Abstract: There are real needs to integrate types of Open
Educational Resources (OER) with an intelligent system to extract
information and knowledge in the semantic searching level. The
needs came because most of current learning standard adopted web
based learning and the e-learning systems do not always serve all
educational goals. Semantic Web systems provide educators,
students, and researchers with intelligent queries based on a semantic
knowledge management learning system. An ontology-based learning
system is an advanced system, where ontology plays the core of the
semantic web in a smart learning environment. The objective of this
paper is to discuss the potentials of ontologies and mapping different
kinds of ontologies; heterogeneous or homogenous to manage and
control different types of Open Educational Resources. The important
contribution of this research is that it uses logical rules and
conceptual relations to map between ontologies of different
educational resources. We expect from this methodology to establish
an intelligent educational system supporting student tutoring, self and
lifelong learning system.
Abstract: During welding or flame cutting of metals, the
prediction of heat affected zone (HAZ) is critical. There is need to
develop a simple mathematical model to calculate the temperature
variation in HAZ and derivative analysis can be used for this purpose.
This study presents analytical solution for heat transfer through
conduction in mild steel plate. The homogeneous and nonhomogeneous
boundary conditions are single variables. The full field
analytical solutions of temperature measurement, subjected to local
heating source, are derived first by method of separation of variables
followed with the experimental visualization using infrared imaging.
Based on the present work, it is suggested that appropriate heat input
characteristics controls the temperature distribution in and around
HAZ.
Abstract: The current study aims to highlight the loading
characteristics impact on the time evolution (focusing particularly on
long term effects) of the deformation of realized reinforced concrete
beams. Namely the tension stiffening code provisions (i.e. within
Eurocode 2) are reviewed with a clear intention to reassess their
operational value and predicting capacity. In what follows the
experimental programme adopted along with some preliminary
findings and numerical modeling attempts are presented. For a range of long slender reinforced concrete simply supported
beams (4200 mm) constant static sustained and repeated cyclic
loadings were applied mapping the time evolution of deformation.
All experiments were carried out at the Heavy Structures Lab of the
University of Leeds. During tests the mid-span deflection, creep
coefficient and shrinkage strains were monitored for duration of 90
days. The obtained results are set against the values predicted by
Eurocode 2 and the tools within an FE commercial package (i.e.
Midas FEA) to yield that existing knowledge and practise is at times
over-conservative.
Abstract: In many countries, governments have been promoting the involvement of private sector entities to enter into long-term agreements for the development and delivery of large infrastructure projects, with a focus on overcoming the limitations upon public fund of the traditional approach. The involvement of private sector through public private partnerships (PPP) brings in new capital investments, value for money and additional risks to handle. Worldwide research studies have shown that an objective, systematic, reliable and useroriented risk assessment process and an optimal allocation mechanism among different stakeholders is crucial to the successful completion. In this framework, this paper, which is the first stage of a research study, aims to identify the main risks for the delivery of PPP projects. A review of cross-countries research projects and case studies was performed to map the key risks affecting PPP infrastructure delivery. The matrix of mapping offers a summary of the frequency of factors, clustered in eleven categories: construction, design, economic, legal, market, natural, operation, political, project finance, project selection and relationship. Results will highlight the most critical risk factors, and will hopefully assist the project managers in directing the managerial attention in the further stages of risk allocation.
Abstract: This study integrates a larger research empirical
project that examines second language (SL) learners’ profiles and
valid procedures to perform complete and diagnostic assessment in
schools. 102 learners of Portuguese as a SL aged 7 and 17 years
speakers of distinct home languages were assessed in several
linguistic tasks. In this article, we focused on writing performance in
the specific task of narrative essay composition. The written outputs
were measured using the score in six components adapted from an
English SL assessment context (Alberta Education): linguistic
vocabulary, grammar, syntax, strategy, socio-linguistic, and
discourse. The writing processes and strategies in Portuguese
language used by different immigrant students were analysed to
determine features and diversity of deficits on authentic texts
performed by SL writers. Differentiated performance was based on
the diversity of the following variables: grades, previous schooling,
home language, instruction in first language, and exposure to
Portuguese as Second Language. Indo-Aryan languages speakers
showed low writing scores compared to their peers and the type of
language and respective cognitive mapping (such as Mandarin and
Arabic) was the predictor, not linguistic distance. Home language
instruction should also be prominently considered in further research
to understand specificities of cognitive academic profile in a
Romance languages learning context. Additionally, this study also
examined the teachers’ representations that will be here addressed to
understand educational implications of second language teaching in
psychological distress of different minorities in schools of specific
host countries.
