Abstract: In this paper, we consider the problem of logic simplification for a special class of logic functions, namely complementary Boolean functions (CBF), targeting low power implementation using static CMOS logic style. The functions are uniquely characterized by the presence of terms, where for a canonical binary 2-tuple, D(mj) ∪ D(mk) = { } and therefore, we have | D(mj) ∪ D(mk) | = 0 [19]. Similarly, D(Mj) ∪ D(Mk) = { } and hence | D(Mj) ∪ D(Mk) | = 0. Here, 'mk' and 'Mk' represent a minterm and maxterm respectively. We compare the circuits minimized with our proposed method with those corresponding to factored Reed-Muller (f-RM) form, factored Pseudo Kronecker Reed-Muller (f-PKRM) form, and factored Generalized Reed-Muller (f-GRM) form. We have opted for algebraic factorization of the Reed-Muller (RM) form and its different variants, using the factorization rules of [1], as it is simple and requires much less CPU execution time compared to Boolean factorization operations. This technique has enabled us to greatly reduce the literal count as well as the gate count needed for such RM realizations, which are generally prone to consuming more cells and subsequently more power consumption. However, this leads to a drawback in terms of the design-for-test attribute associated with the various RM forms. Though we still preserve the definition of those forms viz. realizing such functionality with only select types of logic gates (AND gate and XOR gate), the structural integrity of the logic levels is not preserved. This would consequently alter the testability properties of such circuits i.e. it may increase/decrease/maintain the same number of test input vectors needed for their exhaustive testability, subsequently affecting their generalized test vector computation. We do not consider the issue of design-for-testability here, but, instead focus on the power consumption of the final logic implementation, after realization with a conventional CMOS process technology (0.35 micron TSMC process). The quality of the resulting circuits evaluated on the basis of an established cost metric viz., power consumption, demonstrate average savings by 26.79% for the samples considered in this work, besides reduction in number of gates and input literals by 39.66% and 12.98% respectively, in comparison with other factored RM forms.
Abstract: Selection of the best possible set of suppliers has a
significant impact on the overall profitability and success of any
business. For this reason, it is usually necessary to optimize all
business processes and to make use of cost-effective alternatives for
additional savings. This paper proposes a new efficient context-aware
supplier selection model that takes into account possible changes of
the environment while significantly reducing selection costs. The
proposed model is based on data clustering techniques while
inspiring certain principles of online algorithms for an optimally
selection of suppliers. Unlike common selection models which re-run
the selection algorithm from the scratch-line for any decision-making
sub-period on the whole environment, our model considers the
changes only and superimposes it to the previously defined best set
of suppliers to obtain a new best set of suppliers. Therefore, any recomputation
of unchanged elements of the environment is avoided
and selection costs are consequently reduced significantly. A
numerical evaluation confirms applicability of this model and proves
that it is a more optimal solution compared with common static
selection models in this field.
Abstract: Predicting short term wind speed is essential in order
to prevent systems in-action from the effects of strong winds. It also
helps in using wind energy as an alternative source of energy, mainly
for Electrical power generation. Wind speed prediction has
applications in Military and civilian fields for air traffic control,
rocket launch, ship navigation etc. The wind speed in near future
depends on the values of other meteorological variables, such as
atmospheric pressure, moisture content, humidity, rainfall etc. The
values of these parameters are obtained from a nearest weather
station and are used to train various forms of neural networks. The
trained model of neural networks is validated using a similar set of
data. The model is then used to predict the wind speed, using the
same meteorological information. This paper reports an Artificial
Neural Network model for short term wind speed prediction, which
uses back propagation algorithm.
Abstract: This policy participation action research explores the
roles of Thai government units during its 2010 fiscal year on how to
create value added to recycling business in the central part of
Thailand. The research aims to a) study how the government plays a
role to support the business, and its problems and obstacles on
supporting the business, b) to design a strategic action – short,
medium, and long term plans -- to create value added to the recycling
business, particularly in local full-loop companies/organizations
licensed by Wongpanit Waste Separation Plant as well as those
licensed by the Department of Provincial Administration. Mixed
method research design, i.e., a combination of quantitative and
qualitative methods is utilized in the present study in both data
collection and analysis procedures. Quantitative data was analyzed
by frequency, percent value, mean scores, and standard deviation,
and aimed to note trend and generalizations. Qualitative data was
collected via semi-structured interviews/focus group interviews to
explore in-depth views of the operators. The sampling included 1,079
operators in eight provinces in the central part of Thailand.
