Abstract: China is experiencing the rural development process, with the agritourism complex becoming one of the significant modes. Therefore, it is imperative to understand the energy performance of agritourism complex. This study focuses on a typical case of the agritourism complex and simulates the energy consumption performance on condition of the regular energy system. It was found that HVAC took 90% of the whole energy demand range. In order to optimize the energy supply structure, the hierarchical analysis was carried out on the level of architecture with three main factors such as construction situation, building types and energy demand types. Finally, the energy planning suggestion of the agritourism complex was put forward and the relevant results were obtained.
Abstract: Objective: Biliary atresia (BA) is characterized by progressive liver fibrosis. Epithelial-mesenchymal transition (EMT) has been implicated as a key mechanism in the pathogenesis of organ fibrosis. MiR-200a has been shown to repress EMT. We aim to explore the role of miR-200a in the fibrogenesis of BA. Methods: We obtained the plasma samples and liver samples from patients with BA or controls to examine the role of miR-200a. Histological liver fibrosis was assessed using the Ishak fibrosis scores. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed to detect the expression of miR-200a in plasma. We also evaluated the expression of miR-200a in liver tissues using tyramide signal amplification fluorescence in situ hybridization (TSA-FISH). The expression of EMT related proteins zinc finger E-box-binding homeobox 1 (ZEB1), E-cadherin and α-smooth muscle actin (α-SMA) in the liver sections were detected by immunohistochemical staining. Results: We found that the expression of miR-200a was both elevated in the plasma and liver tissues from BA patients compared with the controls. The hepatic expression of ZEB1 and α-SMA were markedly increased in the liver sections from BA patients compared to the controls, whereas E-cadherin was downregulated in the BA group. Simultaneously, we noted that the hepatic expression of miR-200a, E-cadherin and α-SMA were upregulated with the progression of liver fibrosis in the BA group, while ZEB1 was downregulated with the progression of liver fibrosis in BA patients. Conclusion: These findings suggest EMT has a critical effect on the fibrotic process of BA, and the interaction between miR-200a and ZEB1 may regulate EMT and eventually influence liver fibrogenesis of BA.
Abstract: A modified two dimensional (2D) logistic map based on cross feedback control is proposed. This 2D map exhibits more random chaotic dynamical properties than the classic one dimensional (1D) logistic map in the statistical characteristics analysis. So it is utilized as the pseudo-random (PN) sequence generator, where the obtained real-valued PN sequence is quantized at first, then applied to radio frequency identification (RFID) communication system in this paper. This system is experimentally validated on a cortex-M0 development board, which shows the effectiveness in key generation, the size of key space and security. At last, further cryptanalysis is studied through the test suite in the National Institute of Standards and Technology (NIST).
Abstract: In data mining, the association rules are used to search
for the relations of items of the transactions database. Following the
data is collected and stored, it can find rules of value through
association rules, and assist manager to proceed marketing strategy
and plan market framework. In this paper, we attempt fuzzy partition
methods and decide membership function of quantitative values of
each transaction item. Also, by managers we can reflect the
importance of items as linguistic terms, which are transformed as
fuzzy sets of weights. Next, fuzzy weighted frequent pattern growth
(FWFP-Growth) is used to complete the process of data mining. The
method above is expected to improve Apriori algorithm for its better
efficiency of the whole association rules. An example is given to
clearly illustrate the proposed approach.
Abstract: In data mining, the association rules are used to find
for the associations between the different items of the transactions
database. As the data collected and stored, rules of value can be found
through association rules, which can be applied to help managers
execute marketing strategies and establish sound market frameworks.
This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth)
to derive from fuzzy association rules. At first, we apply fuzzy
partition methods and decide a membership function of quantitative
value for each transaction item. Next, we implement FFP-growth
to deal with the process of data mining. In addition, in order to
understand the impact of Apriori algorithm and FFP-growth algorithm
on the execution time and the number of generated association
rules, the experiment will be performed by using different sizes of
databases and thresholds. Lastly, the experiment results show FFPgrowth
algorithm is more efficient than other existing methods.
Abstract: This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid model intrusion detection system uses DARPA data set to do test evaluation. It uses ROC curve to display the test result intuitively. After the experiment it proves this hybrid model intrusion detection system can effectively improve the detection rate, and reduce the rate of false alarm and fail.
Abstract: This paper applies fuzzy set theory to evaluate the
service quality of online auction. Service quality is a composition of
various criteria. Among them many intangible attributes are difficult
to measure. This characteristic introduces the obstacles for respondent
in replying to the survey. So as to overcome this problem, we
invite fuzzy set theory into the measurement of performance. By
using AHP in obtaining criteria and TOPSIS in ranking, we found
the most concerned dimension of service quality is Transaction
Safety Mechanism and the least is Charge Item. Regarding to the
most concerned attributes are information security, accuracy and
information.
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 paper applies fuzzy AHP to evaluate the service
quality of online auction. Service quality is a composition of various
criteria. Among them many intangible attributes are difficult to
measure. This characteristic introduces the obstacles for respondents
on reply in the survey. So as to overcome this problem, we invite
fuzzy set theory into the measurement of performance and use AHP in
obtaining criteria. We found the most concerned dimension of service
quality is Transaction Safety Mechanism and the least is Charge Item.
Other criteria such as information security, accuracy and information
are too vital.
Abstract: To extract the important physiological factors related to
diabetes from an oral glucose tolerance test (OGTT) by mathematical
modeling, highly informative but convenient protocols are required.
Current models require a large number of samples and extended
period of testing, which is not practical for daily use. The purpose
of this study is to make model assessments possible even from a
reduced number of samples taken over a relatively short period.
For this purpose, test values were extrapolated using a support
vector machine. A good correlation was found between reference and
extrapolated values in evaluated 741 OGTTs. This result indicates
that a reduction in the number of clinical test is possible through a
computational approach.
Abstract: In the era of great competition, understanding and satisfying
customers- requirements are the critical tasks for a company
to make a profits. Customer relationship management (CRM) thus
becomes an important business issue at present. With the help of
the data mining techniques, the manager can explore and analyze
from a large quantity of data to discover meaningful patterns and
rules. Among all methods, well-known association rule is most
commonly seen. This paper is based on Apriori algorithm and uses
genetic algorithms combining a data mining method to discover fuzzy
classification rules. The mined results can be applied in CRM to
help decision marker make correct business decisions for marketing
strategies.