Abstract: In this study, a mathematical model was proposed and
the accuracy of this model was assessed to predict the growth of
Pseudomonas aeruginosa and rhamnolipid production under nitrogen
limiting (sodium nitrate) fed-batch fermentation. All of the
parameters used in this model were achieved individually without
using any data from the literature.
The overall growth kinetic of the strain was evaluated using a
dual-parallel substrate Monod equation which was described by
several batch experimental data. Fed-batch data under different
glycerol (as the sole carbon source, C/N=10) concentrations and feed
flow rates were used to describe the proposed fed-batch model and
other parameters. In order to verify the accuracy of the proposed
model several verification experiments were performed in a vast
range of initial glycerol concentrations. While the results showed an
acceptable prediction for rhamnolipid production (less than 10%
error), in case of biomass prediction the errors were less than 23%. It
was also found that the rhamnolipid production by P. aeruginosa was
more sensitive at low glycerol concentrations.
Based on the findings of this work, it was concluded that the
proposed model could effectively be employed for rhamnolipid
production by this strain under fed-batch fermentation on up to 80 g l-
1 glycerol.
Abstract: In the present work, the performance of the particle
swarm optimization and the genetic algorithm compared as a typical
geometry design problem. The design maximizes the heat transfer
rate from a given fin volume. The analysis presumes that a linear
temperature distribution along the fin. The fin profile generated using
the B-spline curves and controlled by the change of control point
coordinates. An inverse method applied to find the appropriate fin
geometry yield the linear temperature distribution along the fin
corresponds to optimum design. The numbers of the populations, the
count of iterations and time to convergence measure efficiency.
Results show that the particle swarm optimization is most efficient
for geometry optimization.
Abstract: Vitamin A deficiency is a public health problem in
Zimbabwe. Addressing vitamin A deficiency has the potential of
enhancing resistance to disease and reducing mortality especially in
children less than 5 years. We implemented and adapted vitamin A
outreach supplementation strategy within the National Immunization
Days and Extended Programme of Immunization in a rural district in
Zimbabwe. Despite usual operational challenges faced this approach
enabled the district to increase delivery of supplementation coverage.
This paper describes the outreach strategy that was implemented in
the remote rural district. The strategy covered 63 outreach sites with
2 sites being covered per day and visited once per month for the
whole year. Coverage reached 71% in an area of previous coverage
rates of around less than 50%. We recommend further exploration of
this strategy by others working in similar circumstances. This
strategy can be a potential way for use by Scaling-Up-Nutrition
member states.
Abstract: This work addresses the problem of optimizing
completely batch water-using network with multiple contaminants
where the flow change caused by mass transfer is taken into
consideration for the first time. A mathematical technique for
optimizing water-using network is proposed based on
source-tank-sink superstructure. The task is to obtain the freshwater
usage, recycle assignments among water-using units, wastewater
discharge and a steady water-using network configuration by
following steps. Firstly, operating sequences of water-using units are
determined by time constraints. Next, superstructure is simplified by
eliminating the reuse and recycle from water-using units with
maximum concentration of key contaminants. Then, the non-linear
programming model is solved by GAMS (General Algebra Model
System) for minimum freshwater usage, maximum water recycle and
minimum wastewater discharge. Finally, numbers of operating periods
are calculated to acquire the steady network configuration. A case
study is solved to illustrate the applicability of the proposed approach.
