Abstract: Thermal conductivity is an important characteristic of
a nanofluid in laminar flow heat transfer. This paper presents an
improved model for the prediction of the effective thermal
conductivity of nanofluids based on dimensionless groups. The
model expresses the thermal conductivity of a nanofluid as a function
of the thermal conductivity of the solid and liquid, their volume
fractions and particle size. The proposed model includes a parameter
which accounts for the interfacial shell, brownian motion, and
aggregation of particle. The validation of the model is verified by
applying the results obtained by the experiments of Tio2-water and
Al2o3-water nanofluids.
Abstract: Fossil fuel-firing power plants dominate electric
power generation in Taiwan, which are also the major contributor to
Green House gases (GHG). CO2 is the most important greenhouse
gas that cause global warming. This paper penetrates the relationship
between carbon trading for GHG reduction and power generation
expansion planning (GEP) problem for the electrical utility. The
Particle Swarm Optimization (PSO) Algorithm is presented to deal
with the generation expansion planning strategy of the utility with
independent power providers (IPPs). The utility has to take both the
IPPs- participation and environment impact into account when a new
generation unit is considering expanded from view of supply side.
Abstract: A two-parameter fatigue model explicitly accounting for the cyclic as well as the mean stress was used to fit static and fatigue data available in literature concerning carbon fiber reinforced composite laminates subjected tension-tension fatigue. The model confirms the strength–life equal rank assumption and predicts reasonably the probability of failure under cyclic loading. The model parameters were found by best fitting procedures and required a minimum of experimental tests.
Abstract: The springs located in urban areas are the outpouring
of surface water, which can serve as water supply, effluent receptors
and important local macro-drainage elements. With unplanned
occupation, non-compliance with environmental legislation and the
importance of these water bodies, it is vital to analyze the springs
within urban areas, considering the Brazilian forest code. This paper
submits an analysis and discussion methodology proposal of
environmental compliance functions of urban springs, by means of
G.I.S. - Geographic Information System analysis - and in situ
analysis. The case study included two springs which exhibit a history
of occupation along its length, with different degrees of impact. The
proposed method is effective and easy to apply, representing a
powerful tool for analyzing the environmental conditions of springs
in urban areas.
Abstract: In this paper back-propagation artificial neural
network (BPANN) is employed to predict the limiting drawing ratio
(LDR) of the deep drawing process. To prepare a training set for
BPANN, some finite element simulations were carried out. die and
punch radius, die arc radius, friction coefficient, thickness, yield
strength of sheet and strain hardening exponent were used as the
input data and the LDR as the specified output used in the training of
neural network. As a result of the specified parameters, the program
will be able to estimate the LDR for any new given condition.
Comparing FEM and BPANN results, an acceptable correlation was
found.
Abstract: State-of-the-art methods for secondary structure (Porter, Psi-PRED, SAM-T99sec, Sable) and solvent accessibility (Sable, ACCpro) predictions use evolutionary profiles represented by the position specific scoring matrix (PSSM). It has been demonstrated that evolutionary profiles are the most important features in the feature space for these predictions. Unfortunately applying PSSM matrix leads to high dimensional feature spaces that may create problems with parameter optimization and generalization. Several recently published suggested that applying feature extraction for the PSSM matrix may result in improvements in secondary structure predictions. However, none of the top performing methods considered here utilizes dimensionality reduction to improve generalization. In the present study, we used simple and fast methods for features selection (t-statistics, information gain) that allow us to decrease the dimensionality of PSSM matrix by 75% and improve generalization in the case of secondary structure prediction compared to the Sable server.
Abstract: In this manuscript, we discuss the problem of determining the optimum stratification of a study (or main) variable based on the auxiliary variable that follows a uniform distribution. If the stratification of survey variable is made using the auxiliary variable it may lead to substantial gains in precision of the estimates. This problem is formulated as a Nonlinear Programming Problem (NLPP), which turn out to multistage decision problem and is solved using dynamic programming technique.
Abstract: In this study, a vibration analysis was carried out of
symmetric angle-ply laminated composite plates with and without
square hole when subjected to compressive loads, numerically. A
buckling analysis is also performed to determine the buckling load of
laminated plates. For each fibre orientation, the compression load is
taken equal to 50% of the corresponding buckling load. In the
analysis, finite element method (FEM) was applied to perform
parametric studies, the effects of degree of orthotropy and stacking
sequence upon the fundamental frequencies and buckling loads are
discussed. The results show that the presence of a constant
compressive load tends to reduce uniformly the natural frequencies
for materials which have a low degree of orthotropy. However, this
reduction becomes non-uniform for materials with a higher degree of
orthotropy.
