Abstract: Essential oils are expensive phytochemicals produced
and extracted from specific species belonging to particular families in
the plant kingdom. In the United Arab Emirates country (UAE), is
located in the arid region of the world, nine species, from the
Lamiaceae family, having the capability to produce therapeutic grade
essential oils. These species include; Mentha spicata, Ocimum
forskolei, Salvia macrosiphon, Salvia aegyptiaca, Salvia macilenta,
Salvia spinosa, Teucrium polium, Teucrium stocksianum and Zataria
multiflora. Although, such potential species are indigenous to the
UAE, however, there are almost no studies available to investigate
the chemical composition and the quality of the extracted essential
oils under the UAE climatological conditions. Therefore, great
attention has to be given to such valuable natural resources, through
conducting highly supported research projects, tailored to the UAE
conditions, and investigating different extraction techniques,
including the application of the latest available technologies, such as
superficial fluid CO2. This is crucially needed; in order to accomplish
the greatest possibilities in the medicinal field, specifically in the
discovery of new therapeutic chemotypes, as well as, to achieve the
sustainability of this natural resource in the country.
Abstract: Since the marine environmental conditions are
extremely different from the other ones, marine actinomycetes might
produce novel bioactive compounds. Therefore, actinomycete strains
were screened from marine water and sediment samples collected
from the coastal areas of Northern Vietnam. Ninety-nine
actinomycete strains were obtained on starch-casein agar media by
dilution technique, only seven strains, named HP112, HP12, HP411,
HPN11, HP 11, HPT13 and HPX12, showed significant antibacterial
activity against both gram-positive and gram-negative bacteria
(Bacillus subtilis ATCC 6633, Staphylococcus epidemidis ATCC
12228, Escherichia coli ATCC 11105). Further studies were carried
out with the most active HP411 strain against Candida albicans
ATCC 10231. This strain could grow rapidly on starch casein agar
and other media with high salt containing 7-10% NaCl at 28-30oC.
Spore-chain of HP411 showed an elongated and circular shape with
10 to 30 spores/chain. Identification of the strain was carried out by
employing the taxonomical studies including the 16S rRNA
sequence. Based on phylogenetic and phenotypic evidence it is
proposed that HP411 to be belongs to species Streptomyces
variabilis. The potent of the crude extract of fermentation broth of
HP411 that are effective against wide range of pathogens: both grampositive,
gram-negative and fungi. Further studies revealed that the
crude extract HP411 could obtain the anticancer activity for cancer
cell lines: Hep-G2 (liver cancer cell line); RD (cardiac and skeletal
muscle letters cell line); FL (membrane of the uterus cancer cell line).
However, the actinomycetes from marine ecosystem will be useful
for the discovery of new drugs in the future.
Abstract: Self-service technologies (SSTs) make an important
contribution to the daily life of people nowadays. However, the
introduction of SST does not lead to its usage. Thereby, this paper
was an attempt on discovery of the most preferred SST in the
customers’ point of view. To fulfill this aim, the Analytical Hierarchy
Process (AHP) was applied based on Saaty’s questionnaire which
was administered to the customers of e-banking services located in
Golestan providence, northern Iran. This study used qualitative
factors in association with the intention of consumers’ usage of SSTs
to rank three SSTs: ATM, mobile banking and internet banking. The
results showed that mobile banking get the highest weight in
consumers’ point of view. This research can be useful both for
managers and service providers and also for customers who intend to
use e-banking.
Abstract: Stress Corrosion Crack (SCC) in pipeline is a type of
environmentally assisted cracking (EAC), since its discovery in 1965
as a possible cause of failure in pipeline, SCC has caused, on
average, one of two failures per year in the U.S, According to the
NACE SCC DA a pipe line segment is considered susceptible to SCC
if all of the following factors are met: The operating stress exceeds
60% of specified minimum yield strength (SMYS), the operating
temperature exceeds 38°C, the segment is less than 32 km
downstream from a compressor station, the age of the pipeline is
greater than 10 years and the coating type is other than Fusion
Bonded Epoxy(FBE). In this paper as a practical experience in
NISOC, Direct Assessment (DA) Method is used for identification
SCC defect in unpiggable pipeline located downstream of
compressor station.
