Abstract: The assessment variables of the accessibility and the sustainability of access infrastructure for remote regions may vary significant by location and a wide range of factors may affect the decision process. In this paper, the environmental disturbance implications of transportation system to key demand and supply variables impact the economic system in remote destination are descripted. According to a systemic approach, the key sustainability variables deals with decision making process that have to be included in strategic plan for the critical transport infrastructure development and their relationship to regional socioeconomic system are presented. The application deals with the development of railway in remote destinations, where the traditional CBA not include the external cost generated by the environmental impacts that may have a range of diverse impacts on transport infrastructure and services. The analysis output provides key messages to decision and policy makers towards sustainable development of transport infrastructure, especially for remote destinations where accessibility is a key factor of regional economic development and social stability. The key conclusion could be essential useful for relevant applications in remote regions in the same latitude.
Abstract: Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.
Abstract: This study examines the feasibility of indirect solar
desalination in oil producing countries in the Middle East and North
Africa (MENA) region. It relies on value engineering (VE) and costbenefit
with sensitivity analyses to identify optimal coupling
configurations of desalination and solar energy technologies. A
comparative return on investment was assessed as a function of water
costs for varied plant capacities (25,000 to 75,000 m3/day), project
lifetimes (15 to 25 years), and discount rates (5 to 15%) taking into
consideration water and energy subsidies, land cost as well as
environmental externalities in the form of carbon credit related to
greenhouse gas (GHG) emissions reduction. The results showed
reverse osmosis (RO) coupled with photovoltaic technologies (PVs)
as the most promising configuration, robust across different prices for
Brent oil, discount rates, as well as different project lifetimes.
Environmental externalities and subsidies analysis revealed that a
16% reduction in existing subsidy on water tariffs would ensure
economic viability. Additionally, while land costs affect investment
attractiveness, the viability of RO coupled with PV remains possible
for a land purchase cost
Abstract: The decision-making processes in Corporate Social Responsibility (CSR) among firms in the airlines industry borders on the benefits that accrue to firms through those investments. The crux of the matter is how firms can quantify the benefits derived from such investments. This paper analyses the cost benefit adjustment strategies for firms in the airline industry in their CSR strategy adoption and implementation. The paper discusses the CBA model in order to understand the ways airlines can reduce costs and increase returns on CSR, or balance the cost and benefits. The analysis indicates that, economic concepts especially the CBA are useful, though they are not without challenges. This paper concludes that the CBA model gives a basic understanding of the motivations for investing in intangible assets like CSR. It sets the tone for formulating relevant hypothesis in empirical studies in investment in CSR and other intangible assets in business operations.
Abstract: Associative classification (AC) is a data mining approach that combines association rule and classification to build classification models (classifiers). AC has attracted a significant attention from several researchers mainly because it derives accurate classifiers that contain simple yet effective rules. In the last decade, a number of associative classification algorithms have been proposed such as Classification based Association (CBA), Classification based on Multiple Association Rules (CMAR), Class based Associative Classification (CACA), and Classification based on Predicted Association Rule (CPAR). This paper surveys major AC algorithms and compares the steps and methods performed in each algorithm including: rule learning, rule sorting, rule pruning, classifier building, and class prediction.
Abstract: The Pads have unique values of thermophysical
properties (THP) having important contribution over heat transfer
into the PCB structure.
Materials with high thermal diffusivity (TD) rapidly adjust their
temperature to that of their surroundings, because the HT is quick in
compare to their volumetric heat capacity (VHC).
In the paper is presenting the diffusivity tests (ASTM E1461 flash
method) for PCBs with different core materials. In the experiments,
the multilayer structure of PCBA was taken into consideration, an
equivalent property referring to each of experimental structure be
practically measured.
Concerning to entire structure, the THP emphasize the major
contribution of substrate in establishing of reflow soldering process
(RSP) heat transfer necessities. This conclusion offer practical
solution for heat transfer time constant calculation as function of
thickness and substrate material diffusivity with an acceptable error
estimation.
Abstract: Calculations of energy efficiency of several AACbased
building envelopes under different climatic conditions are
presented. As thermal insulating materials, expanded polystyrene and
hydrophobic and hydrophilic mineral wools are assumed. The
computations are accomplished using computer code HEMOT
developed at Department of Materials Engineering, Faculty of Civil
Engineering at the Czech Technical University in Prague. The
climatic data of Athens, Kazan, Oslo, Prague and Reykjavík are
obtained using METEONORM software.
Abstract: Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In the light of the clustering data, we have verified some interactions which were not identified as core interactions in DIP and also, we have characterized some functionally unknown proteins according to the interaction data and functional correlation. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins, also to predict new interactions and to characterize functions of unknown proteins.
