Abstract: Partitioning is a critical area of VLSI CAD. In order to build complex digital logic circuits its often essential to sub-divide multi -million transistor design into manageable Pieces. This paper looks at the various partitioning techniques aspects of VLSI CAD, targeted at various applications. We proposed an evolutionary time-series model and a statistical glitch prediction system using a neural network with selection of global feature by making use of clustering method model, for partitioning a circuit. For evolutionary time-series model, we made use of genetic, memetic & neuro-memetic techniques. Our work focused in use of clustering methods - K-means & EM methodology. A comparative study is provided for all techniques to solve the problem of circuit partitioning pertaining to VLSI design. The performance of all approaches is compared using benchmark data provided by MCNC standard cell placement benchmark net lists. Analysis of the investigational results proved that the Neuro-memetic model achieves greater performance then other model in recognizing sub-circuits with minimum amount of interconnections between them.
Abstract: The paper deals with the analysis of triggering
conditions and evolution processes of piping phenomena, in relation
to both mechanical and hydraulic aspects. In particular, the aim of
the study is to predict slope instabilities triggered by piping,
analysing the conditions necessary for a flow failure to occur. Really,
the mechanical effect involved in the loads redistribution around the
pipe is coupled to the drainage process arising from higher
permeability of the pipe. If after the pipe formation, the drainage
goes prevented for pipe clogging, the porewater pressure increase can
lead to the failure or even the liquefaction, with a subsequent flow
slide. To simulate the piping evolution and to verify relevant stability
conditions, a iterative coupled modelling approach has been pointed
out. As example, the proposed tool has been applied to the Stava
Valley disaster (July, 1985), demonstrating that piping might be one
of triggering phenomena of the tailings dams collapse.
Abstract: Visualizing sound and noise often help us to determine
an appropriate control over the source localization. Near-field acoustic
holography (NAH) is a powerful tool for the ill-posed problem.
However, in practice, due to the small finite aperture size, the discrete
Fourier transform, FFT based NAH couldn-t predict the activeregion-
of-interest (AROI) over the edges of the plane. Theoretically
few approaches were proposed for solving finite aperture problem.
However most of these methods are not quite compatible for the
practical implementation, especially near the edge of the source. In
this paper, a zip-stuffing extrapolation approach has suggested with
2D Kaiser window. It is operated on wavenumber complex space
to localize the predicted sources. We numerically form a practice
environment with touch impact databases to test the localization of
sound source. It is observed that zip-stuffing aperture extrapolation
and 2D window with evanescent components provide more accuracy
especially in the small aperture and its derivatives.
Abstract: Semantic Web Technologies enable machines to
interpret data published in a machine-interpretable form on the web.
At the present time, only human beings are able to understand the
product information published online. The emerging semantic Web
technologies have the potential to deeply influence the further
development of the Internet Economy. In this paper we propose a
scenario based research approach to predict the effects of these new
technologies on electronic markets and business models of traders
and intermediaries and customers. Over 300 million searches are
conducted everyday on the Internet by people trying to find what
they need. A majority of these searches are in the domain of
consumer ecommerce, where a web user is looking for something to
buy. This represents a huge cost in terms of people hours and an
enormous drain of resources. Agent enabled semantic search will
have a dramatic impact on the precision of these searches. It will
reduce and possibly eliminate information asymmetry where a better
informed buyer gets the best value. By impacting this key
determinant of market prices semantic web will foster the evolution
of different business and economic models. We submit that there is a
need for developing these futuristic models based on our current
understanding of e-commerce models and nascent semantic web
technologies. We believe these business models will encourage
mainstream web developers and businesses to join the “semantic web
revolution."
Abstract: In this paper, subtractive clustering based fuzzy inference system approach is used for early detection of faults in the function oriented software systems. This approach has been tested with real time defect datasets of NASA software projects named as PC1 and CM1. Both the code based model and joined model (combination of the requirement and code based metrics) of the datasets are used for training and testing of the proposed approach. The performance of the models is recorded in terms of Accuracy, MAE and RMSE values. The performance of the proposed approach is better in case of Joined Model. As evidenced from the results obtained it can be concluded that Clustering and fuzzy logic together provide a simple yet powerful means to model the earlier detection of faults in the function oriented software systems.
