Abstract: Air pollution is a major environmental health
problem, affecting developed and developing countries around the
world. Increasing amounts of potentially harmful gases and
particulate matter are being emitted into the atmosphere on a global
scale, resulting in damage to human health and the environment.
Petroleum-related air pollutants can have a wide variety of adverse
environmental impacts. In the crude oil production sectors, there is a
strong need for a thorough knowledge of gaseous emissions resulting
from the flaring of associated gas of known composition on daily
basis through combustion activities under several operating
conditions. This can help in the control of gaseous emission from
flares and thus in the protection of their immediate and distant
surrounding against environmental degradation.
The impacts of methane and non-methane hydrocarbons emissions
from flaring activities at oil production facilities at Kuwait Oilfields
have been assessed through a screening study using records of flaring
operations taken at the gas and oil production sites, and by analyzing
available meteorological and air quality data measured at stations
located near anthropogenic sources. In the present study the
Industrial Source Complex (ISCST3) Dispersion Model is used to
calculate the ground level concentrations of methane and nonmethane
hydrocarbons emitted due to flaring in all over Kuwait
Oilfields.
The simulation of real hourly air quality in and around oil
production facilities in the State of Kuwait for the year 2006,
inserting the respective source emission data into the ISCST3
software indicates that the levels of non-methane hydrocarbons from
the flaring activities exceed the allowable ambient air standard set by
Kuwait EPA. So, there is a strong need to address this acute problem
to minimize the impact of methane and non-methane hydrocarbons
released from flaring activities over the urban area of Kuwait.
Abstract: In this article, we propose an Intelligent Medical
Diagnostic System (IMDS) accessible through common
web-based interface, to on-line perform initial screening for
osteoporosis. The fundamental approaches which construct the
proposed system are mainly based on the fuzzy-neural theory,
which can exhibit superiority over other conventional technologies
in many fields. In diagnosis process, users simply answer
a series of directed questions to the system, and then they
will immediately receive a list of results which represents the
risk degrees of osteoporosis. According to clinical testing results,
it is shown that the proposed system can provide the general
public or even health care providers with a convenient, reliable,
inexpensive approach to osteoporosis risk assessment.
Abstract: In the last few years, three multivariate spectral
analysis techniques namely, Principal Component Analysis (PCA),
Independent Component Analysis (ICA) and Non-negative Matrix
Factorization (NMF) have emerged as effective tools for oscillation
detection and isolation. While the first method is used in determining
the number of oscillatory sources, the latter two methods
are used to identify source signatures by formulating the detection
problem as a source identification problem in the spectral domain.
In this paper, we present a critical drawback of the underlying linear
(mixing) model which strongly limits the ability of the associated
source separation methods to determine the number of sources
and/or identify the physical source signatures. It is shown that the
assumed mixing model is only valid if each unit of the process gives
equal weighting (all-pass filter) to all oscillatory components in its
inputs. This is in contrast to the fact that each unit, in general, acts
as a filter with non-uniform frequency response. Thus, the model
can only facilitate correct identification of a source with a single
frequency component, which is again unrealistic. To overcome
this deficiency, an iterative post-processing algorithm that correctly
identifies the physical source(s) is developed. An additional issue
with the existing methods is that they lack a procedure to pre-screen
non-oscillatory/noisy measurements which obscure the identification
of oscillatory sources. In this regard, a pre-screening procedure
is prescribed based on the notion of sparseness index to eliminate
the noisy and non-oscillatory measurements from the data set used
for analysis.