the observing of both shallow
and deep groundwater level and quality from existing observation
wells. The simulation of surface water was carried out using a
hydrologic modeling system (HEC-HMS) to compute the ungauged
side flow catchments as the lateral flows for the river system model
(HEC-RAS). The constant water levels in the upstream of the
operated gate caused a slight rising up of shallow groundwater level
when compared to the water table. However, the groundwater levels
in the confined aquifers remained less impacted than in the shallow
aquifers but groundwater levels in late of wet season in some wells
were higher than the phreatic surface. This causes salinization of the
groundwater at the soil surface and might affect some crops. This
research aims for the balance of water stage in the river and efficient
groundwater utilization in this area.
Abstract: This paper presents a novel template-based method to
detect objects of interest from real images by shape matching. To
locate a target object that has a similar shape to a given template
boundary, the proposed method integrates three components: contour
grouping, partial shape matching, and boundary verification. In the
first component, low-level image features, including edges and
corners, are grouped into a set of perceptually salient closed contours
using an extended ratio-contour algorithm. In the second component,
we develop a partial shape matching algorithm to identify the
fractions of detected contours that partly match given template
boundaries. Specifically, we represent template boundaries and
detected contours using landmarks, and apply a greedy algorithm to
search the matched landmark subsequences. For each matched
fraction between a template and a detected contour, we estimate an
affine transform that transforms the whole template into a hypothetic
boundary. In the third component, we provide an efficient algorithm
based on oriented edge lists to determine the target boundary from
the hypothetic boundaries by checking each of them against image
edges. We evaluate the proposed method on recognizing and
localizing 12 template leaves in a data set of real images with clutter
back-grounds, illumination variations, occlusions, and image noises.
The experiments demonstrate the high performance of our proposed
method1.
Abstract: This research paper presents a framework on how to
build up malware dataset.Many researchers took longer time to
clean the dataset from any noise or to transform the dataset into a
format that can be used straight away for testing. Therefore, this
research is proposing a framework to help researchers to speed up
the malware dataset cleaningprocesses which later can be used for
testing. It is believed, an efficient malware dataset cleaning
processes, can improved the quality of the data, thus help to improve
the accuracy and the efficiency of the subsequent analysis. Apart
from that, an in-depth understanding of the malware taxonomy is
also important prior and during the dataset cleaning processes. A
new Trojan classification has been proposed to complement this
framework.This experiment has been conducted in a controlled lab
environment and using the dataset from VxHeavens dataset. This
framework is built based on the integration of static and dynamic
analyses, incident response method and knowledge database
discovery (KDD) processes.This framework can be used as the basis
guideline for malware researchers in building malware dataset.
Abstract: This paper presents an experimental case using sensory thermography to describe temperatures behavior on median nerve once an activity of repetitive motion was done. Thermography is a noninvasive technique without biological hazard and not harm at all times and has been applied in many experiments to seek for temperature patterns that help to understand diseases like cancer and cumulative trauma disorders (CTD’s). An infrared sensory thermography technology was developed to execute this study. Three women in good shape were selected for the repetitive motion tests for 4 days, two right-handed women and 1 left handed woman, two sensory thermographers were put on both median nerve wrists to get measures. The evaluation time was of 3 hours 30 minutes in a controlled temperature, 20 minutes of stabilization time at the beginning and end of the operation. Temperatures distributions are statistically evaluated and showed similar temperature patterns behavior.