Abstract: Buildings are one of the valuable assets to provide
people with shelters for work, leisure and rest. After years of
attacks by weather, buildings will deteriorate which need proper
maintenance in order to fulfill the requirements and satisfaction of
the users. Poorly managed buildings not just give a negative image
to the city itself, but also pose potential risk hazards to the health
and safety of the general public. As a result, the management of
maintenance projects has played an important role in cities like
Hong Kong where the problem of urban decay has drawn much
attention. However, most research has focused on managing new
construction, and little research effort has been put on maintenance
projects. Given the short duration and more diversified nature of
work, repair and maintenance works are found to be more difficult
to monitor and regulate when compared with new works. Project
participants may face with problems in running maintenance
projects which should be investigated so that proper strategies can
be established. This paper aims to provide a thorough analysis on
the problems of running maintenance projects. A review of
literature on the characteristics of building maintenance projects
was firstly conducted, which forms a solid basis for the empirical
study. Results on the problems and difficulties of running
maintenance projects from the viewpoints of industry practitioners
will also be delivered with a view to formulating effective
strategies for managing maintenance projects successfully.
Abstract: The gas safety management system using an
intelligent gas meter we proposed is to monitor flow and
pressure of gas, earthquake, temperature, smoke and leak of
methane. Then our system takes safety measures to protect a
serious risk by the result of an event, to communicate with a
wall-pad including a gateway by zigbee network in buildings
and to report the event to user by the safety management
program in a server. Also, the inner cutoff valve of an
intelligent gas meter is operated if any event occurred or
abnormal at each sensor.
Abstract: For the sensor network to operate successfully, the active nodes should maintain both sensing coverage and network connectivity. Furthermore, scheduling sleep intervals plays critical role for energy efficiency of wireless sensor networks. Traditional methods for sensor scheduling use either sensing coverage or network connectivity, but rarely both. In this paper, we use random scheduling for sensing coverage and then turn on extra sensor nodes, if necessary, for network connectivity. Simulation results have demonstrated that the number of extra nodes that is on with upper bound of around 9%, is small compared to the total number of deployed sensor nodes. Thus energy consumption for switching on extra sensor node is small.
Abstract: Dynamic bandwidth allocation in EPONs can be
generally separated into inter-ONU scheduling and intra-ONU scheduling. In our previous work, the active intra-ONU scheduling
(AS) utilizes multiple queue reports (QRs) in each report message to cooperate with the inter-ONU scheduling and makes the granted
bandwidth fully utilized without leaving unused slot remainder (USR).
This scheme successfully solves the USR problem originating from the
inseparability of Ethernet frame. However, without proper setting of
threshold value in AS, the number of QRs constrained by the IEEE
802.3ah standard is not enough, especially in the unbalanced traffic
environment. This limitation may be solved by enlarging the threshold
value. The large threshold implies the large gap between the adjacent QRs, thus resulting in the large difference between the best granted bandwidth and the real granted bandwidth. In this paper, we integrate
AS with a cooperative prediction mechanism and distribute multiple
QRs to reduce the penalty brought by the prediction error.
Furthermore, to improve the QoS and save the usage of queue reports,
the highest priority (EF) traffic which comes during the waiting time is
granted automatically by OLT and is not considered in the requested
bandwidth of ONU. The simulation results show that the proposed
scheme has better performance metrics in terms of bandwidth
utilization and average delay for different classes of packets.
Abstract: By using the method of coincidence degree and constructing suitable Lyapunov functional, some sufficient conditions are established for the existence and global exponential stability of antiperiodic solutions for a kind of impulsive Cohen-Grossberg shunting inhibitory cellular neural networks (CGSICNNs) on time scales. An example is given to illustrate our results.
Abstract: Polyphenolics and sugar are the components of many
fruit juices. In this work, the performance of ultra-filtration (UF) for
separating phenolic compounds from apple juice was studied by
performing batch experiments in a membrane module with an area of
0.1 m2 and fitted with a regenerated cellulose membrane of 1 kDa
MWCO. The effects of various operating conditions: transmembrane
pressure (3, 4, 5 bar), temperature (30, 35, 40 ºC), pH (2, 3, 4, 5),
feed concentration (3, 5, 7, 10, 15 ºBrix for apple juice) and feed flow
rate (1, 1.5, 1.8 L/min) on the performance were determined.