Abstract: This paper discusses the approach of real-time
controlling of the energy management system using the data
acquisition tool of LabVIEW. The main idea of this inspiration was
to interface the Station (PC) with the system and publish the data on
internet using LabVIEW. In this venture, controlling and switching of
3 phase AC loads are effectively and efficiently done. The phases are
also sensed through devices. In case of any failure the attached
generator starts functioning automatically. The computer sends
command to the system and system respond to the request. The
modern feature is to access and control the system world-wide using
world wide web (internet). This controlling can be done at any time
from anywhere to effectively use the energy especially in developing
countries where energy management is a big problem. In this system
totally integrated devices are used to operate via remote location.
Abstract: In this paper we present a Feed-Foward Neural
Networks Autoregressive (FFNN-AR) model with genetic algorithms
training optimization in order to predict the gross domestic product
growth of six countries. Specifically we propose a kind of weighted
regression, which can be used for econometric purposes, where the
initial inputs are multiplied by the neural networks final optimum
weights from input-hidden layer of the training process. The
forecasts are compared with those of the ordinary autoregressive
model and we conclude that the proposed regression-s forecasting
results outperform significant those of autoregressive model.
Moreover this technique can be used in Autoregressive-Moving
Average models, with and without exogenous inputs, as also the
training process with genetics algorithms optimization can be
replaced by the error back-propagation algorithm.
Abstract: This paper presents a new methodology to select test
cases from regression test suites. The selection strategy is based on
analyzing the dynamic behavior of the applications that written in
any programming language. Methods based on dynamic analysis are
more safe and efficient. We design a technique that combine the code
based technique and model based technique, to allow comparing the
object oriented of an application that written in any programming
language. We have developed a prototype tool that detect changes
and select test cases from test suite.
Abstract: Gas flaring is one of the most GHG emitting sources in the oil and gas industries. It is also a major way for wasting such an energy that could be better utilized and even generates revenue. Minimize flaring is an effective approach for reducing GHG emissions and also conserving energy in flaring systems. Integrating waste and flared gases into the fuel gas networks (FGN) of refineries is an efficient tool. A fuel gas network collects fuel gases from various source streams and mixes them in an optimal manner, and supplies them to different fuel sinks such as furnaces, boilers, turbines, etc. In this article we use fuel gas network model proposed by Hasan et al. as a base model and modify some of its features and add constraints on emission pollution by gas flaring to reduce GHG emissions as possible. Results for a refinery case study showed that integration of flare gas stream with waste and natural gas streams to construct an optimal FGN can significantly reduce total annualized cost and flaring emissions.
Abstract: Embedding Sustainability in technological curricula has become a crucial factor for educating engineers with competences in sustainability. The Technical University of Catalonia UPC, in 2008, designed the Sustainable Technology Excellence Program STEP 2015 in order to assure a successful Sustainability Embedding. This Program takes advantage of the opportunity that the redesign of all Bachelor and Master Degrees in Spain by 2010 under the European Higher Education Area framework offered. The STEP program goals are: to design compulsory courses in each degree; to develop the conceptual base and identify reference models in sustainability for all specialties at UPC; to create an internal interdisciplinary network of faculty from all the schools; to initiate new transdisciplinary research activities in technology-sustainability-education; to spread the know/how attained; to achieve international scientific excellence in technology-sustainability-education and to graduate the first engineers/architects of the new EHEA bachelors with sustainability as a generic competence. Specifically, in this paper authors explain their experience in leading the STEP program, and two examples are presented: Industrial Robotics subject and the curriculum for the School of Architecture.
Abstract: There are several approaches in trying to solve the
Quantitative 1Structure-Activity Relationship (QSAR) problem.
These approaches are based either on statistical methods or on
predictive data mining. Among the statistical methods, one should
consider regression analysis, pattern recognition (such as cluster
analysis, factor analysis and principal components analysis) or partial
least squares. Predictive data mining techniques use either neural
networks, or genetic programming, or neuro-fuzzy knowledge. These
approaches have a low explanatory capability or non at all. This
paper attempts to establish a new approach in solving QSAR
problems using descriptive data mining. This way, the relationship
between the chemical properties and the activity of a substance
would be comprehensibly modeled.