Abstract: This research presents the main ideas to implement an
intelligent system composed by communicating wireless sensors
measuring environmental data linked to drought indicators (such as
air temperature, soil moisture , etc...). On the other hand, the setting
up of a spatio temporal database communicating with a Web mapping
application for a monitoring in real time in activity 24:00 /day, 7
days/week is proposed to allow the screening of the drought
parameters time evolution and their extraction. Thus this system
helps detecting surfaces touched by the phenomenon of drought.
Spatio-temporal conceptual models seek to answer the users who
need to manage soil water content for irrigating or fertilizing or other
activities pursuing crop yield augmentation. Effectively, spatiotemporal
conceptual models enable users to obtain a diagram of
readable and easy data to apprehend. Based on socio-economic
information, it helps identifying people impacted by the phenomena
with the corresponding severity especially that this information is
accessible by farmers and stakeholders themselves. The study will be
applied in Siliana watershed Northern Tunisia.
Abstract: ANDASA is a knowledge management platform for
the capitalization of knowledge and cultural assets for the artistic and
cultural sectors. It was built based on the priorities expressed by the
participating artists. Through mapping artistic activities and
specificities, it enables to highlight various aspects of the artistic
research and production. Such instrument will contribute to create
networks and partnerships, as it enables to evidentiate who does
what, in what field, using which methodology. The platform is
accessible to network participants and to the general public.
Abstract: In this study, to clarify the effectiveness of an
aluminum/chromium/tungsten-based-coated tool for cutting sintered
steel, tool wear was experimentally investigated. The sintered steel
was turned with the (Al60,Cr25,W15)N-, (Al60,Cr25,W15)(C,N)- and
(Al64,Cr28,W8)(C,N)-coated cemented carbide tools according to the
physical vapor deposition (PVD) method. Moreover, the tool wear of
the aluminum/chromium/tungsten-based-coated item was compared
with that of the (Al,Cr)N coated tool. Furthermore, to clarify the tool
wear mechanism of the aluminum/chromium/tungsten-coating film for
cutting sintered steel, Scanning Electron Microscope observation and
Energy Dispersive x-ray Spectroscopy mapping analysis were
conducted on the abraded surface. The following results were
obtained: (1) The wear progress of the (Al64,Cr28,W8)(C,N)-coated
tool was the slowest among that of the five coated tools. (2) Adding
carbon (C) to the aluminum/chromium/tungsten-based-coating film
was effective for improving the wear-resistance. (3) The main wear
mechanism of the (Al60,Cr25,W15)N-, the (Al60,Cr25,W15)(C,N)-
and the (Al64,Cr28,W8)(C,N)-coating films was abrasive wear.
Abstract: Artificial neural networks have gained a lot of interest
as empirical models for their powerful representational capacity,
multi input and output mapping characteristics. In fact, most feedforward
networks with nonlinear nodal functions have been proved to
be universal approximates. In this paper, we propose a new
supervised method for color image classification based on selforganizing
feature maps (SOFM). This algorithm is based on
competitive learning. The method partitions the input space using
self-organizing feature maps to introduce the concept of local
neighborhoods. Our image classification system entered into RGB
image. Experiments with simulated data showed that separability of
classes increased when increasing training time. In additional, the
result shows proposed algorithms are effective for color image
classification.
Abstract: The work aims to develop a robot in the form of
autonomous vehicle to detect, inspection and mapping of
underground pipelines through the ATmega328 Arduino platform.
Hardware prototyping is very similar to C / C ++ language that
facilitates its use in robotics open source, resembles PLC used in
large industrial processes. The robot will traverse the surface
independently of direct human action, in order to automate the
process of detecting buried pipes, guided by electromagnetic
induction. The induction comes from coils that send the signal to the
Arduino microcontroller contained in that will make the difference in
intensity and the treatment of the information, and then this
determines actions to electrical components such as relays and
motors, allowing the prototype to move on the surface and getting the
necessary information. This change of direction is performed by a
stepper motor with a servo motor. The robot was developed by
electrical and electronic assemblies that allowed test your application.
The assembly is made up of metal detector coils, circuit boards and
microprocessor, which interconnected circuits previously developed
can determine, process control and mechanical actions for a robot
(autonomous car) that will make the detection and mapping of buried
pipelines plates. This type of prototype can prevent and identifies
possible landslides and they can prevent the buried pipelines suffer an
external pressure on the walls with the possibility of oil leakage and
thus pollute the environment.
