Abstract: Environmental decision making, particularly about
hazardous waste management, is inherently exposed to a high
potential conflict, principally because of the trade-off between sociopolitical,
environmental, health and economic factors. The need to
plan complex contexts has led to an increasing request for decision
analytic techniques as support for the decision process. In this work,
alternative systems of asbestos-containing waste management
(ACW) in Puglia (Southern Italy) were explored by a multi-criteria
decision analysis. In particular, through Analytic Hierarchy Process
five alternatives management have been compared and ranked
according to their performance and efficiency, taking into account
environmental, health and socio-economic aspects. A separated
valuation has been performed for different temporal scale. For short
period results showed a narrow deviation between the disposal
alternatives “mono-material landfill in public quarry" and “dedicate
cells in existing landfill", with the best performance of the first one.
While for long period “treatment plant to eliminate hazard from
asbestos-containing waste" was prevalent, although high energy
demand required to achieve the change of crystalline structure. A
comparison with results from a participative approach in valuation
process might be considered as future development of method
application to ACW management.
Abstract: This article combines two techniques: data
envelopment analysis (DEA) and Factor analysis (FA) to data
reduction in decision making units (DMU). Data envelopment
analysis (DEA), a popular linear programming technique is useful to
rate comparatively operational efficiency of decision making units
(DMU) based on their deterministic (not necessarily stochastic)
input–output data and factor analysis techniques, have been proposed
as data reduction and classification technique, which can be applied
in data envelopment analysis (DEA) technique for reduction input –
output data. Numerical results reveal that the new approach shows a
good consistency in ranking with DEA.
Abstract: With the proliferation of Weblogs (blogs) use in
educational contexts, gaining a better understanding of why
students are willing to utilize blog systems has become an
important topic for practitioners and academics. While perceived
enjoyment has been found to have a significant influence on
behavioral intentions to use blogs or hedonic systems, few studies
have investigated the antecedents of perceived enjoyment in the
acceptance of blogging. The main purpose of the present study is to
explore the individual difference antecedents of perceived
enjoyment and examine how they influence behavioral intention to
blog through the mediation of perceived enjoyment. Based on the
previous literature, the Big Five personality traits (i.e.,
extraversion, agreeableness, conscientiousness, neuroticism, and
openness to experience), as well as computer self-efficacy and
personal innovation in information technology (PIIT), are
hypothesized as potential antecedents of perceived enjoyment in
the acceptance of blogging. Data collected from 358 respondents in
Taiwan are tested against the research model using the structural
equation modeling approach. The results indicate that extraversion,
agreeableness, conscientiousness, and PIIT have a significant
influence on perceived enjoyment, which in turn significantly
influences the behavioral intention to blog. These findings lead to
several important implications for future research.
Abstract: The governing differential equations of laminated
plate utilizing trigonometric shear deformation theory are derived
using energy approach. The governing differential equations
discretized by different radial basis functions are used to predict the
free vibration behavior of symmetric laminated composite plates.
Effect of orthotropy and span to thickness ratio on frequency
parameter of simply supported laminated plate is presented.
Numerical results show the accuracy and good convergence of radial
basis functions.
Abstract: In this work, we try to find the best setting
of Computational Fluid Dynamic solver available for the problems in
the field of supersonic internal flows. We used the supersonic air-toair
ejector to represent the typical problem in focus. There are
multiple oblique shock waves, shear layers, boundary layers
and normal shock interacting in the supersonic ejector making this
device typical in field of supersonic inner flows. Modeling of shocks
in general is demanding on the physical model of fluid, because
ordinary conservation equation does not conform to real conditions in
the near-shock region as found in many works. From these reasons,
we decided to take special care about solver setting in this article by
means of experimental approach of color Schlieren pictures and
pneumatic measurement. Fast pressure transducers were used to
measure unsteady static pressure in regimes with normal shock in
mixing chamber. Physical behavior of ejector in several regimes is
discussed. Best choice of eddy-viscosity setting is discussed on the
theoretical base. The final verification of the k-ω SST is done on the
base of comparison between experiment and numerical results.
Abstract: Recognizing human action from videos is an active
field of research in computer vision and pattern recognition. Human
activity recognition has many potential applications such as video
surveillance, human machine interaction, sport videos retrieval and
robot navigation. Actually, local descriptors and bag of visuals words
models achieve state-of-the-art performance for human action
recognition. The main challenge in features description is how to
represent efficiently the local motion information. Most of the
previous works focus on the extension of 2D local descriptors on 3D
ones to describe local information around every interest point. In this
paper, we propose a new spatio-temporal descriptor based on a spacetime
description of moving points. Our description is focused on an
Accordion representation of video which is well-suited to recognize
human action from 2D local descriptors without the need to 3D
extensions. We use the bag of words approach to represent videos.
