Abstract: Skin aging is a slow multifactorial process influenced
by both internal as well as external factors. Ultra-violet radiations
(UV), diet, smoking and personal habits are the most common
environmental factors that affect skin aging. Fat contents and fibrous
proteins as collagen and elastin are core internal structural
components. The direct influence of UV on elastin integrity and
health is central on aging of skin especially by time. The deposition
of abnormal elastic material is a major marker in a photo-aged skin.
Searching for compounds that may protect against cutaneous photodamage
is exceedingly valued. Retinoids and alpha hydroxy acids
have been endorsed by some researchers as possible candidates for
protecting and or repairing the effect of UV damaged skin. For
consolidating a better system of anti- and protective effects of such
anti-aging agents, we evaluated the combinatory effects of various
dosages of lactic acid and retinol on the dermal fibroblast’s elastin
levels exposed to UV. The UV exposed cells showed significant
reduction in the elastin levels. A combination of drugs with a higher
concentration of lactic acid (30 -35 mM) and a lower concentration of
retinol (10-15mg/mL) showed to work better in maintaining elastin
concentration in UV exposed cells. We assume this preservation
could be the result of increased tropo-elastin gene expression
stimulated by retinol whereas lactic acid probably repaired the UV
irradiated damage by enhancing the amount and integrity of the
elastin fibers.
Abstract: In this paper, we used data mining to extract
biomedical knowledge. In general, complex biomedical data
collected in studies of populations are treated by statistical methods,
although they are robust, they are not sufficient in themselves to
harness the potential wealth of data. For that you used in step two
learning algorithms: the Decision Trees and Support Vector Machine
(SVM). These supervised classification methods are used to make the
diagnosis of thyroid disease. In this context, we propose to promote
the study and use of symbolic data mining techniques.
Abstract: Electroencephalogram (EEG) is a noninvasive
technique that registers signals originating from the firing of neurons
in the brain. The Emotiv EEG Neuroheadset is a consumer product
comprised of 14 EEG channels and was used to record the reactions
of the neurons within the brain to two forms of stimuli in 10
participants. These stimuli consisted of auditory and visual formats
that provided directions of ‘right’ or ‘left.’ Participants were
instructed to raise their right or left arm in accordance with the
instruction given. A scenario in OpenViBE was generated to both
stimulate the participants while recording their data. In OpenViBE,
the Graz Motor BCI Stimulator algorithm was configured to govern
the duration and number of visual stimuli. Utilizing EEGLAB under
the cross platform MATLAB®, the electrodes most stimulated during
the study were defined. Data outputs from EEGLAB were analyzed
using IBM SPSS Statistics® Version 20. This aided in determining
the electrodes to use in the development of a brain-machine interface
(BMI) using real-time EEG signals from the Emotiv EEG
Neuroheadset. Signal processing and feature extraction were
accomplished via the Simulink® signal processing toolbox. An
Arduino™ Duemilanove microcontroller was used to link the Emotiv
EEG Neuroheadset and the right and left Mecha TE™ Hands.
Abstract: The article discusses the legal framework of the
government’s environmental function and analyzes the role of the
national policy in protection of wetlands. The problem is of interest
for it deals with the most important branch of economy – utilization
of Kazakhstan’s natural resources, protection of health and
environmental wellbeing of the population. Development of a longterm
environmental program addressing the protection of wetlands
represents the final stage of the government’s environmental policy,
and is a relatively new function for the public administration system.
It appeared due to the environmental measures that require immediate
decisions to be taken. It is an integral part of the effort in the field of
management of state-owned natural resource, as well as of the
measures aimed at efficient management of natural resources to avoid
their early depletion or contamination.
Abstract: In this paper, reliable consensus of multi-agent systems
with sampled-data is investigated. By using a suitable
Lyapunov-Krasovskii functional and some techniques such as
Wirtinger Inequality, Schur Complement and Kronecker Product, the
results of such system are obtained by solving a set of Linear Matrix
Inequalities (LMIs). One numerical example is included to show the
effectiveness of the proposed criteria.
