Abstract: This paper identifies a research gap in the literature on tourism entrepreneurship in Malawi, Africa, and investigates how entrepreneurs from the Malawian tourism sector discover and exploit business opportunities. In particular, the importance of prior experience and business networks in the opportunity development process is debated. Another area of empirical research examined here is the opportunity recognition-venture creation sequence. While Malawi presents fruitful business opportunities, exploiting these opportunities into fully realized business ideas is a real challenge due to the country’s difficult business environment and poor promotional and marketing efforts. The study concludes by calling for further research in Sub-Saharan Africa in order to develop our understanding of entrepreneurship in this (African) context.
Abstract: In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.
Abstract: In the recent years, a considerable level of interest has been developed on the use of earth in construction, led by its rediscovery as an environmentally building material. The Stabilized Earth Concrete (SEC) is a good alternative to the cement concrete, thanks to its thermal and moisture regulating features. Many parameters affect the behavior of stabilized earth concrete. This article presents research results related to the influence of the compacting nature on some SEC properties namely: The mechanical behavior, capillary absorption, shrinkage and sustainability to water erosion, and this, basing on two types of compacting: Manual and semi-automatic.
Abstract: This paper discusses the Chinese Language Teaching as a Second Language by focusing on Immersion Teaching. Researchers used narrative literature review to describe the current states of both art and science in focused areas of inquiry. Immersion teaching comes with a standard that teachers must reliably meet. Chinese language-immersion instruction consists of language and content lessons, including functional usage of the language, academic language, authentic language, and correct Chinese sociocultural language. Researchers used narrative literature reviews to build a scientific knowledge base. Researchers collected all the important points of discussion, and put them here with reference to the specific field where this paper is originally based on. The findings show that Chinese Language in immersion teaching is not like standard foreign language classroom; immersion setting provides more opportunities to teach students colloquial language than academic. Immersion techniques also introduce a language’s cultural and social contexts in a meaningful and memorable way. It is particularly important that immersion teachers connect classwork with real-life experiences. Immersion also includes more elements of discovery and inquiry based learning than do other kinds of instructional practices. Students are always and consistently interpreted the conclusions and context clues.
Abstract: In this paper, we analyze NEtwork MObility (NEMO) supporting problems in Content-Centric Networking (CCN), and propose the CCN-NEMO which can well support the deployment of the content-centric paradigm in large-scale mobile Internet. The CCN-NEMO extends the signaling message of the basic CCN protocol, to support the mobility discovery and fast trigger of Interest re-issuing during the network mobility. Besides, the Mobile Router (MR) is extended to optimize the content searching and relaying in the local subnet. These features can be employed by the nested NEMO to maximize the advantages of content retrieving with CCN. Based on the analysis, we compare the performance on handover latency between the basic CCN and our proposed CCN-NEMO. The results show that our scheme can facilitate the content-retrieving in the NEMO scenario with improved performance.
Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: Despite the wide spread use of synthetic dyes, natural
dyes are still exploited and used to enhance its inherent aesthetic
qualities as a major material for beautification of the body. Centuries
before the discovery of synthetic dyes, natural dyes were the only
source of dye open to mankind. Dyes are extracted from plant -
leaves, roots and barks, insect secretions, and minerals. However,
research findings have made it clear that of all, plants- leaves, roots,
barks or flowers are the most explored and exploited in which henna
(Lawsonia innermis L.) is one of those plants. Experiment has also
shown that henna is used in body painting in conjunction with an
alkaline (Ammonium Sulphate) as a fixing agent. This of course
gives a clue that if colour derived from henna is properly
investigated, it may not only be used for body decoration but
possibly, may have affinity to fiber substrate. This paper investigates
the dyeing potentials – dye ability and fastness qualities of henna dye
extracts on cotton and linen fibers using mordants like ammonium
sulphate and other alkalis (hydrosulphate and caustic soda, potash,
common salt, potassium alum). Hot and cold water and ethanol
solvent were used in the extraction of the dye to investigate the most
effective method, dye ability, and fastness qualities of these extracts
under room temperature. The results of the experiment show that
cotton have a high rate of dye intake than other fiber. On a similar
note, the colours obtained depend most on the solvent used. In
conclusion, hot water extraction appears more effective. While the
colours obtained from ethanol and both cold hot methods of
extraction range from light to dark yellow, light green to army green
and to some extent shades of brown hues.