We quantify 2D local descriptor describing both temporal and spatial
features with a good compromise between computational complexity
and action recognition rates. We have reached impressive results on
publicly available action data set
Abstract: Optical network uses a tool for routing called Latin
router. These routers use particular algorithms for routing. For
example, we can refer to LDF algorithm that uses backtracking (one
of CSP methods) for problem solving. In this paper, we proposed
new approached for completion routing table (DRA&CRA
algorithm) and compare with pervious proposed ways and showed
numbers of backtracking, blocking and run time for DRA algorithm
less than LDF and CRA algorithm.
Abstract: Based on the combined shape feature and texture
feature, a fast object detection method with rotation invariant features
is proposed in this paper. A quick template matching scheme based
online learning designed for online applications is also introduced in
this paper. The experimental results have shown that the proposed
approach has the features of lower computation complexity and
higher detection rate, while keeping almost the same performance
compared to the HOG-based method, and can be more suitable for
run time applications.
Abstract: The work describes the use of a synthetic transmit
aperture (STA) with a single element transmitting and all elements
receiving in medical ultrasound imaging. STA technique is a novel
approach to today-s commercial systems, where an image is acquired
sequentially one image line at a time that puts a strict limit on the
frame rate and the amount of data needed for high image quality. The
STA imaging allows to acquire data simultaneously from all
directions over a number of emissions, and the full image can be
reconstructed.
In experiments a 32-element linear transducer array with 0.48 mm
inter-element spacing was used. Single element transmission aperture
was used to generate a spherical wave covering the full image region.
The 2D ultrasound images of wire phantom are presented obtained
using the STA and commercial ultrasound scanner Antares to
demonstrate the benefits of the SA imaging.
Abstract: Persian (Farsi) script is totally cursive and each character is written in several different forms depending on its former and later characters in the word. These complexities make automatic handwriting recognition of Persian a very hard problem and there are few contributions trying to work it out. This paper presents a novel practical approach to online recognition of Persian handwriting which is based on representation of inputs and patterns with very simple visual features and comparison of these simple terms. This recognition approach is tested over a set of Persian words and the results have been quite acceptable when the possible words where unknown and they were almost all correct in cases that the words where chosen from a prespecified list.
Abstract: Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for Thyristor Controlled Series Compensator (TCSC)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both the PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances, and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a TCSC-based controller, to enhance power system stability.
Abstract: This study employs auto-regressive distributed lag (ARDL) bounds approach to cointegration for long run and errorcorrection modeling (ECM) for short run analysis to examine the relationship between revenue gap and economic growth for Pakistan using annual time series data over the period 1980 to 2008. The short and long run results indicate that revenue gap is statistical significant and negatively effect economic growth. The significant and negative coefficient of error correction term in ECM indicates that after a shock, the long rum equilibrium will again converge towards equilibrium about 10.406 percent within a year.
Abstract: In the Fe-3%Si sheets, grade Hi-B, with AlN and MnS
as inhibitors, the Goss grains which abnormally grow do not have a
size greater than the average size of the primary matrix. In this
heterogeneous microstructure, the size factor is not a required
condition for the secondary recrystallization. The onset of the small
Goss grain abnormal growth appears to be related to a particular
behavior of their grain boundaries, to the local texture and to the
distribution of the inhibitors. The presence and the evolution of
oriented clusters ensure to the small Goss grains a favorable
neighborhood to grow. The modified Monte-Carlo approach, which
is applied, considers the local environment of each grain. The grain
growth is dependent of its real spatial position; the matrix
heterogeneity is then taken into account. The grain growth conditions
are considered in the global matrix and in different matrixes
corresponding to A component clusters. The grain growth behaviour
is considered with introduction of energy only, energy and mobility,
energy and mobility and precipitates.
Abstract: The job shop scheduling problem (JSSP) is a
notoriously difficult problem in combinatorial optimization. This
paper presents a hybrid artificial immune system for the JSSP with the
objective of minimizing makespan. The proposed approach combines
the artificial immune system, which has a powerful global exploration
capability, with the local search method, which can exploit the optimal
antibody. The antibody coding scheme is based on the operation based
representation. The decoding procedure limits the search space to the
set of full active schedules. In each generation, a local search heuristic
based on the neighborhood structure proposed by Nowicki and
Smutnicki is applied to improve the solutions. The approach is tested
on 43 benchmark problems taken from the literature and compared
with other approaches. The computation results validate the
effectiveness of the proposed algorithm.