Abstract: In the paper, information on economic development
trends in developed countries are analyzed. The current status of
information society and economy of the country is reviewed and
some recommendations are given for future development.
The problems of Information Society and establishment of its
innovative economy are studied. In this turn, development trends
information economy in developed countries are analyzed.
Abstract: This paper presents powerful techniques for the
development of a new monitoring method based on multi-scale
entropy (MSE) in order to characterize the behaviour of the
concentrations of different gases present in the synthesis of Ammonia
and soft-sensor based on Principal Component Analysis (PCA).
Abstract: Paints are the most widely used methods of protection
against atmospheric corrosion of metals. The aim of this work was to
determine the protective performance of epoxy coating against sea
water before and after damage.
Investigations are conducted using stationary and non-stationary
electrochemical tools such as electrochemical impedance
spectroscopy has allowed us to characterize the protective qualities of
these films. The application of the EIS on our damaged in-situ
painting shows the existence of several capacitive loops which is an
indicator of the failure of our tested paint. Microscopic analysis
(micrograph) helped bring essential elements in understanding the
degradation of our paint condition and immersion training corrosion
products.
Abstract: This paper presents two techniques, local feature
extraction using image spectrum and low frequency spectrum
modelling using GMM to capture the underlying statistical
information to improve the performance of face recognition
system. Local spectrum features are extracted using overlap sub
block window that are mapped on the face image. For each of this
block, spatial domain is transformed to frequency domain using
DFT. A low frequency coefficient is preserved by discarding high
frequency coefficients by applying rectangular mask on the
spectrum of the facial image. Low frequency information is non-
Gaussian in the feature space and by using combination of several
Gaussian functions that has different statistical properties, the best
feature representation can be modelled using probability density
function. The recognition process is performed using maximum
likelihood value computed using pre-calculated GMM components.
The method is tested using FERET datasets and is able to achieved
92% recognition rates.
Abstract: Pedagogy has always been open to other disciplines
that reflect about the educational process (philosophy, sociology,
psychology, anthropology, technology, etc.). Its interdisciplinary
openness puts education, as the subject of pedagogy within a broader
context of the community, enabling the knowledge of other
disciplines to contribute to a better understanding of the fundamental
pedagogical notion of education. The purpose of pedagogy as a
science serves humans, strives towards humans, must be for humans,
and this is its ultimate goal. Humans are essentially dependent on
education, which is also considered as a category of humans’ being,
because through education an entire world develops in humans.
Anthropological assumptions of humans as "deficient beings" see the
solution in education, but they also indicate a wealth of shortcomings,
because they provide an opportunity for enrichment and formation of
culture, living and the self. In that context, this paper illustrates the
determination of pedagogy through an anthropological conception of
humans and the phenomenon of education. It presents a review of
anthropological ideas about education, by providing an analysis of
relevant literature dealing with the anthropological notion of humans,
which provides fruitful conditions for a pedagogical reconsideration
of education.
Abstract: Surface modification and functionalization has been
an important tool for scientists in order to open new frontiers in
nanoscience and nanotechnology. Desired surface characteristics for
the intended applications can be achieved with surface
functionalization.
In this work, the effect of water soluble ligands on the adsorption
capabilities of silver nanoparticles onto AC which was synthesized
from German beech wood was investigated. Sodium borohydride
(NaBH4) and polyvinyl alcohol (PVA) were used as the ligands.
Silver nanoparticles with different surface coatings have average
sizes range from 10 to 13 nm. They were synthesized in aqueous
media by reducing Ag (I) ion in the presence of ligands. These
particles displayed adsorption tendencies towards AC when they
were mixed together and shaken in distilled water.