Abstract: The γ-turns play important roles in protein folding and
molecular recognition. The prediction and analysis of γ-turn types are
important for both protein structure predictions and better
understanding the characteristics of different γ-turn types. This study
proposed a physicochemical property-based decision tree (PPDT)
method to interpretably predict γ-turn types. In addition to the good
prediction performance of PPDT, three simple and human
interpretable IF-THEN rules are extracted from the decision tree
constructed by PPDT. The identified informative physicochemical
properties and concise rules provide a simple way for discriminating
and understanding γ-turn types.
Abstract: In this research the separation efficiency of deoiling hydrocyclone is evaluated using three-dimensional simulation of multiphase flow based on Eulerian-Eulerian finite volume method. The mixture approach of Reynolds Stress Model is also employed to capture the features of turbulent multiphase swirling flow. The obtained separation efficiency of Colman's design is compared with available experimental data and showed that the separation curve of deoiling hydrocyclones can be predicted using numerical simulation.
Abstract: The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. An improved model for temperature prediction in Georgia was developed by including information on seasonality and modifying parameters of an existing artificial neural network model. Alternative models were compared by instantiating and training multiple networks for each model. The inclusion of up to 24 hours of prior weather information and inputs reflecting the day of year were among improvements that reduced average four-hour prediction error by 0.18°C compared to the prior model. Results strongly suggest model developers should instantiate and train multiple networks with different initial weights to establish appropriate model parameters.
Abstract: In the present study, a procedure was developed to
determine the optimum reaction rate constants in generalized
Arrhenius form and optimized through the Nelder-Mead method. For
this purpose, a comprehensive mathematical model of a fixed bed
reactor for dehydrogenation of heavy paraffins over Pt–Sn/Al2O3
catalyst was developed. Utilizing appropriate kinetic rate expressions
for the main dehydrogenation reaction as well as side reactions and
catalyst deactivation, a detailed model for the radial flow reactor was
obtained. The reactor model composed of a set of partial differential
equations (PDE), ordinary differential equations (ODE) as well as
algebraic equations all of which were solved numerically to
determine variations in components- concentrations in term of mole
percents as a function of time and reactor radius. It was demonstrated
that most significant variations observed at the entrance of the bed
and the initial olefin production obtained was rather high. The
aforementioned method utilized a direct-search optimization
algorithm along with the numerical solution of the governing
differential equations. The usefulness and validity of the method was
demonstrated by comparing the predicted values of the kinetic
constants using the proposed method with a series of experimental
values reported in the literature for different systems.
Abstract: Employees commonly encounter unpredictable and
unavoidable work related stressors. Exposure to such stressors can
evoke negative appraisals and associated adverse mental, physical,
and behavioral responses. Because Acceptance and Commitment
Therapy (ACT) emphasizes acceptance of unavoidable stressors and
diffusion from negative appraisals, it may be particularly beneficial
for work stress. Forty-five workers were randomly assigned to an
ACT intervention for work stress (n = 21) or a waitlist control group
(n = 24). The intervention consisted of two 3-hour sessions spaced
one week apart. An examination of group process and outcomes was
conducted using the Revised Sessions Rating Scale. Results indicated
that the ACT participants reported that they perceived the
intervention to be supportive, task focused, and without adverse
therapist behaviors (e.g., feelings of being criticized or discounted).
Additionally, the second session (values clarification and
commitment to action) was perceived to be more supportive and task
focused than the first session (mindfulness, defusion). Process ratings
were correlated with outcomes. Results indicated that perceptions of
therapy supportiveness and task focus were associated with reduced
psychological distress and improved perceived physical health.
