two-region computational model

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Computational Analysis of Tumor Angiogenesis Patterns Using a Two dimensional Model

Computational Analysis of Tumor Angiogenesis Patterns Using a Two dimensional Model

Tumor angiogenesis was simulated using a two-dimensional computational model. The equation that governed angiogenesis comprised a tumor angiogenesis factor (TAF) conservation equation in time and space, which was solved numerically using the Galerkin finite element method. The time derivative in the equation was approximated by a forward Euler scheme. A stochastic process model was used to simulate vessel formation and vessel elongation towards a paracrine site, i.e., tumor-secreted basic fibroblast growth factor (bFGF). In this study, we assumed a two-dimensional model that represented a thin (1.0 mm) slice of the tumor. The growth of the tumor over time was modeled according to the dynamic value of bFGF secreted within the tumor. The data used for the model were based on a previously reported model of a brain tumor in which four distinct stages (multicellular spherical, first detectable lesion, diagnosis, and death of the virtual patient) were modeled. In our study, computation was not continued beyond the 'diagnosis' time point to avoid the computational complexity of analyzing numerous vascular branches. The numerical solutions revealed that no bFGF remained within the region in which vessels developed, owing to the uptake of bFGF by endothelial cells. Consequently, a sharp declining gradient of bFGF existed near the surface of the tumor. The vascular architecture developed numerous branches close to the tumor surface (the brush-border effect). Asymmetrical tumor growth was associated with a greater degree of branching at the tumor surface.
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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL 
NETWORK FOR PREDICTING THE DIABETES DISEASE

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE

The main idea of CoMP is when an UE is in the cell-edge region, it may be able to receive signal from multiple cell sites and the UEs transmission may be received at multiple cell sites. The dependence of the user rate distribution on the number of rings of neighbours with which each base station is coordinated, as well as signal to noise ratio distribution in the network, results in the possibility of increasing cell edge user rates almost tenfold, doubling the overall system throughput in high-SNR conditions. The UL interference prediction is to perform link adaptation based on SINR value occurs during data transmissions. The performance gains with intrasite cooperation obtained up to 25 percent gain with respect to baseline cell edge throughput due to UL receivers with Channel State Information (CSI). For the UL, the signal can be received by multiple cell sites; the system can take advantage of this multiple reception to significantly improve the link performance.
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Numerical simulation of flow surrounding a thermoacoustic stack:single-stack against double-stack plate domain

Numerical simulation of flow surrounding a thermoacoustic stack:single-stack against double-stack plate domain

The first numerical simulation of the thermoacoustic effects was by Cao et al. [8] but the study with a negligible thickness plate assumed the standing wave as a priori. The first simulation on the whole resonator where the stack is encased was probably by Mohd- Ghazali [9], where many complex behaviors were reported as the acoustics were generated and progressed. Zoontjenset al. [10] utilized a commercial CFD package, FLUENT, to model the flow behavior near a single plate. Experimental and numerical studies have shown streaming effects near the stack which could be the reason for the low performance of the thermoacoustic refrigerator [2-7, 9-12]. This and the presence of vortices removed the kinetic energy otherwise absorbed by the stack for the heat transfer processes. These studies on single- and two-plate stack region have not focused onthe differences resulted from the choice of the computational domain where the general macroscopic behavior of the vortices and streaming are always observed. Thus, this study has been undertaken to look at the flow surrounding a single-plate and a double-plate stack to identify if there is any difference that exist in the development of the streaming effects and vortices.
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Two-dimensional MHD model of the reconnection diffusion region

Two-dimensional MHD model of the reconnection diffusion region

Abstract. Magnetic reconnection is an important process providing a fast conversion of magnetic energy into thermal and kinetic plasma energy. In this concern, a key problem is that of the resistive diffusion region where the reconnec- tion process is initiated. In this paper, the diffusion region is associated with a nonuniform conductivity localized to a small region. The nonsteady resistive incompressible MHD equations are solved numerically for the case of symmetric reconnection of antiparallel magnetic fields. A Petschek type steady-state solution is obtained as a result of time relax- ation of the reconnection layer structure from an arbitrary initial stage. The structure of the diffusion region is studied for various ratios of maximum and minimum values of the plasma resistivity. The effective length of the diffusion re- gion and the reconnection rate are determined as functions of the length scale and the maximum of the resistivity. For suf- ficiently small length scale of the resistivity, the reconnection rate is shown to be consistent with Petschek’s formula. By increasing the resistivity length scale and decreasing the re- sistivity maximum, the reconnection layer tends to be wider, and correspondingly, the reconnection rate tends to be more consistent with that of the Parker-Sweet regime.
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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL 
NETWORK FOR PREDICTING THE DIABETES DISEASE

