Abstract: Utilizing echoic intension and distribution from different organs and local details of human body, ultrasonic image can catch important medical pathological changes, which unfortunately may be affected by ultrasonic speckle noise. A feature preserving ultrasonic image denoising and edge enhancement scheme is put forth, which includes two terms: anisotropic diffusion and edge enhancement, controlled by the optimum smoothing time. In this scheme, the anisotropic diffusion is governed by the local coordinate transformation and the first and the second order normal derivatives of the image, while the edge enhancement is done by the hyperbolic tangent function. Experiments on real ultrasonic images indicate effective preservation of edges, local details and ultrasonic echoic bright strips on denoising by our scheme.
Abstract: Object: Review recent publications of patient safety
culture to investigate the relationship between leadership behavior,
safety culture, and safety performance in the healthcare industry.
Method: This study is a cross-sectional study, 350 questionnaires were
mailed to hospital workers with 195 valid responses obtained, and a
55.7% valid response rate. Confirmatory factor analysis (CFA) was
carried out to test the factor structure and determine if the composite
reliability was significant with a factor loading of >0.5, resulting in an
acceptable model fit. Results: Through the analysis of One-way
ANOVA, the results showed that physicians significantly have more
negative patient safety culture perceptions and safety performance
perceptions than non- physicians. Conclusions: The path analysis
results show that leadership behavior affects safety culture and safety
performance in the health care industry. Safety performance was
affected and improved with contingency leadership and a positive
patient safety organization culture. The study suggests improving
safety performance by providing a well-managed system that
includes: consideration of leadership, hospital worker training
courses, and a solid safety reporting system.
Abstract: The purpose of this study was to suggest some optimal
waterfront scales around small rivers by reviewing domestic and
foreign survey reports about concept and relevant systems of the
ecological cities, analyzing the data collected from a survey about
scales and facilities of waterfront green zones around small rivers. The
questionnaire survey was conducted by sampling professional
designers, developers, the citizens living in the GunpoSanbon district
covered by no river system and the citizens living in such districts
covered by a river system. The question items were about need, uses,
scale and facilities of waterfront in common, and about satisfaction
with waterfront in case of citizen groups. In short, most of the subjects
in 5 groups preferred 10~20 wide waterfront green zone. And it is
judged that the results of this study about uses and facilities of the
waterfront green zone and its scales would provide for some basic data
useful to future waterfront green zone and urban development plans.
Abstract: Using entropy weight and TOPSIS method, a
comprehensive evaluation is done on the development level of
Chinese regional service industry in this paper. Firstly, based on
existing research results, an evaluation index system is constructed
from the scale of development, the industrial structure and the
economic benefits. An evaluation model is then built up based on
entropy weight and TOPSIS, and an empirical analysis is conducted on
the development level of service industries in 31 Chinese provinces
during 2006 and 2009 from the two dimensions or time series and
cross section, which provides new idea for assessing regional service
industry. Furthermore, the 31 provinces are classified into four
categories based on the evaluation results, and deep analysis is carried
out on the evaluation results.
Abstract: In an effort to understand the preliminary effects of aerodynamic stress on alveolar epithelial cells, we developed a multifluidic cell culture platform capable of supporting alveolar cultures at an air-liquid interface under constant air flow and exposure to varying pressure stimuli on the apical side while providing nourishment on the basolateral plane. Our current study involved utilizing the platform to study the effect of basement membrane coating and addition of dexamethasone on cellular response to pressure in A549 and H441 alveolar epithelial cells.
Abstract: With deep development of software reuse, componentrelated
technologies have been widely applied in the development of
large-scale complex applications. Component identification (CI) is
one of the primary research problems in software reuse, by analyzing
domain business models to get a set of business components with high
reuse value and good reuse performance to support effective reuse.
Based on the concept and classification of CI, its technical stack is
briefly discussed from four views, i.e., form of input business models,
identification goals, identification strategies, and identification
process. Then various CI methods presented in literatures are
classified into four types, i.e., domain analysis based methods,
cohesion-coupling based clustering methods, CRUD matrix based
methods, and other methods, with the comparisons between these
methods for their advantages and disadvantages. Additionally, some
insufficiencies of study on CI are discussed, and the causes are
explained subsequently. Finally, it is concluded with some
significantly promising tendency about research on this problem.
Abstract: In this paper, a class of recurrent neural networks (RNNs) with variable delays are studied on almost periodic time scales, some sufficient conditions are established for the existence and global exponential stability of the almost periodic solution. These results have important leading significance in designs and applications of RNNs. Finally, two examples and numerical simulations are presented to illustrate the feasibility and effectiveness of the results.