Silver nanoparticles (NaBH4-AgNPs) reduced and stabilized by
NaBH4 adsorbed onto AC with a homogenous dispersion of
aggregates with sizes in the range of 100-400 nm. Beside, silver
nanoparticles, which were prepared in the presence of both NaBH4
and PVA (NaBH4/PVA-Ag NPs), demonstrated that NaBH4/PVA-Ag
NPs adsorbed and dispersed homogenously but, they aggregated with
larger sizes on the AC surface (range from 300 to 600 nm). In
addition, desorption resistance of Ag nanoparticles were investigated
in distilled water. According to the results AgNPs were not desorbed
on the AC surface in distilled water.
Abstract: Natural hydrocarbon seepage has helped petroleum
exploration as a direct indicator of gas and/or oil subsurface
accumulations. Surface macro-seeps are generally an indication of a
fault in an active Petroleum Seepage System belonging to a Total
Petroleum System. This paper describes a case study in which
multiple analytical techniques were used to identify and characterize
trace petroleum-related hydrocarbons and other volatile organic
compounds in groundwater samples collected from Sousse aquifer
(Central Tunisia). The analytical techniques used for analyses of
water samples included gas chromatography-mass spectrometry (GCMS),
capillary GC with flame-ionization detection, Compound
Specific Isotope Analysis, Rock Eval Pyrolysis. The objective of the
study was to confirm the presence of gasoline and other petroleum
products or other volatile organic pollutants in those samples in order
to assess the respective implication of each of the potentially
responsible parties to the contamination of the aquifer. In addition,
the degree of contamination at different depths in the aquifer was also
of interest. The oil and gas seeps have been investigated using
biomarker and stable carbon isotope analyses to perform oil-oil and
oil-source rock correlations. The seepage gases are characterized by
high CH4 content, very low δ13CCH4 values (-71,9 ‰) and high
C1/C1–5 ratios (0.95–1.0), light deuterium–hydrogen isotope ratios (-
198 ‰) and light δ13CC2 and δ13CCO2 values (-23,8‰ and-23,8‰
respectively) indicating a thermogenic origin with the contribution of
the biogenic gas. An organic geochemistry study was carried out on
the more ten oil seep samples. This study includes light hydrocarbon
and biomarkers analyses (hopanes, steranes, n-alkanes, acyclic
isoprenoids, and aromatic steroids) using GC and GC-MS. The
studied samples show at least two distinct families, suggesting two
different types of crude oil origins: the first oil seeps appears to be
highly mature, showing evidence of chemical and/or biological
degradation and was derived from a clay-rich source rock deposited
in suboxic conditions. It has been sourced mainly by the lower
Fahdene (Albian) source rocks. The second oil seeps was derived
from a carbonate-rich source rock deposited in anoxic conditions,
well correlated with the Bahloul (Cenomanian-Turonian) source rock.
Abstract: Content Based Image Retrieval (CBIR) coupled with
Case Based Reasoning (CBR) is a paradigm that is becoming
increasingly popular in the diagnosis and therapy planning of medical
ailments utilizing the digital content of medical images. This paper
presents a survey of some of the promising approaches used in the
detection of abnormalities in retina images as well in
mammographic screening and detection of regions of interest
in MRI scans of the brain. We also describe our proposed
algorithm to detect hard exudates in fundus images of the
retina of Diabetic Retinopathy patients.
Abstract: Part and parcel of building green homes (GHs) with
favorable thermal comfort (TC) is to design and build with reduced
carbon footprint (CF) from embodied energy in the building envelope
and reduced operational CF overall. Together, the environmental
impact of GHs can be reduced significantly. Nevertheless, there is
still a need to identify the base CF value for Malaysian GHs and this
can be done by assessing existing ones which can then be compared
to conventional and vernacular houses which are built differently
with different building materials. This paper underlines the research
design and introduces the case studies. For now, the operational CF
of the case studies is beyond the scope of this study. Findings from
this research could identify the best building material and
construction technique combination to build GHs depending on the
available skills, financial constraints and the condition of the
immediate environment.