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE

17 This paper develops an optimal control system which is applied to vehicle models. The structure of the control system built using FLC as the main controller to lateral motion and using conventional control systems are common and reliable enough used to linear system that is the controller Proportional, Integral and Derivative (PID) [13] as the next controller to yaw motion To obtain the parameters of optimal control system, used FA optimization method. Simulations performed through a Software In the Loop Simulations (SILS) with the input in the form desired trajectory (Double lane change). Vehicle models built involves a 10-DOF (Degree of Freedom), which consists of 7-DOF model of ride vehicles (rolling, pitching, bounching and vertical displacement of each wheel) and 3-DOF model of handling vehicle (longitudinal motion, lateral motion and yaw motion). The simulation results show that the control system with the use of PLC and PID controller tuned by FA can further improve the vehicle dynamic performance compared with PSO. System performance expressed by lateral and yaw motion error in the form of Continuous Root Mean Error (C-RMS) along the desired trajectory. 2. VEHICLE MODEL
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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL 
NETWORK FOR PREDICTING THE DIABETES DISEASE

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE

Data Mining is a collection of number of computational approaches. These approaches are used to develop Knowledge inference systems by identifying the hidden and convincing patterns from the input data. The aim of this study is to propose a computational Hybrid Prediction Model (HPM) for efficient diabetes prediction. The Pima diabetic dataset is used as the data source, obtained from the University of California, Irvine (UCI), the machine learning repository. At first stage of the proposed HPM the filtration feature selection method of MATLAB is used for selecting the most discriminatory predictors, reflecting the possibility of diabetes occurrence. At second stage, a two-layered classification is applied on the filtered data, by combining Support Vector machine (SVM) and Neural Network, to enhance the overall recognition rate of the model. The proposed hybrid model gained 96.09% of overall accuracy. The comparative study is also conducted and it is evident that the proposed model had obtained the significant classification accuracy. The accuracy rates achieved by many researchers in the past, on the same data set, ranges from 59.4% to 92% of accuracy. Further, for validating and evaluating the results, Recognition rate, Mean Absolute Error (MAE) and Receiver Operating Characteristics (ROC) performance measures are used. This research can be helpful to the physicians for predicting and detecting the diabetes at an early stage, efficiently.
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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL 
NETWORK FOR PREDICTING THE DIABETES DISEASE

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE

Russia has prepared a universal concept of information model of educational databases covering the different areas of specialty and activities, based on many years of experience (authors: V.I.Averchenkov, V.B.Ilitsky, V.V.Naduvaev, Yu.M.Kazakov E.N.Frolov). Educational base and database of educational specialization, focused on the use of different data arrays applicants, students, graduate students and undergraduates; also they are a brief statement of the principles and problems of different courses with links to tutorials, educational and scientific literature [5]. The close relationship between educational databases and database of information researched disciplines, links can be represented in the form of hypertext. Therefore, a universal system is prepared to work with hypertext, allowing simultaneous control of symbolic information on the monitor, also cover the objects represented in the form of rastr graphs (PCX) and vector (DXF) format [6].
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Composite Materials Damage Modeling Based on Dielectric Properties

Composite Materials Damage Modeling Based on Dielectric Properties

For the tri-layer model, the total volume fraction is the sum of the volume fraction of the defect and the vo- lume fraction of the conductive layer. For all of the cases of tri-layer modelling, the conductive layer thickness was specified as 0.5 micro meter. We observed that for two phase models, the real part of the permittivity was almost linear but in Figure 16 we can see that for the three phase case there is an increase in real part of the permittivity for lower volume fractions and then a decreasing trend. As there is a conductive layer in between the defect and the host matrix, the interfacial polarization plays a vital role for this type of behavior. The subse- quent decrease of the real part of the permittivity for higher volume fractions is caused by the dominance of the volume of the defects which is higher than the interfacial polarization contributed by the conductive layer.
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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL 
NETWORK FOR PREDICTING THE DIABETES DISEASE