Abstract: BioEnergy is an archetypal appropriate technology
and alternate source of energy in rural areas of China, and can meet
the basic need for cooking fuel in rural areas. The paper introduces
with an alternate mean of research that can accelerate the biogas
energy production. Tithonia diversifolia or the Tree marigold can be
hailed as mesophillic anaerobic digestion to increase the production
of more Bioenergy. Tithonia diversifolia is very native to Mexico and
Central America, which can be served as ornamental plants- green
manure and can prevent soil erosion. Tithonia diversifolia is widely
grown and known to Asia, Africa, America and Australia as well.
Nowadays, Considering China’s geographical condition it is found
that Tithonia diversifolia is widely growing plant in the many tropical
and subtropical regions of southern Yunnan- which can have great
usage in accelerating and increasing the Bioenergy production
technology. The paper discussed aiming at proving possibility that
Tithonia diversifolia can be applied in biogas fermentation and its
biogas production potential, the research carried experiment on
Tithonia diversifolia biogas fermentation under the mesophilic
condition (35 Celsius Degree). The result revealed that Tithonia
diversifolia can be used as biogas fermentative material, and 6%
concentration can get the best biogas production, with the TS biogas
production rate 656mL/g and VS biogas production rate 801mL/g. It
is well addressed that Tithonia diversifolia grows wildly in 53
Counties and 9 cities of Yunnan Province, which mainly grows in
form of the road side plants, the edge of the field, countryside, forest
edge, open space; of which demersum-natures can form dense
monospecific beds -causing serious harm to agricultural production
landforms threatening the ecological system as a potentially harmful
exotic plant. There are also found the three types of invasive daisy
alien plants -Eupatorium adenophorum, Eupatorium Odorata and
Tithonia diversifolia in Yunnan Province of China-among them the
Tithonia diversifolia is responsible for causing serious harm to
agricultural production. In this paper we have designed the
experimental explanation of Biogas energy production that requires
anaerobic environment and some microbes; Tithonia diversifolia
plant has been taken into consideration while carrying experiments
and with successful resulting of generating more BioEnergy
emphasizing on the practical applications of Tithonia diversifolia.
This paper aims at- to find a new mechanism to provide a more
scientific basis for the development of this plant herbicides in Biogas
energy and to improve the utilization throughout the world as well.
Abstract: The projection methods, usually viewed as the methods
for computing eigenvalues, can also be used to estimate pseudospectra.
This paper proposes a kind of projection methods for computing
the pseudospectra of large scale matrices, including orthogonalization
projection method and oblique projection method respectively. This
possibility may be of practical importance in applications involving
large scale highly nonnormal matrices. Numerical algorithms are
given and some numerical experiments illustrate the efficiency of
the new algorithms.
Abstract: In this paper, we consider a new particle filter inspired
by biological evolution. In the standard particle filter, a resampling
scheme is used to decrease the degeneracy phenomenon and improve
estimation performance. Unfortunately, however, it could cause the
undesired the particle deprivation problem, as well. In order to
overcome this problem of the particle filter, we propose a novel
filtering method called the genetic filter. In the proposed filter, we
embed the genetic algorithm into the particle filter and overcome the
problems of the standard particle filter. The validity of the proposed
method is demonstrated by computer simulation.
Abstract: The tagging data of (users, tags and resources) constitutes a folksonomy that is the user-driven and bottom-up approach to organizing and classifying information on the Web. Tagging data stored in the folksonomy include a lot of very useful information and knowledge. However, appropriate approach for analyzing tagging data and discovering hidden knowledge from them still remains one of the main problems on the folksonomy mining researches. In this paper, we have proposed a folksonomy data mining approach based on FCA for discovering hidden knowledge easily from folksonomy. Also we have demonstrated how our proposed approach can be applied in the collaborative tagging system through our experiment. Our proposed approach can be applied to some interesting areas such as social network analysis, semantic web mining and so on.
Abstract: Aiming at most of the aviation products are facing the problem of fatigue fracture in vibration environment, we makes use of the testing result of a bracket, analysis for the structure with ANSYS-Workbench, predict the life of the bracket by different ways, and compared with the testing result. With the research on analysis methods, make an organic combination of simulation analysis and testing, Not only ensure the accuracy of simulation analysis and life predict, but also make a dynamic supervision of product life process, promote the application of finite element simulation analysis in engineering practice.