Abstract: Region covariance (RC) descriptor is an effective
and efficient feature for visual tracking. Current RC-based tracking
algorithms use the whole RC matrix to track the target in video
directly. However, there exist some issues for these whole RCbased
algorithms. If some features are contaminated, the whole RC
will become unreliable, which results in lost object-tracking. In
addition, if some features are very discriminative to the
background, other features are still processed and thus reduce the
efficiency. In this paper a new robust tracking method is proposed,
in which the whole RC matrix is decomposed into several low rank
matrices. Those matrices are dynamically chosen and processed so
as to achieve a good tradeoff between discriminability and
complexity. Experimental results have shown that our method is
more robust to complex environment changes, especially either
when occlusion happens or when the background is similar to the
target compared to other RC-based methods.
Abstract: Champs Bourcin black grape originated from
Aquitaine, France and planted in Sapa, Lao cai provice, exhibited
high total acidity (11.72 g/L). After 9 days of alcoholic fermentation
at 25oC using Saccharomyces cerevisiae UP3OY5 strain, the ethanol
concentration of wine was 11.5% v/v, however the sharp sour taste of
wine has been found. The malolactic fermentation (MLF) was carried
out by Oenococcus oeni ATCCBAA-1163 strain which had been preadapted
to acid (pH 3-4) and ethanol (8-12%v/v) conditions. We
obtained the highest vivability (83.2%) upon malolactic fermentation
after 5 days at 22oC with early stationary phase O. oeni cells preadapted
to pH 3.5 and 8% v/v ethanol in MRS medium. The malic
acid content in wine was decreased from 5.82 g/L to 0.02 g/L after
MLF (21 days at 22oC). The sensory quality of wine was
significantly improved.
Abstract: Textures are replications, symmetries and
combinations of various basic patterns, usually with some random
variation one of the gray-level statistics. This article proposes a
new approach to Segment texture images. The proposed approach
proceeds in 2 stages. First, in this method, local texture information
of a pixel is obtained by fuzzy texture unit and global texture
information of an image is obtained by fuzzy texture spectrum.
The purpose of this paper is to demonstrate the usefulness of fuzzy
texture spectrum for texture Segmentation.
The 2nd Stage of the method is devoted to a decision process,
applying a global analysis followed by a fine segmentation,
which is only focused on ambiguous points. The above Proposed
approach was applied to brain image to identify the components
of brain in turn, used to locate the brain tumor and its Growth
rate.
Abstract: Understanding the cell's large-scale organization is an
interesting task in computational biology. Thus, protein-protein
interactions can reveal important organization and function of the
cell. Here, we investigated the correspondence between protein
interactions and function for the yeast. We obtained the correlations
among the set of proteins. Then these correlations are clustered using
both the hierarchical and biclustering methods. The detailed analyses
of proteins in each cluster were carried out by making use of their
functional annotations. As a result, we found that some functional
classes appear together in almost all biclusters. On the other hand, in
hierarchical clustering, the dominancy of one functional class is
observed. In brief, from interaction data to function, some correlated
results are noticed about the relationship between interaction and
function which might give clues about the organization of the
proteins.
Abstract: The European countries that during the past two
decades based their exchange rate regimes on currency board
arrangement (CBA) are usually analysed from the perspective of
corner solution choice’s stabilisation effects. There is an open
discussion on the positive and negative background of a strict
exchange rate regime choice, although it should be seen as part of the
transition process towards the monetary union membership. The
focus of the paper is on the Baltic countries that after two decades of
a rigid exchange rate arrangement and strongly influenced by global
crisis are finishing their path towards the euro zone. Besides the
stabilising capacity, the CBA is highly vulnerable regime, with
limited developing potential. The rigidity of the exchange rate (and
monetary) system, despite the ensured credibility, do not leave
enough (or any) space for the adjustment and/or active crisis
management. Still, the Baltics are in a process of recovery, with fiscal
consolidation measures combined with (painful and politically
unpopular) measures of internal devaluation. Today, two of them
(Estonia and Latvia) are members of euro zone, fulfilling their
ultimate transition targets, but de facto exchanging one fixed regime
with another.
The paper analyses the challenges for the CBA in unstable
environment since the fixed regimes rely on imported stability and
are sensitive to external shocks. With limited monetary instruments,
these countries were oriented to the fiscal policies and used a
combination of internal devaluation and tax policy measures. Despite
their rather quick recovery, our second goal is to analyse the long
term influence that the measures had on the national economy.
Abstract: Streaming Applications usually run in parallel or in
series that incrementally transform a stream of input data. It poses a
design challenge to break such an application into distinguishable
blocks and then to map them into independent hardware processing
elements. For this, there is required a generic controller that
automatically maps such a stream of data into independent processing
elements without any dependencies and manual considerations. In
this paper, Kahn Process Networks (KPN) for such streaming
applications is designed and developed that will be mapped on
MPSoC. This is designed in such a way that there is a generic Cbased
compiler that will take the mapping specifications as an input
from the user and then it will automate these design constraints and
automatically generate the synthesized RTL optimized code for
specified application.