Abstract: There have been rigorous research and development
of unmanned aerial vehicles in the field of search and rescue (SAR)
operation recently. UAVs reduce unnecessary human risks while
assisting rescue efforts through aerial imagery, topographic mapping
and emergency delivery. The application of UAVs in offshore and
nearshore marine SAR missions is discussed in this paper. Projects
that integrate UAV technology into their systems are introduced to
highlight the great advantages and capabilities of UAVs. Scenarios
where UAVs could provide invaluable assistance are also suggested.
Abstract: The present study is concerned with the problem of determining the shape of the free surface flow in a hydraulic channel which has an uneven bottom. For the mathematical formulation of the problem, the fluid of the two-dimensional irrotational steady flow in water is assumed inviscid and incompressible. The solutions of the nonlinear problem are obtained by using the usual conformal mapping theory and Hilbert’s technique. An experimental study, for comparing the obtained results, has been conducted in a hydraulic channel (subcritical regime and supercritical regime).
Abstract: For this study, a town based soil database created in
Gümüsçay District of Biga Town, Çanakkale, Turkey. Crop and
livestock production are major activities in the district. Nutrient
management is mainly based on commercial fertilizer application
ignoring the livestock manure. Within the boundaries of district, 122
soil sampling points determined over the satellite image. Soil samples
collected from the determined points with the help of handheld
Global Positioning System. Labeled samples were sent to a
commercial laboratory to determine 11 soil parameters including
salinity, pH, lime, organic matter, nitrogen, phosphorus, potassium,
iron, manganese, copper and zinc. Based on the test results soil maps
for mentioned parameters were developed using remote sensing, GIS,
and geostatistical analysis. In this study we developed a GIS database
that will be used for soil nutrient management. Methods were
explained and soil maps and their interpretations were summarized in
the study.
Abstract: The Scheduling and mapping of tasks on a set of
processors is considered as a critical problem in parallel and
distributed computing system. This paper deals with the problem of
dynamic scheduling on a special type of multiprocessor architecture
known as Linear Crossed Cube (LCQ) network. This proposed
multiprocessor is a hybrid network which combines the features of
both linear types of architectures as well as cube based architectures.
Two standard dynamic scheduling schemes namely Minimum
Distance Scheduling (MDS) and Two Round Scheduling (TRS)
schemes are implemented on the LCQ network. Parallel tasks are
mapped and the imbalance of load is evaluated on different set of
processors in LCQ network. The simulations results are evaluated
and effort is made by means of through analysis of the results to
obtain the best solution for the given network in term of load
imbalance left and execution time. The other performance matrices
like speedup and efficiency are also evaluated with the given
dynamic algorithms.
Abstract: This exploratory study gives an overview of the
evolution of the main financial and performance indicators of the
Academic Spin-Off’s and High Growth Academic Spin-Off’s in year
3 and year 6 after its creation in the region of Catalonia in Spain. The
study compares and evaluates results of these different measures of
performance and the degree of success of these companies for each
University.
We found that the average Catalonian Academic Spin-Off is small
and have not achieved the sustainability stage at year 6. On the
contrary, a small group of High Growth Academic Spin-Off’s
exhibits robust performance with high profits in year 6. Our results
support the need to increase selectivity and support for these
companies especially near year 3, because are the ones that will bring
wealth and employment. University role as an investor has rigid
norms and habits that impede an efficient economic return from their
ASO investment.
Universities with high performance on sales and employment in
year 3 not always could sustain this growth in year 6 because their
ASO’s are not profitable. On the contrary, profitable ASO exhibit
superior performance in all measurement indicators in year 6. We
advocate the need of a balanced growth (with profits) as a way to
obtain subsequent continuous growth.
Abstract: Applications of the Hausdorff space and its mappings
into tangent spaces are outlined, including their fractal dimensions
and self-similarities. The paper details this theory set up and further
describes virtualizations and atomization of manufacturing processes.
