Abstract: DNA shuffling is a powerful method used for in vitro
evolute molecules with specific functions and has application in areas
such as, for example, pharmaceutical, medical and agricultural
research. The success of such experiments is dependent on a variety
of parameters and conditions that, sometimes, can not be properly
pre-established. Here, two computational models predicting DNA
shuffling results is presented and their use and results are evaluated
against an empirical experiment. The in silico and in vitro results
show agreement indicating the importance of these two models and
motivating the study and development of new models.
Abstract: This paper applies Bayesian Networks to support
information extraction from unstructured, ungrammatical, and
incoherent data sources for semantic annotation. A tool has been
developed that combines ontologies, machine learning, and
information extraction and probabilistic reasoning techniques to
support the extraction process. Data acquisition is performed with the
aid of knowledge specified in the form of ontology. Due to the
variable size of information available on different data sources, it is
often the case that the extracted data contains missing values for
certain variables of interest. It is desirable in such situations to
predict the missing values. The methodology, presented in this paper,
first learns a Bayesian network from the training data and then uses it
to predict missing data and to resolve conflicts. Experiments have
been conducted to analyze the performance of the presented
methodology. The results look promising as the methodology
achieves high degree of precision and recall for information
extraction and reasonably good accuracy for predicting missing
values.
Abstract: Load forecasting has always been the essential part of
an efficient power system operation and planning. A novel approach
based on support vector machines is proposed in this paper for annual
power load forecasting. Different kernel functions are selected to
construct a combinatorial algorithm. The performance of the new
model is evaluated with a real-world dataset, and compared with two
neural networks and some traditional forecasting techniques. The
results show that the proposed method exhibits superior performance.
Abstract: The term hybrid composite refers to the composite
containing more than one type of fiber material as reinforcing fillers.
It has become attractive structural material due to the ability of
providing better combination of properties with respect to single fiber
containing composite. The eco-friendly nature as well as processing
advantage, light weight and low cost have enhanced the attraction
and interest of natural fiber reinforced composite. The objective of
present research is to study the mechanical properties of jute-coir
fiber reinforced hybrid polypropylene (PP) composite according to
filler loading variation. In the present work composites were
manufactured by using hot press machine at four levels of fiber
loading (5, 10, 15 and 20 wt %). Jute and coir fibers were utilized at a
ratio of (1:1) during composite manufacturing. Tensile, flexural,
impact and hardness tests were conducted for mechanical
characterization. Tensile test of composite showed a decreasing trend
of tensile strength and increasing trend of the Young-s modulus with
increasing fiber content. During flexural, impact and hardness tests,
the flexural strength, flexural modulus, impact strength and hardness
were found to be increased with increasing fiber loading. Based on
the fiber loading used in this study, 20% fiber reinforced composite
resulted the best set of mechanical properties.
Abstract: One problem of synthetic sunflower cultivation is an
erratic germination of the seeds. To improve the germination, presowing
seed treatment with an ultrasound was tested. All treatments
were carried out at 40 kHz frequency with the intensities of 40, 60,
80 and 100% of the ultrasonic generator total power (250 W) for the
durations of 5, 10, 15 and 20 minutes. Data on seed germination
percentage, seed vigor index (SVI), root and shoot lengths of
seedlings were collected. The results showed that germination, SVI,
root and shoot lengths of ultrasonic treated seedlings were different
from the control, depending on intensity of the ultrasound. The
effects of ultrasonic treatment were significant on germination,
resulting in a maximum increase of 43% at 40 and 60% intensities
compared to that of the control seeds. In addition, seedlings of these 2
treatments had higher SVI and longer root and shoot lengths than that
of the control seedlings. All treatment durations resulted in higher
germination and SVI, longer root and higher shoot lenghts of
seedlings than the control. Among the duration treatments, only SVI
and seedling root length were significantly different.
Abstract: Evolutionary Algorithms are population-based,
stochastic search techniques, widely used as efficient global
optimizers. However, many real life optimization problems often
require finding optimal solution to complex high dimensional,
multimodal problems involving computationally very expensive
fitness function evaluations. Use of evolutionary algorithms in such
problem domains is thus practically prohibitive. An attractive
alternative is to build meta models or use an approximation of the
actual fitness functions to be evaluated. These meta models are order
of magnitude cheaper to evaluate compared to the actual function
evaluation. Many regression and interpolation tools are available to
build such meta models. This paper briefly discusses the
architectures and use of such meta-modeling tools in an evolutionary
optimization context. We further present two evolutionary algorithm
frameworks which involve use of meta models for fitness function
evaluation. The first framework, namely the Dynamic Approximate
Fitness based Hybrid EA (DAFHEA) model [14] reduces
computation time by controlled use of meta-models (in this case
approximate model generated by Support Vector Machine
regression) to partially replace the actual function evaluation by
approximate function evaluation. However, the underlying
assumption in DAFHEA is that the training samples for the metamodel
are generated from a single uniform model. This does not take
into account uncertain scenarios involving noisy fitness functions.