Abstract: Hemodialysis patients might suffer from unhealthy
care behaviors or long-term dialysis treatments. Ultimately they need
to be hospitalized. If the hospitalization rate of a hemodialysis center
is high, its quality of service would be low. Therefore, how to decrease
hospitalization rate is a crucial problem for health care. In this study
we combined temporal abstraction with data mining techniques for
analyzing the dialysis patients' biochemical data to develop a decision
support system. The mined temporal patterns are helpful for clinicians
to predict hospitalization of hemodialysis patients and to suggest them
some treatments immediately to avoid hospitalization.
Abstract: This paper proposes a methodology for mitigating the occurrence of cascading failure in stressed power systems. The methodology is essentially based on predicting voltage instability in the power system using a voltage stability index and then devising a corrective action in order to increase the voltage stability margin. The paper starts with a brief description of the cascading failure mechanism which is probable root cause of severe blackouts. Then, the voltage instability indices are introduced in order to evaluate stability limit. The aim of the analysis is to assure that the coordination of protection, by adopting load shedding scheme, capable of enhancing performance of the system after the major location of instability is determined. Finally, the proposed method to generate instability prediction is introduced.
Abstract: In this paper, the differential quadrature method is applied to simulate natural convection in an inclined cubic cavity using velocity-vorticity formulation. The numerical capability of the present algorithm is demonstrated by application to natural convection in an inclined cubic cavity. The velocity Poisson equations, the vorticity transport equations and the energy equation are all solved as a coupled system of equations for the seven field variables consisting of three velocities, three vorticities and temperature. The coupled equations are simultaneously solved by imposing the vorticity definition at boundary without requiring the explicit specification of the vorticity boundary conditions. Test results obtained for an inclined cubic cavity with different angle of inclinations for Rayleigh number equal to 103, 104, 105 and 106 indicate that the present coupled solution algorithm could predict the benchmark results for temperature and flow fields. Thus, it is convinced that the present formulation is capable of solving coupled Navier-Stokes equations effectively and accurately.
Abstract: An artificial neural network (ANN) model is
presented for the prediction of kinematic viscosity of binary mixtures
of poly (ethylene glycol) (PEG) in water as a function of temperature,
number-average molecular weight and mass fraction. Kinematic
viscosities data of aqueous solutions for PEG (0.55419×10-6 –
9.875×10-6 m2/s) were obtained from the literature for a wide range
of temperatures (277.15 - 338.15 K), number-average molecular
weight (200 -10000), and mass fraction (0.0 – 1.0). A three layer
feed-forward artificial neural network was employed. This model
predicts the kinematic viscosity with a mean square error (MSE) of
0.281 and the coefficient of determination (R2) of 0.983. The results
show that the kinematic viscosity of binary mixture of PEG in water
could be successfully predicted using an artificial neural network
model.
Abstract: A kinetic model for propane dehydrogenation in an
industrial moving bed reactor is developed based on the reported
reaction scheme. The kinetic parameters and activity constant are
fine tuned with several sets of balanced plant data. Plant data at
different operating conditions is applied to validate the model and
the results show a good agreement between the model
predictions and plant observations in terms of the amount of main
product, propylene produced. The simulation analysis of key
variables such as inlet temperature of each reactor (Tinrx) and
hydrogen to total hydrocarbon ratio (H2/THC) affecting process
performance is performed to identify the operating condition to
maximize the production of propylene. Within the range of operating
conditions applied in the present studies, the operating condition to
maximize the propylene production at the same weighted average
inlet temperature (WAIT) is ΔTinrx1= -2, ΔTinrx2= +1, ΔTinrx3= +1 ,
ΔTinrx4= +2 and ΔH2/THC= -0.02. Under this condition, the surplus
propylene produced is 7.07 tons/day as compared with base case.
Abstract: The Swine flu outbreak in humans is due to a new
strain of influenza A virus subtype H1N1 that derives in part from
human influenza, avian influenza, and two separated strains of swine
influenza. It can be transmitted from human to human. A
mathematical model for the transmission of Swine flu is developed in
which the human populations are divided into two classes, the risk
and non-risk human classes. Each class is separated into susceptible,
exposed, infectious, quarantine and recovered sub-classes. In this
paper, we formulate the dynamical model of Swine flu transmission
and the repetitive contacts between the people are also considered.