The optimum operating conditions were: transmembrane pressure
4 bar, temperature 30 ºC, feed flow rate 1 – 1.8 L/min, pH 3 and 10
Brix (apple juice). After performing ultrafiltration under these
conditions, the concentration of polyphenolics in retentate was
increased by a factor of up to 2.7 with up to 70% recovered in the
permeate and with approx. 20% of the sugar in that stream..
Application of diafiltration (addition of water to the concentrate) can
regain the flux by a factor of 1.5, which has been decreased due to
fouling. The material balance performed on the process has shown
the amount of deposits on the membrane and the extent of fouling in
the system. In conclusion, ultrafiltration has been demonstrated as a
potential technology to separate the polyphenolics and sugars from
their mixtures and can be applied to remove sugars from fruit juice.
Abstract: This paper describes a new method for affine parameter
estimation between image sequences. Usually, the parameter
estimation techniques can be done by least squares in a quadratic
way. However, this technique can be sensitive to the presence
of outliers. Therefore, parameter estimation techniques for various
image processing applications are robust enough to withstand the
influence of outliers. Progressively, some robust estimation functions
demanding non-quadratic and perhaps non-convex potentials adopted
from statistics literature have been used for solving these. Addressing
the optimization of the error function in a factual framework for
finding a global optimal solution, the minimization can begin with
the convex estimator at the coarser level and gradually introduce nonconvexity
i.e., from soft to hard redescending non-convex estimators
when the iteration reaches finer level of multiresolution pyramid.
Comparison has been made to find the performance of the results
of proposed method with the results found individually using two
different estimators.
Abstract: Wireless LAN technologies have picked up
momentum in the recent years due to their ease of deployment, cost
and availability. The era of wireless LAN has also given rise to
unique applications like VOIP, IPTV and unified messaging.
However, these real-time applications are very sensitive to network
and handoff latencies. To successfully support these applications,
seamless roaming during the movement of mobile station has become
crucial. Nowadays, centralized architecture models support roaming
in WLANs. They have the ability to manage, control and
troubleshoot large scale WLAN deployments. This model is managed
by Control and Provision of Wireless Access Point protocol
(CAPWAP). This paper covers the CAPWAP architectural solution
along with its proposals that have emerged. Based on the literature
survey conducted in this paper, we found that the proposed
algorithms to reduce roaming latency in CAPWAP architecture do
not support seamless roaming. Additionally, they are not sufficient
during the initial period of the network. This paper also suggests
important design consideration for mobility support in future
centralized IEEE 802.11 networks.
Abstract: In this paper, a joint source-channel coding (JSCC) scheme for time-varying channels is presented. The proposed scheme uses hierarchical framework for both source encoder and transmission via QAM modulation. Hierarchical joint source channel codes with hierarchical QAM constellations are designed to track the channel variations which yields to a higher throughput by adapting certain parameters of the receiver to the channel variation. We consider the problem of still image transmission over time-varying channels with channel state information (CSI) available at 1) receiver only and 2) both transmitter and receiver being informed about the state of the channel. We describe an algorithm that optimizes hierarchical source codebooks by minimizing the distortion due to source quantizer and channel impairments. Simulation results, based on image representation, show that, the proposed hierarchical system outperforms the conventional schemes based on a single-modulator and channel optimized source coding.
Abstract: This article presents a computationally tractable probabilistic model for the relation between the complex wavelet coefficients of two images of the same scene. The two images are acquisitioned at distinct moments of times, or from distinct viewpoints, or by distinct sensors. By means of the introduced probabilistic model, we argue that the similarity between the two images is controlled not by the values of the wavelet coefficients, which can be altered by many factors, but by the nature of the wavelet coefficients, that we model with the help of hidden state variables. We integrate this probabilistic framework in the construction of a new image registration algorithm. This algorithm has sub-pixel accuracy and is robust to noise and to other variations like local illumination changes. We present the performance of our algorithm on various image types.