Abstract: Multi-Level Inverter technology has been developed in the area of high-power medium-voltage energy scheme, because of their advantages such as devices of lower rating can be used thereby enabling the schemes to be used for high voltage applications. Reduced Total Harmonic Distortion (THD).Since the dv/dt is low; the Electromagnetic Interference from the scheme is low. To avoid the switching losses Lower switching frequencies can be used. In this paper present a survey of various topologies, control strategy and modulation techniques used by these inverters. Here the regenerative and superior topologies are also discussed.
Abstract: Chitosan has been an attractive biopolymer for
decades, but its processability is lowered by its poor solubility,
especially in physiological pH values. Freeze concentrated reactions
of chitosan with several organic acids including acrylic, citraconic,
itaconic, and maleic acid revealed improved solubility and
morphological properties. Solubility traits were assessed with a
modified ninhydrin test. Chitosan derivatives were characterized by
ATR-FTIR and morphological characteristics were determined by
SEM. This study is a unique approach to chemically modify chitosan
to enhance water solubility.
Abstract: Planning of infrastructure and processes in logistic
center within the frame of various kinds of logistic hubs and
technological activities in them represent quite complex problem.
The main goal is to design appropriate layout, which enables to
realize expected operation on the desired levels. The simulation
software represents progressive contemporary experimental
technique, which can support complex processes of infrastructure
planning and all of activities on it. It means that simulation
experiments, reflecting various planned infrastructure variants,
investigate and verify their eligibilities in relation with corresponding
expected operation. The inducted approach enables to make qualified
decisions about infrastructure investments or measures, which derive
benefit from simulation-based verifications. The paper represents
simulation software for simulation infrastructural layout and
technological activities in marshalling yard, intermodal terminal,
warehouse and combination between them as the parts of logistic
center.
Abstract: The aim of this paper is to select the most accurate
forecasting method for predicting the future values of the
unemployment rate in selected European countries. In order to do so,
several forecasting techniques adequate for forecasting time series
with trend component, were selected, namely: double exponential
smoothing (also known as Holt`s method) and Holt-Winters` method
which accounts for trend and seasonality. The results of the empirical
analysis showed that the optimal model for forecasting
unemployment rate in Greece was Holt-Winters` additive method. In
the case of Spain, according to MAPE, the optimal model was double
exponential smoothing model. Furthermore, for Croatia and Italy the
best forecasting model for unemployment rate was Holt-Winters`
multiplicative model, whereas in the case of Portugal the best model
to forecast unemployment rate was Double exponential smoothing
model. Our findings are in line with European Commission
unemployment rate estimates.
Abstract: Due to the rapid increase of Internet, web opinion
sources dynamically emerge which is useful for both potential
customers and product manufacturers for prediction and decision
purposes. These are the user generated contents written in natural
languages and are unstructured-free-texts scheme. Therefore, opinion
mining techniques become popular to automatically process customer
reviews for extracting product features and user opinions expressed
over them. Since customer reviews may contain both opinionated and
factual sentences, a supervised machine learning technique applies
for subjectivity classification to improve the mining performance. In
this paper, we dedicate our work is the task of opinion
summarization. Therefore, product feature and opinion extraction is
critical to opinion summarization, because its effectiveness
significantly affects the identification of semantic relationships. The
polarity and numeric score of all the features are determined by
Senti-WordNet Lexicon. The problem of opinion summarization
refers how to relate the opinion words with respect to a certain
feature. Probabilistic based model of supervised learning will
improve the result that is more flexible and effective.
Abstract: Quantification of cardiac function is performed by
calculating blood volume and ejection fraction in routine clinical
practice. However, these works have been performed by manual
contouring, which requires computational costs and varies on the
observer. In this paper, an automatic left ventricle segmentation
algorithm on cardiac magnetic resonance images (MRI) is presented.
Using knowledge on cardiac MRI, a K-mean clustering technique is
applied to segment blood region on a coil-sensitivity corrected image.
Then, a graph searching technique is used to correct segmentation
errors from coil distortion and noises. Finally, blood volume and
ejection fraction are calculated. Using cardiac MRI from 15 subjects,
the presented algorithm is tested and compared with manual
contouring by experts to show outstanding performance.