Abstract: Evaluation and survey of curriculum quality as one of the most important components of universities system is necessary for different levels in higher education. The main purpose of this study was to survey of the curriculum quality of Actuarial science field. Case: University of SHahid Beheshti and Higher education institute of Eco insurance (according to viewpoint of students, alumni, employers and faculty members). Descriptive statistics (mean, tables, percentage, and frequency distribution) and inferential statistics (CHI SQUARE) were used to analyze the data. Six criteria considered for the Quality of curriculum: objectives, content, teaching and learning methods, space and facilities, Time, assessment of learning. Content, teaching and learning methods, space and facilities, assessment of learning criteria were relatively desirable level, objectives and time criterions were desirable level. The quality of curriculum of Actuarial Science field was relatively desirable level.
Abstract: Pretreatment of oil palm empty fruit bunch (OPEFB) with N-Methylmorpholine-N-oxide (NMMO) to enhance biogas production was investigated. The pretreatments were performed at 90 and 120ºC for 1, 3, and 5 h using three different concentrations of NMMO of 73%, 79%, and 85%. The pretreated OPEFB was subsequently anaerobically digested to produce biogas. After pretreatment, there were no significant changes of the main composition of OPEFB and the maximum total solid recovery was 92%. The amorphous phase was increased up to 78% at pretreatment condition using 85% NMMO solution for 3 h at 120oC. In general, higher concentration of NMMO and higher temperature resulted in increased amorphous form and higher biogas production. The best results of biogas production reached enhancement of methane yield of 148% compared to the untreated OPEFB and increased in digestion of 94% compared to starch as reference.
Abstract: Spatial trends are one of the valuable patterns in geo
databases. They play an important role in data analysis and
knowledge discovery from spatial data. A spatial trend is a regular
change of one or more non spatial attributes when spatially moving
away from a start object. Spatial trend detection is a graph search
problem therefore heuristic methods can be good solution. Artificial
immune system (AIS) is a special method for searching and
optimizing. AIS is a novel evolutionary paradigm inspired by the
biological immune system. The models based on immune system
principles, such as the clonal selection theory, the immune network
model or the negative selection algorithm, have been finding
increasing applications in fields of science and engineering.
In this paper, we develop a novel immunological algorithm based
on clonal selection algorithm (CSA) for spatial trend detection. We
are created neighborhood graph and neighborhood path, then select
spatial trends that their affinity is high for antibody. In an
evolutionary process with artificial immune algorithm, affinity of
low trends is increased with mutation until stop condition is satisfied.
Abstract: This study determines the effect of naked and heparinbased
super-paramagnetic iron oxide nanoparticles on the human
cancer cell lines of A2780. Doxorubicin was used as the anticancer
drug, entrapped in the SPIO-NPs. This study aimed to decorate
nanoparticles with heparin, a molecular ligand for 'active' targeting
of cancerous cells and the application of modified-nanoparticles in
cancer treatment. The nanoparticles containing the anticancer drug
DOX were prepared by a solvent evaporation and emulsification
cross-linking method. The physicochemical properties of the
nanoparticles were characterized by various techniques, and uniform
nanoparticles with an average particle size of 110±15 nm with high
encapsulation efficiencies (EE) were obtained. Additionally, a
sustained release of DOX from the SPIO-NPs was successful.
Cytotoxicity tests showed that the SPIO-DOX-HP had higher cell
toxicity than the individual HP and confocal microscopy analysis
confirmed excellent cellular uptake efficiency. These results indicate
that HP based SPIO-NPs have potential uses as anticancer drug
carriers and also have an enhanced anticancer effect.