Abstract: The objective of this paper is to estimate realistic
principal extrusion process parameters by means of artificial neural
network. Conventionally, finite element analysis is used to derive
process parameters. However, the finite element analysis of the
extrusion model does not consider the manufacturing process
constraints in its modeling. Therefore, the process parameters
obtained through such an analysis remains highly theoretical.
Alternatively, process development in industrial extrusion is to a
great extent based on trial and error and often involves full-size
experiments, which are both expensive and time-consuming. The
artificial neural network-based estimation of the extrusion process
parameters prior to plant execution helps to make the actual extrusion
operation more efficient because more realistic parameters may be
obtained. And so, it bridges the gap between simulation and real
manufacturing execution system. In this work, a suitable neural
network is designed which is trained using an appropriate learning
algorithm. The network so trained is used to predict the
manufacturing process parameters.
Abstract: This paper presents an algebraic approach to optimize
queries in domain-specific database management system
for protein structure data. The approach involves the introduction of
several protein structure specific algebraic operators to query the
complex data stored in an object-oriented database system. The
Protein Algebra provides an extensible set of high-level Genomic
Data Types and Protein Data Types along with a comprehensive
collection of appropriate genomic and protein functions. The paper
also presents a query translator that converts high-level query
specifications in algebra into low-level query specifications in
Protein-QL, a query language designed to query protein structure
data. The query transformation process uses a Protein Ontology that
serves the purpose of a dictionary.
Abstract: This paper studies ruin probabilities in two discrete-time
risk models with premiums, claims and rates of interest modelled by
three autoregressive moving average processes. Generalized Lundberg
inequalities for ruin probabilities are derived by using recursive
technique. A numerical example is given to illustrate the applications
of these probability inequalities.
Abstract: As privacy becomes a major concern for consumers
and enterprises, many research have been focused on the privacy
protecting technology in recent years. In this paper, we present a
comprehensive approach for usage access control based on the notion
purpose. In our model, purpose information associated with a given
data element specifies the intended use of the subjects and objects in
the usage access control model. A key feature of our model is that it
allows when an access is required, the access purpose is checked
against the intended purposes for the data item. We propose an
approach to represent purpose information to support access control
based on purpose information. Our proposed solution relies on usage
access control (UAC) models as well as the components which based
on the notions of the purpose information used in subjects and
objects. Finally, comparisons with related works are analyzed.
Abstract: This research attempts to study the feasibility of
augmenting an augmented reality (AR) image card on a Quick
Response (QR) code. The authors have developed a new visual tag,
which contains a QR code and an augmented AR image card. The new
visual tag has features of reading both of the revealed data of the QR
code and the instant data from the AR image card. Furthermore, a
handheld communicating device is used to read and decode the new
visual tag, and then the concealed data of the new visual tag can be
revealed and read through its visual display. In general, the QR code is
designed to store the corresponding data or, as a key, to access the
corresponding data from the server through internet. Those reveled
data from the QR code are represented in text. Normally, the AR
image card is designed to store the corresponding data in
3-Dimensional or animation/video forms. By using QR code's
property of high fault tolerant rate, the new visual tag can access those
two different types of data by using a handheld communicating device.
The new visual tag has an advantage of carrying much more data than
independent QR code or AR image card. The major findings of this
research are: 1) the most efficient area for the designed augmented AR
card augmenting on the QR code is 9% coverage area out of the total
new visual tag-s area, and 2) the best location for the augmented AR
image card augmenting on the QR code is located in the bottom-right
corner of the new visual tag.
Abstract: In this paper, we propose the pre-processor based on
the Evidence Supporting Measure of Similarity (ESMS) filter and also
propose the unified fusion approach (UFA) based on the general
fusion machine coupled with ESMS filter, which improve the
correctness and precision of information fusion in any fields of
application. Here we mainly apply the new approach to Simultaneous
Localization And Mapping (SLAM) of Pioneer II mobile robots. A
simulation experiment was performed, where an autonomous virtual
mobile robot with sonar sensors evolves in a virtual world map with
obstacles. By comparing the result of building map according to the
general fusion machine (here DSmT-based fusing machine and
PCR5-based conflict redistributor considereded) coupling with ESMS
filter and without ESMS filter, it shows the benefit of the selection of
the sources as a prerequisite for improvement of the information
fusion, and also testifies the superiority of the UFA in dealing with
SLAM.
Abstract: In this paper, we discuss convergence of the extrapolated iterative methods for linear systems with the coefficient matrices are singular H-matrices. And we present the sufficient and necessary conditions for convergence of the extrapolated iterative methods. Moreover, we apply the results to the GMAOR methods. Finally, we give one numerical example.
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.