It demonstrates novel concurrency principles that will guide
manufacturing processes and resources configurations. Moreover,
varying levels of details may be produced by up folding and breaking
down of newly introduced generic models. This choice of layered
generic models for units and systems aspects along specific aspects
allows research work in parallel to other disciplines with the same
focus on all levels of detail. More credit and easier access are granted
to outside disciplines for enriching manufacturing grounds. Specific
mappings and the layers give hints for chances for interdisciplinary
outcomes and may highlight more details for interoperability
standards, as already worked on the international level. The new rules
are described, which require additional properties concerning all
involved entities for defining distributed decision cycles, again on the
base of self-similarity. All properties are further detailed and assigned
to a maturity scale, eventually displaying the smartness maturity of a
total shopfloor or a factory. The paper contributes to the intensive
ongoing discussion in the field of intelligent distributed
manufacturing and promotes solid concepts for implementations of
Cyber Physical Systems and the Internet of Things into
manufacturing industry, like industry 4.0, as discussed in German-speaking
countries.
Abstract: We have been grouping and developing various kinds
of practical, promising sensing applied systems concerning
agricultural advancement and technical tradition (guidance). These
include advanced devices to secure real-time data related to worker
motion, and we analyze by methods of various advanced statistics and
human dynamics (e.g. primary component analysis, Ward system
based cluster analysis, and mapping). What is more, we have been
considering worker daily health and safety issues. Targeted fields are
mainly common farms, meadows, and gardens. After then, we
observed and discussed time-line style, changing data. And, we made
some suggestions. The entire plan makes it possible to improve both
the aforementioned applied systems and farms.
Abstract: Over the past few years, the online multimedia
collection has grown at a fast pace. Several companies showed
interest to study the different ways to organise the amount of audio
information without the need of human intervention to generate
metadata. In the past few years, many applications have emerged on
the market which are capable of identifying a piece of music in a
short time. Different audio effects and degradation make it much
harder to identify the unknown piece. In this paper, an audio
fingerprinting system which makes use of a non-parametric based
algorithm is presented. Parametric analysis is also performed using
Gaussian Mixture Models (GMMs). The feature extraction methods
employed are the Mel Spectrum Coefficients and the MPEG-7 basic
descriptors. Bin numbers replaced the extracted feature coefficients
during the non-parametric modelling. The results show that nonparametric
analysis offer potential results as the ones mentioned in
the literature.
Abstract: Maize constitutes a major agrarian production for use
by the vast population but despite its economic importance; it has not
been produced to meet the economic needs of the country. Achieving
optimum yield in maize can meaningfully be supported by land
suitability analysis in order to guarantee self-sufficiency for future
production optimization. This study examines land suitability for
maize production through the analysis of the physicochemical
variations in soil properties and other land attributes over space using
a Geographic Information System (GIS) framework.
Physicochemical parameters of importance selected include slope,
landuse, physical and chemical properties of the soil, and climatic
variables. Landsat imagery was used to categorize the landuse,
Shuttle Radar Topographic Mapping (SRTM) generated the slope and
soil samples were analyzed for its physical and chemical components.
Suitability was categorized into highly, moderately and marginally
suitable based on Food and Agricultural Organisation (FAO)
classification, using the Analytical Hierarchy Process (AHP)
technique of GIS. This result can be used by small scale farmers for
efficient decision making in the allocation of land for maize
production.
Abstract: The Cone Penetration Test (CPT) is a common in-situ
test which generally investigates a much greater volume of soil more
quickly than possible from sampling and laboratory tests. Therefore,
it has the potential to realize both cost savings and assessment of soil
properties rapidly and continuously. The principle objective of this
paper is to demonstrate the feasibility and efficiency of using
artificial neural networks (ANNs) to predict the soil angle of internal
friction (Φ) and the soil modulus of elasticity (E) from CPT results
considering the uncertainties and non-linearities of the soil. In
addition, ANNs are used to study the influence of different
parameters and recommend which parameters should be included as
input parameters to improve the prediction. Neural networks discover
relationships in the input data sets through the iterative presentation
of the data and intrinsic mapping characteristics of neural topologies.
General Regression Neural Network (GRNN) is one of the powerful
neural network architectures which is utilized in this study. A large
amount of field and experimental data including CPT results, plate
load tests, direct shear box, grain size distribution and calculated data
of overburden pressure was obtained from a large project in the
United Arab Emirates. This data was used for the training and the
validation of the neural network. A comparison was made between
the obtained results from the ANN's approach, and some common
traditional correlations that predict Φ and E from CPT results with
respect to the actual results of the collected data. The results show
that the ANN is a very powerful tool. Very good agreement was
obtained between estimated results from ANN and actual measured
results with comparison to other correlations available in the
literature. The study recommends some easily available parameters
that should be included in the estimation of the soil properties to
improve the prediction models. It is shown that the use of friction
ration in the estimation of Φ and the use of fines content in the
estimation of E considerable improve the prediction models.