Abstract: In this paper by measuring the cutting forces the effect
of the tool shape and qualifications (sharp and worn cutting tools of
both vee and knife edge profile) and cutting conditions (depth of cut
and cutting speed) in the turning operation on the tool deflection and
cutting force is investigated. The workpiece material was mild steel
and the cutting tool was made of high speed steel. Cutting forces
were measured by a dynamometer (type P.E.I. serial No 154). The
dynamometer essentially consisted of a cantilever structure which
held the cutting tool. Deflection of the cantilever was measured by an
L.V.D.T (Mercer 122) deflection indicator. No cutting fluid was used
during the turning operations. A modern CNC lathe machine (Okuma
LH35-N) was used for the tests. It was noted that worn vee profile
tools tended to produce a greater increase in the vertical force
component than the axial component, whereas knife tools tended to
show a more pronounced increase in the axial component.
Abstract: The Niger Delta Region of Nigeria is home to about
20 million people and 40 different ethnic groups. The region has an
area of seventy thousand square kilometers (70,000 KM2) of
wetlands, formed primarily by sediments deposition and makes up
7.5 percent of Nigeria's total landmass. The notable ecological zones
in this region includes: coastal barrier islands; mangrove swamp
forests; fresh water swamps; and lowland rainforests. This incredibly
naturally-endowed ecosystem region, which contains one of the
highest concentrations of biodiversity on the planet, in addition to
supporting abundant flora and fauna, is threatened by the inhuman act
known as gas flaring. Gas flaring is the combustion of natural gas
that is associated with crude oil when it is pumped up from the
ground. In petroleum-producing areas such as the Niger Delta region
of Nigeria where insufficient investment was made in infrastructure
to utilize natural gas, flaring is employed to dispose of this associated
gas. This practice has impoverished the communities where it is
practiced, with attendant environmental, economic and health
challenges. This paper discusses the adverse environmental and
health implication associated with the practice, the role of
Government, Policy makers, Oil companies and the Local
communities aimed at bring this inhuman practice to a prompt end.
Abstract: In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STLF systems was used. Our study based on MLP was trained and tested using three years (2004-2006) data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STLF systems.
Abstract: In this research it is aimed that the effect of some demographic factors on Turkish Adolescents' subjective well being is investigated. 432 adolescents who are 247 girls and 185 boys are participated in this study. They are ages 15-17, and also are high school students. The Positive and Negative Affect Scale and Life Satisfaction Scale are used for measuring adolescents' subjective well being. The ANOVA method is used in order to examine the effect of ages. For gender differences, independent t-test method is used, and finally the Pearson Correlation method is used so as to examine the effect of socio economic statues of adolescents' parents. According to results, there is no gender difference on adolescents' subjective well being. On the other hand, SES and age are effect significantly lover level on adolescents' subjective well being.
Abstract: Structural behavior of ring stiffened thick walled
cylinders made of functionally graded materials (FGMs) is
investigated in this paper. Functionally graded materials are inhomogeneous composites which are usually made from a mixture
of metal and ceramic. The gradient compositional variation of the
constituents from one surface to the other provides an elegant solution to the problem of high transverse shear stresses that are
induced when two dissimilar materials with large differences in material properties are bonded. FGM formation of the cylinder is
modeled by power-law exponent and the variation of characteristics is supposed to be in radial direction.
A finite element formulation is derived for the analysis. According to the property variation of the constituent materials in the radial
direction of the wall, it is not convenient to use conventional elements to model and analyze the structure of the stiffened FGM
cylinders. In this paper a new cylindrical super-element is used to model the finite element formulation and analyze the static and
modal behavior of stiffened FGM thick walled cylinders. By using
this super-element the number of elements, which are needed for
modeling, will reduce significantly and the process time is less in comparison with conventional finite element formulations. Results for static and modal analysis are evaluated and verified by
comparison to finite element formulation with conventional
elements. Comparison indicates a good conformity between results.
Abstract: An optimal power flow (OPF) based on particle swarm
optimization (PSO) was developed with more realistic generator
security constraint using the capability curve instead of only Pmin/Pmax
and Qmin/Qmax. Neural network (NN) was used in designing digital
capability curve and the security check algorithm. The algorithm is
very simple and flexible especially for representing non linear
generation operation limit near steady state stability limit and under
excitation operation area. In effort to avoid local optimal power flow
solution, the particle swarm optimization was implemented with
enough widespread initial population. The objective function used in
the optimization process is electric production cost which is
dominated by fuel cost. The proposed method was implemented at
Java Bali 500 kV power systems contain of 7 generators and 20
buses. The simulation result shows that the combination of generator
power output resulted from the proposed method was more economic
compared with the result using conventional constraint but operated
at more marginal operating point.