Abstract: An appropriate project delivery system (PDS) is crucial
to the success of a construction projects. Case-based Reasoning (CBR)
is a useful support for PDS selection. However, the traditional CBR
approach represents cases as attribute-value vectors without taking
relations among attributes into consideration, and could not calculate
the similarity when the structures of cases are not strictly same.
Therefore, this paper solves this problem by adopting the Relational
Case-based Reasoning (RCBR) approach for PDS selection,
considering both the structural similarity and feature similarity. To
develop the feature terms of the construction projects, the criteria and
factors governing PDS selection process are first identified. Then
feature terms for the construction projects are developed. Finally, the
mechanism of similarity calculation and a case study indicate how
RCBR works for PDS selection. The adoption of RCBR in PDS
selection expands the scope of application of traditional CBR method
and improves the accuracy of the PDS selection system.
Abstract: Feature selection has recently been the subject of intensive research in data mining, specially for datasets with a large number of attributes. Recent work has shown that feature selection can have a positive effect on the performance of machine learning algorithms. The success of many learning algorithms in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. In this paper, a novel feature search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results.
Abstract: This paper presents a new strategy of identification
and classification of pathological voices using the hybrid method
based on wavelet transform and neural networks. After speech
acquisition from a patient, the speech signal is analysed in order to
extract the acoustic parameters such as the pitch, the formants, Jitter,
and shimmer. Obtained results will be compared to those normal and
standard values thanks to a programmable database. Sounds are
collected from normal people and patients, and then classified into
two different categories. Speech data base is consists of several
pathological and normal voices collected from the national hospital
“Rabta-Tunis". Speech processing algorithm is conducted in a
supervised mode for discrimination of normal and pathology voices
and then for classification between neural and vocal pathologies
(Parkinson, Alzheimer, laryngeal, dyslexia...). Several simulation
results will be presented in function of the disease and will be
compared with the clinical diagnosis in order to have an objective
evaluation of the developed tool.
Abstract: The purpose of this work is to present a method for
rigid registration of medical images using 1D binary projections
when a part of one of the two images is missing. We use 1D binary
projections and we adjust the projection limits according to the
reduced image in order to perform accurate registration. We use the
variance of the weighted ratio as a registration function which we
have shown is able to register 2D and 3D images more accurately and
robustly than mutual information methods. The function is computed
explicitly for n=5 Chebyshev points in a [-9,+9] interval and it is
approximated using Chebyshev polynomials for all other points. The
images used are MR scans of the head. We find that the method is
able to register the two images with average accuracy 0.3degrees for
rotations and 0.2 pixels for translations for a y dimension of 156 with
initial dimension 256. For y dimension 128/256 the accuracy
decreases to 0.7 degrees for rotations and 0.6 pixels for translations.
Abstract: This paper introduces a low cost INS/GPS algorithm for
land vehicle navigation application. The data fusion process is done
with an extended Kalman filter in cascade configuration mode. In
order to perform numerical simulations, MATLAB software has been
developed. Loosely coupled configuration is considered. The results
obtained in this work demonstrate that a low-cost INS/GPS navigation
system is partially capable of meeting the performance requirements
for land vehicle navigation. The relative effectiveness of the kalman
filter implementation in integrated GPS/INS navigation algorithm is
highlighted. The paper also provides experimental results; field test
using a car is carried out.
Abstract: I/O workload is a critical and important factor to
analyze I/O pattern and file system performance. However tracing I/O
operations on the fly distributed parallel file system is non-trivial due
to collection overhead and a large volume of data. In this paper, we
design and implement a parallel file system logging method for high
performance computing using shared memory-based multi-layer
scheme. It minimizes the overhead with reduced logging operation
response time and provides efficient post-processing scheme through
shared memory. Separated logging server can collect sequential logs
from multiple clients in a cluster through packet communication.
Implementation and evaluation result shows low overhead and high
scalability of this architecture for high performance parallel logging
analysis.