Abstract: The Reverse Monte Carlo (RMC) simulation is applied in the study of an aqueous electrolyte LiCl6H2O. On the basis of the available experimental neutron scattering data, RMC computes pair radial distribution functions in order to explore the structural features of the system. The obtained results include some unrealistic features. To overcome this problem, we use the Hybrid Reverse Monte Carlo (HRMC), incorporating an energy constraint in addition to the commonly used constraints derived from experimental data. Our results show a good agreement between experimental and computed partial distribution functions (PDFs) as well as a significant improvement in pair partial distribution curves. This kind of study can be considered as a useful test for a defined interaction model for conventional simulation techniques.
Abstract: The objective of this work was to investigate flow
properties of powdered infant formula samples. Samples were
purchased at a local pharmacy and differed in composition. Lactose
free infant formula, gluten free infant formula and infant formulas
containing dietary fibers and probiotics were tested and compared
with a regular infant formula sample which did not contain any of
these supplements. Particle size and bulk density were determined
and their influence on flow properties was discussed. There were no
significant differences in bulk densities of the samples, therefore the
connection between flow properties and bulk density could not be
determined. Lactose free infant formula showed flow properties
different to standard supplement-free sample. Gluten free infant
formula with addition of probiotic microorganisms and dietary fiber
had the narrowest particle size distribution range and exhibited the
best flow properties. All the other samples exhibited the same
tendency of decreasing compaction coefficient with increasing flow
speed, which means they all become freer flowing with higher flow
speeds.
Abstract: Many real-world data sets consist of a very high dimensional feature space. Most clustering techniques use the distance or similarity between objects as a measure to build clusters. But in high dimensional spaces, distances between points become relatively uniform. In such cases, density based approaches may give better results. Subspace Clustering algorithms automatically identify lower dimensional subspaces of the higher dimensional feature space in which clusters exist. In this paper, we propose a new clustering algorithm, ISC – Intelligent Subspace Clustering, which tries to overcome three major limitations of the existing state-of-art techniques. ISC determines the input parameter such as є – distance at various levels of Subspace Clustering which helps in finding meaningful clusters. The uniform parameters approach is not suitable for different kind of databases. ISC implements dynamic and adaptive determination of Meaningful clustering parameters based on hierarchical filtering approach. Third and most important feature of ISC is the ability of incremental learning and dynamic inclusion and exclusions of subspaces which lead to better cluster formation.
Abstract: We present a prototype interactive (hyper) map of strategic, tactical, and logistic options for Supply Chain Management. The map comprises an anthology of options, broadly classified within the strategic spectrum of efficiency versus responsiveness, and according to logistic and cross-functional drivers. They are exemplified by cases in diverse industries. We seek to get all these information and ideas organized to help supply chain managers identify effective choices for specific business environments. The key and innovative linkage we introduce is the configuration of competitive forces. Instead of going through seemingly endless and isolated cases and wondering how one can borrow from them, we aim to provide a guide by force comparisons. The premise is that best practices in a different industry facing similar forces may be a most productive resource in supply chain design and planning. A prototype template is demonstrated.
Abstract: Paced Auditory Serial Addition Test (PASAT) has
been used as a common research tool for different neurological
disorders like Multiple Sclerosis. Recently, technology let
researchers to introduce a new versions of the visual test, the paced
visual serial addition test (PVSAT). In this paper, the computerized
version of these two tests is introduced. Beside the number of true
responses are interpreted, the reaction time of subjects are calculated
by the software. We hypothesize that paying attention to the reaction
time may be valuable. For this purpose, sixty eight female normal
subjects and fifty eight male normal subjects are enrolled in the
study. We investigate the similarity between the PASAT3 and
PVSAT3 in number of true responses and the new criterion (the
average reaction time of each subject). The similarity between two
tests were rejected (p-value = 0.000) which means that these two test
differ. The effect of sex in the tests were not approved since the pvalues
of different between PASAT3 and PVSAT3 in both sex is the
same (p-value = 0.000) which means that male and female subjects
performed the tests at no different level of performance. The new
criterion shows a negative correlation with the age which offers aged
normal subjects may have the same number of true responses as the
young subjects but they have latent responses. This will give prove
for the importance of reaction time.
Abstract: The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.