Abstract: Inverse kinematics analysis plays an important role in developing a robot manipulator. But it is not too easy to derive the inverse kinematic equation of a robot manipulator especially robot manipulator which has numerous degree of freedom. This paper describes an application of Artificial Neural Network for modeling the inverse kinematics equation of a robot manipulator. In this case, the robot has three degree of freedoms and the robot was implemented for drilling a printed circuit board. The artificial neural network architecture used for modeling is a multilayer perceptron networks with steepest descent backpropagation training algorithm. The designed artificial neural network has 2 inputs, 2 outputs and varies in number of hidden layer. Experiments were done in variation of number of hidden layer and learning rate. Experimental results show that the best architecture of artificial neural network used for modeling inverse kinematics of is multilayer perceptron with 1 hidden layer and 38 neurons per hidden layer. This network resulted a RMSE value of 0.01474.
Abstract: This paper features the modeling and design of a Fast
Output Sampling (FOS) Feedback control technique for the Active
Vibration Control (AVC) of a smart flexible aluminium cantilever
beam for a Single Input Single Output (SISO) case. Controllers are
designed for the beam by bonding patches of piezoelectric layer as
sensor / actuator to the master structure at different locations along
the length of the beam by retaining the first 2 dominant vibratory
modes. The entire structure is modeled in state space form using the
concept of piezoelectric theory, Euler-Bernoulli beam theory, Finite
Element Method (FEM) and the state space techniques by dividing
the structure into 3, 4, 5 finite elements, thus giving rise to three
types of systems, viz., system 1 (beam divided into 3 finite
elements), system 2 (4 finite elements), system 3 (5 finite elements).
The effect of placing the sensor / actuator at various locations along
the length of the beam for all the 3 types of systems considered is
observed and the conclusions are drawn for the best performance and
for the smallest magnitude of the control input required to control the
vibrations of the beam. Simulations are performed in MATLAB. The
open loop responses, closed loop responses and the tip displacements
with and without the controller are obtained and the performance of
the proposed smart system is evaluated for vibration control.
Abstract: A general stochastic spatial MIMO channel model is
proposed for evaluating various MIMO techniques in this paper. It can
generate MIMO channels complying with various MIMO
configurations such as smart antenna, spatial diversity and spatial
multiplexing. The modeling method produces the stochastic fading
involving delay spread, Doppler spread, DOA (direction of arrival),
AS (angle spread), PAS (power azimuth Spectrum) of the scatterers,
antenna spacing and the wavelength. It can be applied in various
MIMO technique researches flexibly with low computing complexity.
Abstract: This study aimed to detect and to identify the main
strains of airborne microorganisms present in the Shanghai Metro
system. Samples were collected using agar plates exposed to the air
and microorganisms were identified using catalase, plasma coagulase
and hymolytic analysis. The results show that the concentration of
mildew present within a newly opened metro line was significantly
higher than for other lines. Differences among underground and
elevated stations can be attributed to differences in passenger flow and
the environment surrounding the stations. Additionally, the
investigation indicated that bacteria reached maximum levels at
different times on weekdays and weekends. The bacteria in the Metro
stations were identified as primarily Gram positive, consisting mainly
of coagulase-negative staphylococcus strains (CNS).
Abstract: In India, the quarrel between the budding human
populace and the planet-s unchanging supply of freshwater and
falling water tables has strained attention the reuse of gray water as
an alternative water resource in rural development. This paper
present the finest design of laboratory scale gray water treatment
plant, which is a combination of natural and physical operations such
as primary settling with cascaded water flow, aeration, agitation and
filtration, hence called as hybrid treatment process. The economical
performance of the plant for treatment of bathrooms, basins and
laundries gray water showed in terms of deduction competency of
water pollutants such as COD (83%), TDS (70%), TSS (83%), total
hardness (50%), oil and grease (97%), anions (46%) and cations
(49%). Hence, this technology could be a good alternative to treat
gray water in residential rural area.
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.