The second model, DAFHEA-II, an enhanced version of the original
DAFHEA framework, incorporates a multiple-model based learning
approach for the support vector machine approximator to handle
noisy functions [15]. Empirical results obtained by evaluating the
frameworks using several benchmark functions demonstrate their
efficiency
Abstract: In this paper, a particle swarm optimization (PSO)
algorithm is proposed to solve machine loading problem in flexible
manufacturing system (FMS), with bicriterion objectives of
minimizing system unbalance and maximizing system throughput in
the occurrence of technological constraints such as available
machining time and tool slots. A mathematical model is used to
select machines, assign operations and the required tools. The
performance of the PSO is tested by using 10 sample dataset and the
results are compared with the heuristics reported in the literature. The
results support that the proposed PSO is comparable with the
algorithms reported in the literature.
Abstract: In this research, the diabetes conditions of people (healthy, prediabete and diabete) were tried to be identified with noninvasive palm perspiration measurements. Data clusters gathered from 200 subjects were used (1.Individual Attributes Cluster and 2. Palm Perspiration Attributes Cluster). To decrase the dimensions of these data clusters, Principal Component Analysis Method was used. Data clusters, prepared in that way, were classified with Support Vector Machines. Classifications with highest success were 82% for Glucose parameters and 84% for HbA1c parametres.
Abstract: Innovation, technology and knowledge are the trilogy
of impact to support the challenges arising from uncertainty.
Evidence showed an opportunity to ask how to manage in this
environment under constant innovation. In an attempt to get a
response from the field of Management Sciences, based in the
Contingency Theory, a research was conducted, with
phenomenological and descriptive approaches, using the Case Study
Method and the usual procedures for this task involving a focus
group composed of managers and employees working in the
pharmaceutical field. The problem situation was raised; the state of
the art was interpreted and dissected the facts. In this tasks were
involved four establishments. The result indicates that these focused
ventures have been managed by its founder empirically and is
experimenting agility described in this work. The expectation of this
study is to improve concepts for stakeholders on creativity in
business.
Abstract: Energy efficient protocol design is the aim of current
researches in the area of sensor networks where limited power
resources impose energy conservation considerations. In this paper
we care for Medium Access Control (MAC) protocols and after an
extensive literature review, two adaptive schemes are discussed. Of
them, adaptive-rate MACs which were introduced for throughput
enhancement show the potency to save energy, even more than
adaptive-power schemes. Then we propose an allocation algorithm
for getting accurate and reliable results. Through a simulation study
we validated our claim and showed the power saving of adaptive-rate
protocols.
Abstract: This paper provides an analysis of corporate income
tax (CIT) incentives in the Western Balkan countries: Slovenia,
Croatia, Serbia, Montenegro, Macedonia and Albania. Western
Balkan countries, as other transition and developing countries, use
large number of the corporate income tax incentives (CIT) to attract
foreign investments and to stimulate economic activity. The main
goal of this paper is to investigate how often these countries use CIT
incentives and provide review of existing tax incentives in Western
Balkan countries. Paper will focus on reduced CIT rates, tax
holidays, and other investment incentives which imply incentives
like accelerated depreciation, tax allowances and tax credits.
Abstract: The stochastic nature of tool life using conventional discrete-wear data from experimental tests usually exists due to many individual and interacting parameters. It is a common practice in batch production to continually use the same tool to machine different parts, using disparate machining parameters. In such an environment, the optimal points at which tools have to be changed, while achieving minimum production cost and maximum production rate within the surface roughness specifications, have not been adequately studied. In the current study, two relevant aspects are investigated using coated and uncoated inserts in turning operations: (i) the accuracy of using machinability information, from fixed parameters testing procedures, when variable parameters situations are emerged, and (ii) the credibility of tool life machinability data from prior discrete testing procedures in a non-stop machining. A novel technique is proposed and verified to normalize the conventional fixed parameters machinability data to suit the cases when parameters have to be changed for the same tool. Also, an experimental investigation has been established to evaluate the error in the tool life assessment when machinability from discrete testing procedures is employed in uninterrupted practical machining.