We analyze the behavior for the transmission of this disease. The
Threshold condition of this disease is found and numerical results are
shown to confirm our theoretical predictions.
Abstract: Protein 3D structure prediction has always been an
important research area in bioinformatics. In particular, the
prediction of secondary structure has been a well-studied research
topic. Despite the recent breakthrough of combining multiple
sequence alignment information and artificial intelligence algorithms
to predict protein secondary structure, the Q3 accuracy of various
computational prediction algorithms rarely has exceeded 75%. In a
previous paper [1], this research team presented a rule-based method
called RT-RICO (Relaxed Threshold Rule Induction from Coverings)
to predict protein secondary structure. The average Q3 accuracy on
the sample datasets using RT-RICO was 80.3%, an improvement
over comparable computational methods. Although this demonstrated
that RT-RICO might be a promising approach for predicting
secondary structure, the algorithm-s computational complexity and
program running time limited its use. Herein a parallelized
implementation of a slightly modified RT-RICO approach is
presented. This new version of the algorithm facilitated the testing of
a much larger dataset of 396 protein domains [2]. Parallelized RTRICO
achieved a Q3 score of 74.6%, which is higher than the
consensus prediction accuracy of 72.9% that was achieved for the
same test dataset by a combination of four secondary structure
prediction methods [2].
Abstract: Prediction of benzene transport in soil and volatilization from soil to the atmosphere is important for the preservation of human health and management of contaminated soils. The adequacy of a simple numerical model, assuming two-phase diffusion and equilibrium of liquid/solid adsorption, was investigated by experimental data of benzene concentration in a flux chamber (with headspace) where Andosol and sand were filled. Adsorption experiment for liquid phase was performed to determine an adsorption coefficient. Furthermore, adequacy of vapor phase adsorption was also studied through two runs of experiment using sand with different water content. The results show that the model adequately predicted benzene transport and volatilization from Andosol and sand with water content of 14.0%. In addition, the experiment additionally revealed that vapor phase adsorption should be considered in diffusion model for sand with very low water content.
Abstract: Base on extenics theory, the statistical physical and mechanical properties from laboratory experiments are used to evaluate the bearing capacity of lateritic soil foundation. The properties include water content, bulk density, liquid limit, cohesion, and so on. The matter-element and the dependent function are defined. Then the synthesis dependent degree and the final grade index are calculated. The results show that predicted outcomes can be matched with the in-situ test data, and a evaluate grade associate with bearing capacity can be deduced. The results provide guidance to assess and determine the bearing capacity grade of lateritic soil foundation.
Abstract: Natural gas flow contains undesirable solid particles,
liquid condensation, and/or oil droplets and requires reliable
removing equipment to perform filtration. Recent natural gas
processing applications are demanded compactness and reliability of
process equipment. Since conventional means are sophisticated in
design, poor in efficiency, and continue lacking robust, a supersonic
nozzle has been introduced as an alternative means to meet such
demands.
A 3-D Convergent-Divergent Nozzle is simulated using
commercial Code for pressure ratio (NPR) varies from 1.2 to 2. Six
different shapes of nozzle are numerically examined to illustrate the
position of shock-wave as such spot could be considered as a
benchmark of particle separation. Rectangle, triangle, circular,
elliptical, pentagon, and hexagon nozzles are simulated using Fluent
Code with all have same cross-sectional area.
The simple one-dimensional inviscid theory does not describe the
actual features of fluid flow precisely as it ignores the impact of
nozzle configuration on the flow properties. CFD Simulation results,
however, show that nozzle geometry influences the flow structures
including location of shock wave.
The CFD analysis predicts shock appearance when p01/pa>1.2 for
almost all geometry and locates at the lower area ratio (Ae/At).
Simulation results showed that shock wave in Elliptical nozzle has
the farthest distance from the throat among the others at relatively
small NPR. As NPR increases, hexagon would be the farthest. The
numerical result is compared with available experimental data and
has shown good agreement in terms of shock location and flow
structure.