Abstract: The aim of this paper is to propose a general
framework for storing, analyzing, and extracting knowledge from
two-dimensional echocardiographic images, color Doppler images,
non-medical images, and general data sets. A number of high
performance data mining algorithms have been used to carry out this
task. Our framework encompasses four layers namely physical
storage, object identification, knowledge discovery, user level.
Techniques such as active contour model to identify the cardiac
chambers, pixel classification to segment the color Doppler echo
image, universal model for image retrieval, Bayesian method for
classification, parallel algorithms for image segmentation, etc., were
employed. Using the feature vector database that have been
efficiently constructed, one can perform various data mining tasks
like clustering, classification, etc. with efficient algorithms along
with image mining given a query image. All these facilities are
included in the framework that is supported by state-of-the-art user
interface (UI). The algorithms were tested with actual patient data
and Coral image database and the results show that their performance
is better than the results reported already.
Abstract: Routing in adhoc networks is a challenge as nodes are
mobile, and links are constantly created and broken. Present ondemand
adhoc routing algorithms initiate route discovery after a path
breaks, incurring significant cost to detect disconnection and
establish a new route. Specifically, when a path is about to be broken,
the source is warned of the likelihood of a disconnection. The source
then initiates path discovery early, avoiding disconnection totally. A
path is considered about to break when link availability decreases.
This study modifies Adhoc On-demand Multipath Distance Vector
routing (AOMDV) so that route handoff occurs through link
availability estimation.
Abstract: This paper introduces the concept and principle of data
cleaning, analyzes the types and causes of dirty data, and proposes
several key steps of typical cleaning process, puts forward a well
scalability and versatility data cleaning framework, in view of data
with attribute dependency relation, designs several of violation data
discovery algorithms by formal formula, which can obtain inconsistent
data to all target columns with condition attribute dependent no matter
data is structured (SQL) or unstructured (NoSql), and gives 6 data
cleaning methods based on these algorithms.
Abstract: Synthesis of gold nanoparticles has attracted much
attention since the pioneering discovery of the high catalytic activity
of supported gold nanoparticles in the reaction of CO oxidation at
low temperature. In this research field, we used Na-montmorillonite
for gold nanoparticles stabilization; various gold loading percentage
1, 2 and 5% were used for gold nanoparticles preparation. The gold
nanoparticles were obtained using chemical reduction method using
NaBH4 as reductant agent. The obtained gold nanoparticles stabilized
in Na-montmorillonite were used as catalysts for the reduction of 4-
nitrophenol to aminophenol with sodium borohydride at room
temperature. The UV-Vis results confirmed directly the gold
nanoparticles formation. The XRD and N2 adsorption results showed
the formation of gold nanoparticles in the pores of montmorillonite
with an average size of 5 nm obtained on samples with 2% gold
loading percentage. The gold particles size increased with the
increase of gold loading percentage. The reduction reaction of 4-
nitrophenol into 4-aminophenol with NaBH4 catalyzed by Au-Namontmorillonite
catalyst exhibits remarkably a high activity; the
reaction was completed within 9 min for 1%Au-Na-montmorillonite
and within 3 min for 2%Au-Na-montmorillonite.
Abstract: Association rule mining is one of the most important fields of data mining and knowledge discovery. In this paper, we propose an efficient multiple support frequent pattern growth algorithm which we called “MSFP-growth” that enhancing the FPgrowth algorithm by making infrequent child node pruning step with multiple minimum support using maximum constrains. The algorithm is implemented, and it is compared with other common algorithms: Apriori-multiple minimum supports using maximum constraints and FP-growth. The experimental results show that the rule mining from the proposed algorithm are interesting and our algorithm achieved better performance than other algorithms without scarifying the accuracy.
Abstract: In our research we aimed to test a managerial
approach for the fuzzy front end (FFE) of innovation by creating
controlled experiment/ business case in a breakthrough innovation
development. The experiment was in the sport industry and covered
all aspects of the customer discovery stage from ideation to
prototyping followed by patent application. In the paper we describe
and analyze mile stones, tasks, management challenges, decisions
made to create the break through innovation, evaluate overall
managerial efficiency that was at the considered FFE stage.