Abstract: Web-based Cognitive Writing Instruction (WeCWI) is
a hybrid e-framework for the development of a web-based instruction
(WBI), which contributes towards instructional design and language
development. WeCWI divides its contribution in instructional design
into macro and micro perspectives. In macro perspective, being a 21st
century educator by disseminating knowledge and sharing ideas with
the in-class and global learners is initiated. By leveraging the virtue
of technology, WeCWI aims to transform an educator into an
aggregator, curator, publisher, social networker and ultimately, a
web-based instructor. Since the most notable contribution of
integrating technology is being a tool of teaching as well as a
stimulus for learning, WeCWI focuses on the use of contemporary
web tools based on the multiple roles played by the 21st century
educator. The micro perspective in instructional design draws
attention to the pedagogical approaches focusing on three main
aspects: reading, discussion, and writing. With the effective use of
pedagogical approaches through free reading and enterprises,
technology adds new dimensions and expands the boundaries of
learning capacity. Lastly, WeCWI also imparts the fundamental
theories and models for web-based instructors’ awareness such as
interactionist theory, cognitive information processing (CIP) theory,
computer-mediated communication (CMC), e-learning interactionalbased
model, inquiry models, sensory mind model, and leaning styles
model.
Abstract: Over the past era, there have been a lot of efforts and
studies are carried out in growing proficient tools for performing
various tasks in big data. Recently big data have gotten a lot of
publicity for their good reasons. Due to the large and complex
collection of datasets it is difficult to process on traditional data
processing applications. This concern turns to be further mandatory
for producing various tools in big data. Moreover, the main aim of
big data analytics is to utilize the advanced analytic techniques
besides very huge, different datasets which contain diverse sizes from
terabytes to zettabytes and diverse types such as structured or
unstructured and batch or streaming. Big data is useful for data sets
where their size or type is away from the capability of traditional
relational databases for capturing, managing and processing the data
with low-latency. Thus the out coming challenges tend to the
occurrence of powerful big data tools. In this survey, a various
collection of big data tools are illustrated and also compared with the
salient features.
Abstract: Some of the key aspects of vulnerability—discovery, dissemination, and disclosure—have received some attention recently. However, the role of interaction among the vulnerability discoverers and vulnerability acquirers has not yet been adequately addressed. Our study suggests that a major percentage of discoverers, a majority in some cases, are unaffiliated with the software developers and thus are free to disseminate the vulnerabilities they discover in any way they like. As a result, multiple vulnerability markets have emerged. In some of these markets, the exchange is regulated, but in others, there is little or no regulation. In recent vulnerability discovery literature, the vulnerability discoverers have remained anonymous individuals. Although there has been an attempt to model the level of their efforts, information regarding their identities, modes of operation, and what they are doing with the discovered vulnerabilities has not been explored.
Reports of buying and selling of the vulnerabilities are now appearing in the press; however, the existence of such markets requires validation, and the natures of the markets need to be analyzed. To address this need, we have attempted to collect detailed information. We have identified the most prolific vulnerability discoverers throughout the past decade and examined their motivation and methods. A large percentage of these discoverers are located in Eastern and Western Europe and in the Far East. We have contacted several of them in order to collect firsthand information regarding their techniques, motivations, and involvement in the vulnerability markets. We examine why many of the discoverers appear to retire after a highly successful vulnerability-finding career. The paper identifies the actual vulnerability markets, rather than the hypothetical ideal markets that are often examined. The emergence of worldwide government agencies as vulnerability buyers has significant implications. We discuss potential factors that can impact the risk to society and the need for detailed exploration.
Abstract: A Mobile Ad-hoc Network (MANET) is a self managing network consists of versatile nodes that are capable of communicating with each other without having any fixed infrastructure. These nodes may be routers and/or hosts. Due to this dynamic nature of the network, routing protocols are vulnerable to various kinds of attacks. The black hole attack is one of the conspicuous security threats in MANETs. As the route discovery process is obligatory and customary, attackers make use of this loophole to get success in their motives to destruct the network. In Black hole attack the packet is redirected to a node that actually does not exist in the network. Many researchers have proposed different techniques to detect and prevent this type of attack. In this paper, we have analyzed various routing protocols in this context. Further we have shown a critical comparison among various protocols. We have shown various routing metrics are required proper and significant analysis of the protocol.