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE

Steganography approaches could be classified based on various criteria. For example, [13] provided classification for methods of steganography depending on the used technique focusing on steganalysis. Steganalysis was divided into two categories statistical and signature. Each one is branched into specific and universal. However, steganography could be classified depending on the cover type that contains the secret message intended to be hidden. According to this, we presented various proposed approaches that used either text, image, or audio file as a cover. First group, using text file as cover (i.e., the secret information will be embedded within a text file). A method to hide a secret message into a text file written with Hindi text where it depends on Hindi letters and its diacritics and numerical code to hide the secret message is provided in [3]. The authors mentioned strong point in their work which is that their proposed approach could be applied to similar Indian languages. Similarly, [4] provided an approach to embed a secret information within Arabic and Persian text file depending on several intersections between these two languages. It used vertical displacements of points to insert and hide the secret message. In their proposed work researchers presented a scheme based on cryptography using SSCE, where the secret key will be directly exchanged between the sender and receiver for both hiding and extracting a secret message[14]. Other researchers have elected certain letters from certain words as hiding place for secret message [15]. In the simplest form, for example, the first words of each sentence are elected in a manner that places the first letters of these words side by side. Similarly [16] followed the same key
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Critical Properties of the Two Dimensional Villain Model with Ferromagnetic Impurities

Critical Properties of the Two Dimensional Villain Model with Ferromagnetic Impurities

This work investigates the square lattice in the presence of anti-ferromagnetic exchange considering the two-dimensional Ising Villain model. A computa- tional scheme is constructed to evaluate the degeneracy of ground states at zero temperature employing the Pfaffian method through perturbation theory. The entropy and canonical spin glass of the considered model are pre- cisely obtained. The critical point between the Villain phase and canonical spin glass phase with the line fit of entropy at the thermodynamic limit is in- vestigated. It is also shown that the transition point of the Villain model lies in the low concentration regime.
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A Computational Model of Social Perlocutions

A Computational Model of Social Perlocutions

What's been lacking is an attempt to integrate the lessons learned from these different research efforts to provide an initial model of social perlocutions; that is, a model that describ[r]

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Achievable Rate Regions for Orthogonally Multiplexed MIMO Broadcast Channels with Multi Dimensional Modulation

Achievable Rate Regions for Orthogonally Multiplexed MIMO Broadcast Channels with Multi Dimensional Modulation

In this work, we consider a MIMO BC with ortho- gonally multiplexed non-cooperating users who employ space-time modulation. As in [23,24], we assume a non-fading space and time dispersive multipath envi- ronment. These schemes model the downlink of cellular communication systems with orthogonal user multiplex- ing. We consider a deterministic channel model since it provides an insight to the behaviour of the capacity re- gion with respect to the number of antenna and multipath components, and often serves as a first step towards the study of fading channels. We investigate the achievable rate region of such orthogonally multiplexed broadcast schemes with multi-dimensional space-time modulation, where a transmitter attempts simultaneously to transfer information to several users without mutual interference. When the channel is known at the transmitter, we con- sider the optimal power allocation that maximizes the rate for each user. We also consider the power allocation for each user that causes the fastest convergence to zero of the fraction of channels whose mutual information is less than any given rate, as the transmitter channel knowledge goes to zero. For both cases, we investigate the maximum sum rate. Considering a two-user broad- cast system, we investigate the asymptotic behaviour of the achievable rate regions at low and high SNR, and provide the optimum power allocations that correspond to the maximum sum rate. Illustrative numerical results are provided for users having different propagation channels, using different multi-dimensional space-time modulation schemes and employing different number of antennas. This paper is structured as follows. Section 2 presents the system model. The capacity region with known channel at the transmitter is investigated in Sec- tion 3. The case of unknown channel at the transmitter is considered in Section 4. Section 5 presents some illustra- tive numerical results. The conclusions follow in Section 6.
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Control System Research of Adaptive Observer for PMLSM

Control System Research of Adaptive Observer for PMLSM

Adaptive observer method is a widely used method, which includes a reference model and adjustable contained identification parameters. Its speed adaptive law is derived by Popov ultra-stability theory, which ensures the asymptotic convergence of the parameter estimates, and it has a good dynamic performance. The control system using space vector control method, and the implementation process of vector control on permanent magnet linear synchronous motor is analyzed particularly, we also give the modules of control system in Matlab/Simulink. Through the results, we can verify the reliability of the speed sensorless control method. The problem of instability in the low-speed electric mode according to the observer linearization method was analyzed. To solve the problem, the observer augmented with a signal injection technique was researched. The simulation results show that the method is effective to enhance the system parameter robustness and speed observation, improve low-speed performance of the speed-sensorless vector control system.
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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL 
NETWORK FOR PREDICTING THE DIABETES DISEASE