We set managerial outcome of the FFE stage as a valid product
concept in hand. In our paper we introduce hypothetical construct
“Q-factor” that helps us in the experiment to distinguish quality of
FFE outcomes.
The experiment simulated for entrepreneur the FFE of innovation
and put on his shoulders responsibility for the outcome of valid
product concept. While developing managerial approach to reach the
outcome there was a decision to look on product concept from the
cognitive psychology and cognitive science point of view. This view
helped us to develop the profile of a person whose projection (mental
representation) of a new product could optimize for a manager or
entrepreneur FFE activities. In the experiment this profile was tested
to develop breakthrough innovation for swimmers. Following the
managerial approach the product concept was created to help
swimmers to feel/sense water. The working prototype was developed
to estimate the product concept validity and value added effect for
customers.
Based on feedback from coachers and swimmers there were strong
positive effect that gave high value for customers, and for the
experiment – the valid product concept being developed by proposed
managerial approach for the FFE.
In conclusions there is a suggestion of managerial approach that
was derived from experiment.
Abstract: This paper argues nation-building theories that
prioritize democratic governance best explain the successful postindependence
development of Botswana. Three main competing
schools of thought exist regarding the sequencing of policies that
should occur to re-build weakened or failed states. The first posits
that economic development should receive foremost attention, while
democratization and a binding sense of nationalism can wait. A
second group of experts identified constructing a sense of nationalism
among a populace is necessary first, so that the state receives popular
legitimacy and obedience that are prerequisites for development.
Botswana, though, transitioned into a multi-party democracy and
prosperous open economy due to the utilization of traditional
democratic structures, enlightened and accountable leadership, and an
educated technocratic civil service. With these political foundations
already in place when the discovery of diamonds occurred, the
resulting revenues were spent wisely on projects that grew the
economy, improved basic living standards, and attracted foreign
investment. Thus democratization preceded, and therefore provided
an accountable basis for, economic development that might otherwise
have been squandered by greedy and isolated elites to the detriment
of the greater population. Botswana was one of the poorest nations in
the world at the time of its independence in 1966, with little
infrastructure, a dependence on apartheid South Africa for trade, and
a largely subsistence economy. Over the next thirty years, though, its
economy grew the fastest of any nation in the world. The transparent
and judicious use of diamond returns is only a partial explanation, as
the government also pursued economic diversification, mass
education, and rural development in response to public needs.
As nation-building has become a project undertaken by nations
and multilateral agencies such as the United Nations and the North
Atlantic Treaty Organization, Botswana may provide best practices
that others should follow in attempting to reconstruct economically
and politically unstable states.
Abstract: The wide use of the Internet-based applications bring many challenges to the researchers to guarantee the continuity of the connections needed by the mobile hosts and provide reliable Internet access for them. One of proposed solutions by Internet Engineering Task Force (IETF) is to connect the local, multi-hop, and infrastructure-less Mobile Ad hoc Network (MANET) with Internet structure. This connection is done through multi-interface devices known as Internet Gateways. Many issues are related to this connection like gateway discovery, handoff, address auto-configuration and selecting the optimum gateway when multiple gateways exist. Many studies were done proposing gateway selection schemes with a single selection criterion or weighted multiple criteria. In this research, a review of some of these schemes is done showing the differences, the features, the challenges and the drawbacks of each of them.
Abstract: Web-based Cognitive Writing Instruction (WeCWI)’s
contribution towards language development can be divided into
linguistic and non-linguistic perspectives. In linguistic perspective,
WeCWI focuses on the literacy and language discoveries, while the
cognitive and psychological discoveries are the hubs in non-linguistic
perspective. In linguistic perspective, WeCWI draws attention to free
reading and enterprises, which are supported by the language
acquisition theories. Besides, the adoption of process genre approach
as a hybrid guided writing approach fosters literacy development.
Literacy and language developments are interconnected in the
communication process; hence, WeCWI encourages meaningful
discussion based on the interactionist theory that involves input,
negotiation, output, and interactional feedback. Rooted in the elearning
interaction-based model, WeCWI promotes online
discussion via synchronous and asynchronous communications,
which allows interactions happened among the learners, instructor,
and digital content. In non-linguistic perspective, WeCWI highlights
on the contribution of reading, discussion, and writing towards
cognitive development. Based on the inquiry models, learners’
critical thinking is fostered during information exploration process
through interaction and questioning. Lastly, to lower writing anxiety,
WeCWI develops the instructional tool with supportive features to
facilitate the writing process. To bring a positive user experience to
the learner, WeCWI aims to create the instructional tool with
different interface designs based on two different types of perceptual
learning style.