Abstract: MicroRNAs (miRNAs), a class of approximately 22 nucleotide long non coding RNAs which play critical role in different biological processes. The mature microRNA is usually 19–27 nucleotides long and is derived from a bigger precursor that folds into a flawed stem-loop structure. Mature micro RNAs are involved in many cellular processes that encompass development, proliferation, stress response, apoptosis, and fat metabolism by gene regulation. Resent finding reveals that certain viruses encode their own miRNA that processed by cellular RNAi machinery. In recent research indicate that cellular microRNA can target the genetic material of invading viruses. Cellular microRNA can be used in the virus life cycle; either to up regulate or down regulate viral gene expression Computational tools use in miRNA target prediction has been changing drastically in recent years. Many of the methods have been made available on the web and can be used by experimental researcher and scientist without expert knowledge of bioinformatics. With the development and ease of use of genomic technologies and computational tools in the field of microRNA biology has superior tremendously over the previous decade. This review attempts to give an overview over the genome wide approaches that have allow for the discovery of new miRNAs and development of new miRNA target prediction tools and databases.
Abstract: Privacy and Security have emerged as an important research issue in Mobile Ad Hoc Networks (MANET) due to its unique nature such as scarce of resources and absence of centralized authority. There are number of protocols have been proposed to provide privacy and security for data communication in an adverse environment, but those protocols are compromised in many ways by the attackers. The concept of anonymity (in terms of unlinkability and unobservability) and pseudonymity has been introduced in this paper to ensure privacy and security. In this paper, a Secure Onion Throat (SOT) protocol is proposed to provide complete anonymity in an adverse environment. The SOT protocol is designed based on the combination of group signature and onion routing with ID-based encryption for route discovery. The security analysis demonstrates the performance of SOT protocol against all categories of attacks. The simulation results ensure the necessity and importance of the proposed SOT protocol in achieving such anonymity.
Abstract: Feature selection is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that all genes are not important in gene expression data. Some of the genes may be redundant, and others may be irrelevant and noisy. Here a novel approach is proposed Hybrid K-Mean-Quick Reduct (KMQR) algorithm for gene selection from gene expression data. In this study, the entire dataset is divided into clusters by applying K-Means algorithm. Each cluster contains similar genes. The high class discriminated genes has been selected based on their degree of dependence by applying Quick Reduct algorithm to all the clusters. Average Correlation Value (ACV) is calculated for the high class discriminated genes. The clusters which have the ACV value as 1 is determined as significant clusters, whose classification accuracy will be equal or high when comparing to the accuracy of the entire dataset. The proposed algorithm is evaluated using WEKA classifiers and compared. The proposed work shows that the high classification accuracy.
Abstract: In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion detection system (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw dataset for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle component analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. This optimal feature subset is used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.
Abstract: This paper is about middleware which enables group-user applications on mobile devices in physical proximity to interact with other devices without intervention of a central server. Requirements of the middleware are identified from service usage scenarios, and the functional architecture of the middleware is specified. These requirements include Group Management, Synchronization, and Resource Management. Group Management needs to provide various capabilities to such applications with respect to managing multiple users (e.g., creation of groups, discovery of group or individual users, member join/leave, election of a group manager and service-group association) using D2D communication technology. We designed the middleware for the above requirements on the Android platform.
Abstract: The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system thus the proposed solution has been verified. The paper documents how is possible to apply the new discovery knowledge to use in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control.