Abstract: The paper presents a compressor anti-surge control
system, that results in maximizing compressor throughput with
pressure standard deviation reduction, increased safety margin
between design point and surge limit line and avoiding possible
machine surge. Alternative control strategies are presented.
Abstract: In this study, we experiment on precise control outlet
temperature of water from the water cooler with hot-gas bypass
method based on PI control logic for machine tool. Recently, technical
trend for machine tools is focused on enhancement of speed and
accuracy. High speedy processing causes thermal and structural
deformation of objects from the machine tools. Water cooler has to be
applied to machine tools to reduce the thermal negative influence with
accurate temperature controlling system. The goal of this study is to
minimize temperature error in steady state. In addition, control period
of an electronic expansion valve were considered to increment of
lifetime of the machine tools and quality of product with a water
cooler.
Abstract: In this paper, a new developed construction model of
the UPFC is proposed. The construction of this model consists of one
shunt compensation block and two series compensation blocks. In
this case, the UPFC with the new construction model will be
investigated when it is installed in multi-machine systems with nonlinear
load model. In addition, the steady–state performance of the
new model operating as impedance compensation will be presented
and compared with that obtained from the system without
compensation.
Abstract: The Improved Generalized Diversity Index (IGDI)
has been proposed as a tool that can be used to identify areas that
have high conservation value and measure the ecological condition of
an area. IGDI is based on the species relative abundances. This paper
is concerned with particular attention is given to comparisons
involving the MacArthur model of species abundances. The
properties and performance of various species indices were assessed.
Both IGDI and species richness increased with sampling area
according to a power function. IGDI were also found to be acceptable
ecological indicators of conditions and consistently outperformed
coefficient of conservatism indices.
Abstract: Cancer classification to their corresponding cohorts has been key area of research in bioinformatics aiming better prognosis of the disease. High dimensionality of gene data has been makes it a complex task and requires significance data identification technique in order to reducing the dimensionality and identification of significant information. In this paper, we have proposed a novel approach for classification of oral cancer into metastasis positive and negative patients. We have used significance analysis of microarrays (SAM) for identifying significant genes which constitutes gene signature. 3 different gene signatures were identified using SAM from 3 different combination of training datasets and their classification accuracy was calculated on corresponding testing datasets using k-Nearest Neighbour (kNN), Fuzzy C-Means Clustering (FCM), Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN). A final gene signature of only 9 genes was obtained from above 3 individual gene signatures. 9 gene signature-s classification capability was compared using same classifiers on same testing datasets. Results obtained from experimentation shows that 9 gene signature classified all samples in testing dataset accurately while individual genes could not classify all accurately.
Abstract: In this paper, an efficient structural approach for
recognizing on-line handwritten digits is proposed. After reading
the digit from the user, the slope is estimated and normalized for
adjacent nodes. Based on the changing of signs of the slope values,
the primitives are identified and extracted. The names of these
primitives are represented by strings, and then a finite state
machine, which contains the grammars of the digits, is traced to
identify the digit. Finally, if there is any ambiguity, it will be
resolved. Experiments showed that this technique is flexible and
can achieve high recognition accuracy for the shapes of the digits
represented in this work.
Abstract: Design of a fixed parameter robust STATCOM controller for a multi-machine power system through an H-? based loop-shaping procedure is presented. The trial and error part of the graphical loop-shaping procedure has been eliminated by embedding a particle swarm optimization (PSO) technique in the design loop. Robust controllers were designed considering the detailed dynamics of the multi-machine system and results were compared with reduced order models. The robust strategy employing loop-shaping and PSO algorithms was observed to provide very good damping profile for a wide range of operation and for various disturbance conditions.
Abstract: Fracture process in mechanically loaded steel fiber
reinforced high-strength (SFRHSC) concrete is characterized by
fibers bridging the crack providing resistance to its opening.
Structural SFRHSC fracture model was created; material fracture
process was modeled, based on single fiber pull-out laws, which were
determined experimentally (for straight fibers, fibers with end hooks
(Dramix), and corrugated fibers (Tabix)) as well as obtained
numerically ( using FEM simulations). For this purpose experimental
program was realized and pull-out force versus pull-out fiber length
was obtained (for fibers embedded into concrete at different depth
and under different angle). Model predictions were validated by
15x15x60cm prisms 4 point bending tests. Fracture surfaces analysis
was realized for broken prisms with the goal to improve elaborated
model assumptions. Optimal SFRHSC structures were recognized.