Abstract: Securing the confidential data transferred via wireless
network remains a challenging problem. It is paramount to ensure
that data are accessible only by the legitimate users rather than by the
attackers. One of the most serious threats to organization is jamming,
which disrupts the communication between any two pairs of nodes.
Therefore, designing an attack-defending scheme without any packet
loss in data transmission is an important challenge. In this paper,
Dependence based Malicious Route Defending DMRD Scheme has
been proposed in multi path routing environment to prevent jamming
attack. The key idea is to defend the malicious route to ensure
perspicuous transmission. This scheme develops a two layered
architecture and it operates in two different steps. In the first step,
possible routes are captured and their agent dependence values are
marked using triple agents. In the second step, the dependence values
are compared by performing comparator filtering to detect malicious
route as well as to identify a reliable route for secured data
transmission. By simulation studies, it is observed that the proposed
scheme significantly identifies malicious route by attaining lower
delay time and route discovery time; it also achieves higher
throughput.
Abstract: Safety is one of the most important considerations
when buying a new car. While active safety aims at avoiding
accidents, passive safety systems such as airbags and seat belts
protect the occupant in case of an accident. In addition to legal
regulations, organizations like Euro NCAP provide consumers with
an independent assessment of the safety performance of cars and
drive the development of safety systems in automobile industry.
Those ratings are mainly based on injury assessment reference values
derived from physical parameters measured in dummies during a car
crash test.
The components and sub-systems of a safety system are designed
to achieve the required restraint performance. Sled tests and other
types of tests are then carried out by car makers and their suppliers
to confirm the protection level of the safety system. A Knowledge
Discovery in Databases (KDD) process is proposed in order to
minimize the number of tests. The KDD process is based on the
data emerging from sled tests according to Euro NCAP specifications.
About 30 parameters of the passive safety systems from different data
sources (crash data, dummy protocol) are first analysed together with
experts opinions. A procedure is proposed to manage missing data
and validated on real data sets. Finally, a procedure is developed to
estimate a set of rough initial parameters of the passive system before
testing aiming at reducing the number of tests.
Abstract: This paper investigates the joint effect of the
interconnected (n,k)-star network topology and Multi-Agent
automated control on restoration and reconfiguration of power
systems. With the increasing trend in development in Multi-Agent
control technologies applied to power system reconfiguration
in presence of faulty components or nodes. Fault tolerance is
becoming an important challenge in the design processes of the
distributed power system topology. Since the reconfiguration of a
power system is performed by agent communication, the (n,k)-star
interconnected network topology is studied and modeled in this
paper to optimize the process of power reconfiguration. In this paper,
we discuss the recently proposed (n,k)-star topology and examine its
properties and advantages as compared to the traditional multi-bus
power topologies. We design and simulate the topology model for
distributed power system test cases. A related lemma based on the
fault tolerance and conditional diagnosability properties is presented
and proved both theoretically and practically. The conclusion is
reached that (n,k)-star topology model has measurable advantages
compared to standard bus power systems while exhibiting fault
tolerance properties in power restoration, as well as showing
efficiency when applied to power system route discovery.
Abstract: The dramatic rise in the use of Social Media (SM)
platforms such as Facebook and Twitter provide access to an
unprecedented amount of user data. Users may post reviews on
products and services they bought, write about their interests, share
ideas or give their opinions and views on political issues. There is a
growing interest in the analysis of SM data from organisations for
detecting new trends, obtaining user opinions on their products and
services or finding out about their online reputations. A recent
research trend in SM analysis is making predictions based on
sentiment analysis of SM. Often indicators of historic SM data are
represented as time series and correlated with a variety of real world
phenomena like the outcome of elections, the development of
financial indicators, box office revenue and disease outbreaks. This
paper examines the current state of research in the area of SM mining
and predictive analysis and gives an overview of the analysis
methods using opinion mining and machine learning techniques.