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE

BTPN Syariah was born from a combination of the two powers, namely, PT. Bank Sahabat Purbadanarta and BTPN’s Syaria Business Unit. Bank Sahabat Purbadanarta, established since March 1991 in Semarang, is a non-foreign bank which 70% of the shares acquired by PT. Bank Tabungan Pensiunan Nasional (National Pesions Saving Bank), Tbk (BTPN), on January 20 th , 2014. It is then converted into BTPN Syariah based on the Decree of the Financial Services Authority (FSA) on 22 May 2014. BTPN’s Syaria Business Unit focusing on serving and empowering underprivileged families throughout Indonesia is one of the business segments in PT. Bank Tabungan Nasional (National Savings Bank), Tbk since March 2008, which was then spined off and merged into BTPN Syariah in June 2014.
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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL 
NETWORK FOR PREDICTING THE DIABETES DISEASE

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE

In related research has been to explore the adoption of mobile payments, the Technology Acceptance Model (TAM) is more popular theory, Moreover, most of the studies are usually based on structural equation modeling to validate (SEM) and other statistical methods between the relevant factors and technology adoption relationship.TAM plays a very important role in the adoption of information systems research.TAM has been widely used for a variety of information technology acceptance problems.The model is the main theoretical basis of this study. Since the initial TAM only considered the perceived ease of use and perceived usefulness. Its structure is too simple and has been criticized by some scholars[1].Therefore, we need to expand the adoption of TAM to better explore the issue of mobile payment. In research methods, the existing literature is often limited to the use of interpretative statistical analysis methods(such as regression analysis, structural equation) to examine the causal relationship between technology adoption and related factors of. Study of this causal relationship is a parametric regression. It assumes that the users adoption decisions are linear compensation. A lack of adoption decisions factors (such as perceived usefulness) of the other factors by improving (such
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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL 
NETWORK FOR PREDICTING THE DIABETES DISEASE

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE

For many of today’s youngsters and children, the Internet, mobile phones and generally digital devices are integral part of their life and they can barely imagine their life without a social networking systems. Despite many advantages of the Internet, it is hard to neglect the Internet side effects in people life. Exposure to illicit images is very common among adolescent and children, with a variety of significant and often upsetting effects on their growth and thoughts. Thus, detecting and filtering illicit images is a hot and fast evolving topic in computer vision. In this research we tried to summarize the existing visual feature extraction techniques used for illicit image detection. Feature extraction can be separate into two sub- techniques feature detection and description. This research presents the-state-of-the-art techniques in each group. The evaluation measurements and metrics used in other researches are summarized at the end of the paper. We hope that this research help the readers to better find the proper feature extraction technique or develop a robust and accurate visual feature extraction technique for illicit image detection and filtering purpose.
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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL 
NETWORK FOR PREDICTING THE DIABETES DISEASE

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE

In the case of uncertain model where system parameters are not known with enough accuracy, suitable choice of control variables and update laws must be done to still ensure the stability condition [11-14]. In this study, it is assumed that the mechanical parameters F, J and mechanical torque T t are unknown. F and J are constant parameters

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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL 
NETWORK FOR PREDICTING THE DIABETES DISEASE

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE

In the present paper we have conferred complementary security approaches for High- Performance Computing clusters. The corresponding security methodologies and approaches we present for this cluster environment is a unified security model including distributed authentication, access control and user level security. At the HPC cluster environment end of the cluster environment , clusters must be versatile to handle a dynamic user constituency executing a large vary of applications. The corresponding security suggestions/techniques we present for this HPC cluster environment focuses on Secure job scheduling ,job credential handling, user level security, secure transparency There is much more work to be done in areas such as intuitive human interfaces to security tools, scalable cluster monitoring, masquerade detection, interconnect security, and versatile protection that progressive with incremental cluster growth. And the secure scheduling approach that we showed and the simulated results from this paper can also be achieved in a real environment.
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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL 
NETWORK FOR PREDICTING THE DIABETES DISEASE

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE

[4] R. Syahputra, I. Soesanti, M. Ashari. (2016). “Performance Enhancement of Distribution Network with DG Integration Using Modified PSO Algorithm”, Journal of Electrical Systems (JES), Vol. 12, No. 1, 2016. pp. 1-19. [5] H. Rahadian; B. Sutopo, I. Soesanti. (2015). “TGS2611 performance as biogas monitoring instrument in digester model application”. 2015 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2015, pp. 119-124.

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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL 
NETWORK FOR PREDICTING THE DIABETES DISEASE

A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE

It seems that the term “stochastic”, as applied to the problems of information security (IS), was used for the first time by S.A. Os- molovsky in constructing the codes, detecting and correcting the errors which arise in the data transmission over the communication channels [2-3]. The stochastic codes, proposed by him, possess the unique properties, among which two are worth to be mentioned: the capability to pro- vide in advance a given probability of correct reception of information and the ability to solve, besides the task of providing the noise resistance, two no less important IS tasks: ensuring confi- dentiality and integrity of the transmitted infor- mation.
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