Abstract: Based on Rayleigh beam theory, the sub-impacts of a
free-free beam struck horizontally by a round-nosed rigid mass is
simulated by the finite difference method and the impact-separation
conditions. In order to obtain the sub-impact force, a uniaxial
compression elastic-plastic contact model is employed to analyze the
local deformation field on contact zone. It is found that the horizontal
impact is a complicated process including the elastic plastic
sub-impacts in sequence. There are two sub-zones of sub-impact. In
addition, it found that the elastic energy of the free-free beam is more
suitable for the Poisson collision hypothesis to explain compression
and recovery processes.
Abstract: This frame work describes a computationally more
efficient and adaptive threshold estimation method for image
denoising in the wavelet domain based on Generalized Gaussian
Distribution (GGD) modeling of subband coefficients. In this
proposed method, the choice of the threshold estimation is carried out
by analysing the statistical parameters of the wavelet subband
coefficients like standard deviation, arithmetic mean and geometrical
mean. The noisy image is first decomposed into many levels to
obtain different frequency bands. Then soft thresholding method is
used to remove the noisy coefficients, by fixing the optimum
thresholding value by the proposed method. Experimental results on
several test images by using this method show that this method yields
significantly superior image quality and better Peak Signal to Noise
Ratio (PSNR). Here, to prove the efficiency of this method in image
denoising, we have compared this with various denoising methods
like wiener filter, Average filter, VisuShrink and BayesShrink.
Abstract: This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation.
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: In the present work we report a gram negative
bacterial isolate, from soil of a dye industry, with promising
biorefining and bioremediation potential. This isolate (GBS.5) could
utilize carbazole (nitrogen containing polycyclic aromatic
hydrocarbon) as the sole source of nitrogen and carbon and utilize
almost 98% of 3mM carbazole in 100 hours. The specific activity of
our GBS.5 isolate for carbazole degradation at 30°C and pH 7.0 was
found to be 11.36 μmol/min/g dry cell weight as compared to 10.4
μmol/min/g dry cell weight, the highest reported specific activity till
date. The presence of car genes (the genes involved in
denitrogenation of carbazole) was confirmed through PCR
amplification.
Abstract: This paper is prepared to provide a review of how an automotive manufacturer, ISUZU HICOM Malaysia Co. Ltd. sustained the supply chain management after business process reengineering in 2007. One of the authors is currently undergoing industrial attachment and has spent almost 6 months researching in the production and operation management system of the company. This study was carried out as part of the tasks in the attachment program. The result shows that delivery lateness and outsourcing are the main barriers that affected productivity. From the gap analysis, the authors found that new business process operation had improved suppliers delivery performance.
Abstract: In turning hardened steel, polycrystalline cubic boron
nitride (cBN) compacts are widely used, due to their higher hardness
and higher thermal conductivity. However, in milling hardened steel,
fracture of cBN cutting tools readily occurs because they have poor
fracture toughness. Therefore, coated cemented carbide tools, which
have good fracture toughness and wear resistance, are generally
widely used. In this study, hardened steel (ASTM D2, JIS SKD11,
60HRC) was milled with three physical vapor deposition
(PVD)-coated cemented carbide end mill cutters in order to determine
effective tool materials for cutting hardened steel at high cutting
speeds. The coating films used were (Ti,W)N/(Ti,W,Si)N and
(Ti,W)N/(Ti,W,Si,Al)N coating films. (Ti,W,Si,Al)N is a new type of
coating film. The inner layer of the (Ti,W)N/(Ti,W,Si)N and
(Ti,W)N/(Ti,W,Si,Al)N coating system is (Ti,W)N coating film, and
the outer layer is (Ti,W,Si)N and (Ti,W,Si,Al)N coating films,
respectively. Furthermore, commercial (Ti,Al)N-based coating film
was also used. The following results were obtained: (1) In milling
hardened steel at a cutting speed of 3.33 m/s, the tool wear width of the
(Ti,W)N/(Ti,W,Si,Al)N-coated tool was smaller than that of the
(Ti,W)N/(Ti,W,Si)N-coated tool. And, compared with the commercial
(Ti,Al)N, the tool wear width of the (Ti,W)N/(Ti,W,Si,Al)N-coated
tool was smaller than that of the (Ti,Al)N-coated tool. (2) The tool
wear of the (Ti,W)N/(Ti,W,Si,Al)N-coated tool increased with an
increase in cutting speed. (3) The (Ti,W)N/(Ti,W,Si,Al)N-coated
cemented carbide was an effective tool material for high-speed cutting
below a cutting speed of 3.33 m/s.
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: 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: Planning the transition period for the adoption of
alternative fuel-technology powertrains is a challenging task that
requires sophisticated analysis tools. In this study, a system dynamic
approach was applied to analyze the bi-directional interaction
between the development of the refueling station network and vehicle
sales. Besides, the developed model was used to estimate the
transition cost to reach a predefined target (share of alternative fuel
vehicles) in different scenarios. Several scenarios have been analyzed
to investigate the effectiveness and cost of incentives on the initial
price of vehicles, and on the evolution of fuel and refueling stations.
Obtained results show that a combined set of incentives will be more
effective than just a single specific type of incentives.
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: For numerical prediction of the NOX in the exhaust of
a compression ignition engine a model was developed by considering
the parameter equivalence ratio. This model was validated by
comparing the predicted results of NOX with experimental ones. The
ultimate aim of the work was to access the applicability, robustness
and performance of the improved NOX model against other NOX
models.
Abstract: The size, complexity and number of databases used
for protein information have caused bioinformatics to lag behind in
adapting to the need to handle this distributed information.
Integrating all the information from different databases into one
database is a challenging problem. Our main research is to develop a
tool which can be used to access and manipulate protein information
from difference databases. In our approach, we have integrated
difference databases such as Swiss-prot, PDB, Interpro, and EMBL
and transformed these databases in flat file format into relational
form using XML and Bioperl. As a result, we showed this tool can
search different sizes of protein information stored in relational
database and the result can be retrieved faster compared to flat file
database. A web based user interface is provided to allow user to
access or search for protein information in the local database.
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.