Abstract: The Tropical Data Hub (TDH) is a virtual research environment that provides researchers with an e-research infrastructure to congregate significant tropical data sets for data reuse, integration, searching, and correlation. However, researchers often require data and metadata synthesis across disciplines for cross-domain analyses and knowledge discovery. A triplestore offers a semantic layer to achieve a more intelligent method of search to support the synthesis requirements by automating latent linkages in the data and metadata. Presently, the benchmarks to aid the decision of which triplestore is best suited for use in an application environment like the TDH are limited to performance. This paper describes a new evaluation tool developed to analyze both features and performance. The tool comprises a weighted decision matrix to evaluate the interoperability, functionality, performance, and support availability of a range of integrated and native triplestores to rank them according to requirements of the TDH.
Abstract: Pattern discovery from time series is of fundamental importance. Particularly, when information about the structure of a pattern is not complete, an algorithm to discover specific patterns or shapes automatically from the time series data is necessary. The dynamic time warping is a technique that allows local flexibility in aligning time series. Because of this, it is widely used in many fields such as science, medicine, industry, finance and others. However, a major problem of the dynamic time warping is that it is not able to work with structural changes of a pattern. This problem arises when the structure is influenced by noise, which is a common thing in practice for almost every application. This paper addresses this problem by means of developing a novel technique called adaptive dynamic time warping.
Abstract: This paper aims to quantify the impact of natural disaster on tourism by the change of annual visitors to scenic spots. The data of visitors to Alishan, Sun Moon Lake, Sitou and Palace Museum in Taiwan during 1986 to 2012 year is collected, and the trend analysis is used to predict the annual visitors to these scenic spots. The findings show that 1999 Taiwan earthquake had significant effect on the visitors to Alishan, Sun Moon Lake and Sitou with an average impact of 55.75% during 1999 to 2000 year except for Palace Museum. The impact was greater as closer epicenter of 1999 earthquake. And the discovery period of visitors is about 2 to 9 years. Further, the impact of heavy rainfall on Alishan, Taiwan is estimated. As the accumulative rainfall reaches to 500 mm, the impact on visitors can be predicted.
Abstract: Improvements in the data fusion and data analysis phase of research are imperative due to the exponential growth of sensed data. Currently, there are developments in the Semantic Sensor Web community to explore efficient methods for reuse, correlation and integration of web-based data sets and live data streams. This paper describes the integration of remotely sensed data with web-available static data for use in observational hypothesis testing and the analysis phase of research. The Semantic Reef system combines semantic technologies (e.g., well-defined ontologies and logic systems) with scientific workflows to enable hypothesis-based research. A framework is presented for how the data fusion concepts from the Semantic Reef architecture map to the Smart Environment Monitoring and Analysis Technologies (SEMAT) intelligent sensor network initiative. The data collected via SEMAT and the inferred knowledge from the Semantic Reef system are ingested to the Tropical Data Hub for data discovery, reuse, curation and publication.
Abstract: Safe drinking water is one of the biggest issues facing
the planet this century. The primary aim of this paper is to present our
research focused on theoretical and experimental analysis of potable
water and in-building water distribution systems from the point of
view of microbiological risk on the basis of confrontation between
the theoretical analysis and synthesis of gathered information in
conditions of the Slovak Republic. The presence of the bacteria
Legionella in water systems, especially in hot water distribution
system, represents in terms of health protection of inhabitants the
crucial problem which cannot be overlooked. Legionella
pneumophila discovery, its classification and its influence on
installations inside buildings are relatively new. There are a lot of
guidelines and regulations developed in many individual countries for
the design, operation and maintenance for tap water systems to avoid
the growth of bacteria Legionella pneumophila, but in Slovakia we
don-t have any. The goal of this paper is to show the necessity of
prevention and regulations for installations inside buildings verified
by simulation methods.
Abstract: The various types of frequent pattern discovery
problem, namely, the frequent itemset, sequence and graph mining
problems are solved in different ways which are, however, in certain
aspects similar. The main approach of discovering such patterns can
be classified into two main classes, namely, in the class of the levelwise
methods and in that of the database projection-based methods.
The level-wise algorithms use in general clever indexing structures
for discovering the patterns. In this paper a new approach is proposed
for discovering frequent sequences and tree-like patterns efficiently
that is based on the level-wise issue. Because the level-wise
algorithms spend a lot of time for the subpattern testing problem, the
new approach introduces the idea of using automaton theory to solve
this problem.