Abstract: This paper introduces the foundations of Bayesian probability theory and Bayesian decision method. The main goal of Bayesian decision theory is to minimize the expected loss of a decision or minimize the expected risk. The purposes of this study are to review the decision process on the issue of flood occurrences and to suggest possible process for decision improvement. This study examines the problem structure of flood occurrences and theoretically explicates the decision-analytic approach based on Bayesian decision theory and application to flood occurrences in Environmental Engineering. In this study, we will discuss about the flood occurrences upon an annual maximum water level in cm, 43-year record available from 1965 to 2007 at the gauging station of Sagaing on the Ayeyarwady River with the drainage area - 120193 sq km by using Bayesian decision method. As a result, we will discuss the loss and risk of vast areas of agricultural land whether which will be inundated or not in the coming year based on the two standard maximum water levels during 43 years. And also we forecast about that lands will be safe from flood water during the next 10 years.
Abstract: In this paper a combination approach of two heuristic-based algorithms: genetic algorithm and tabu search is proposed. It has been developed to obtain the least cost based on the split-pipe design of looped water distribution network. The proposed combination algorithm has been applied to solve the three well-known water distribution networks taken from the literature. The development of the combination of these two heuristic-based algorithms for optimization is aimed at enhancing their strengths and compensating their weaknesses. Tabu search is rather systematic and deterministic that uses adaptive memory in search process, while genetic algorithm is probabilistic and stochastic optimization technique in which the solution space is explored by generating candidate solutions. Split-pipe design may not be realistic in practice but in optimization purpose, optimal solutions are always achieved with split-pipe design. The solutions obtained in this study have proved that the least cost solutions obtained from the split-pipe design are always better than those obtained from the single pipe design. The results obtained from the combination approach show its ability and effectiveness to solve combinatorial optimization problems. The solutions obtained are very satisfactory and high quality in which the solutions of two networks are found to be the lowest-cost solutions yet presented in the literature. The concept of combination approach proposed in this study is expected to contribute some useful benefits in diverse problems.
Abstract: This paper presents a new growing neural network for
cluster analysis and market segmentation, which optimizes the size
and structure of clusters by iteratively checking them for multivariate
normality. We combine the recently published SGNN approach [8]
with the basic principle underlying the Gaussian-means algorithm
[13] and the Mardia test for multivariate normality [18, 19]. The new
approach distinguishes from existing ones by its holistic design and
its great autonomy regarding the clustering process as a whole. Its
performance is demonstrated by means of synthetic 2D data and by
real lifestyle survey data usable for market segmentation.
Abstract: In this paper, applying frequency domain approach, a delayed predator-prey fishery model with prey reserve is investigated. By choosing the delay τ as a bifurcation parameter, It is found that Hopf bifurcation occurs as the bifurcation parameter τ passes a sequence of critical values. That is, a family of periodic solutions bifurcate from the equilibrium when the bifurcation parameter exceeds a critical value. The length of delay which preserves the stability of the positive equilibrium is calculated. Some numerical simulations are included to justify the theoretical analysis results. Finally, main conclusions are given.
Abstract: During last decades, developing multi-objective
evolutionary algorithms for optimization problems has found
considerable attention. Flexible job shop scheduling problem, as an
important scheduling optimization problem, has found this attention
too. However, most of the multi-objective algorithms that are
developed for this problem use nonprofessional approaches. In
another words, most of them combine their objectives and then solve
multi-objective problem through single objective approaches. Of
course, except some scarce researches that uses Pareto-based
algorithms. Therefore, in this paper, a new Pareto-based algorithm
called controlled elitism non-dominated sorting genetic algorithm
(CENSGA) is proposed for the multi-objective FJSP (MOFJSP). Our
considered objectives are makespan, critical machine work load, and
total work load of machines. The proposed algorithm is also
compared with one the best Pareto-based algorithms of the literature
on some multi-objective criteria, statistically.
Abstract: This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple neural networks (NN), and interval neutrosophic sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of n-class neural networks are used to predict n-degree of truth memberships and n-degree of false memberships. Indeterminacy memberships or uncertainties in the predictions are estimated using a multidimensional interpolation method. These three memberships form the INS used to support the confidence in results of multiclass classification. Based on the experimental data, our approach improves the classification performance as compared to an existing technique applied only to the truth membership. In addition, our approach has the capability to provide a measure of uncertainty in the problem of multiclass classification.