Search results for: Optimization techniques

Authors: Yaojun Wang, Yaoqing Wang

Abstract:

Stock selection is an important decision-making problem. Many machine learning and data mining technologies are employed to build automatic stock-selection system. A profitable stock-selection system should consider the stock’s investment value and the market timing. In this paper, we present a hybrid system including both engage for stock selection. This system uses a case-based reasoning (CBR) model to execute the stock classification, uses a decision-tree model to help with market timing and stock selection. The experiments show that the performance of this hybrid system is better than that of other techniques regarding to the classification accuracy, the average return and the Sharpe ratio.

Keywords: Case-based reasoning, decision tree, stock selection, machine learning.

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Authors: Hossein Nezamabadi-pour, Saeid Saryazdi

Abstract:

In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported.

Keywords: Object-based image retrieval, DCT domain, Image indexing, Image classification.

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Authors: S. Hossainpour, A. R. Binesh

Abstract:

High-velocity oxygen fuel (HVOF) thermal spraying uses a combustion process to heat the gas flow and coating material. A computational fluid dynamics (CFD) model has been developed to predict gas dynamic behavior in a HVOF thermal spray gun in which premixed oxygen and propane are burnt in a combustion chamber linked to a parallel-sided nozzle. The CFD analysis is applied to investigate axisymmetric, steady-state, turbulent, compressible, chemically reacting, subsonic and supersonic flow inside and outside the gun. The gas velocity, temperature, pressure and Mach number distributions are presented for various locations inside and outside the gun. The calculated results show that the most sensitive parameters affecting the process are fuel-to-oxygen gas ratio and total gas flow rate. Gas dynamic behavior along the centerline of the gun depends on both total gas flow rate and fuel-to-oxygen gas ratio. The numerical simulations show that the axial gas velocity and Mach number distribution depend on both flow rate and ratio; the highest velocity is achieved at the higher flow rate and most fuel-rich ratio. In addition, the results reported in this paper illustrate that the numerical simulation can be one of the most powerful and beneficial tools for the HVOF system design, optimization and performance analysis.

Keywords: HVOF, CFD, gas dynamics, thermal spray, combustion.

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Authors: Bahloul Amel

Abstract:

This study is an attempt to raise the awareness of the stakeholders and the authorities on the sensitivity of Algerian secondary school teachers of English as a Foreign Language about the students’ loss of English language skills learned during formal schooling with effort and at expense and the supposed measures to arrest that loss. Data was collected from secondary school teachers of EFL and analyzed quantitatively using a questionnaire containing open-ended and close-ended questions. The results advocate a consensus about the need for actions to be adopted to make assessment techniques outcome-oriented. Most of the participants were in favor of including curricular activities involving contextualized learning, problem-solving learning critical selfawareness, self and peer-assisted learning, use of computers and internet so as to make learners autonomous.

Keywords: Contextualized learning, EFL, Lifelong learning.

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Authors: Tomohiro Ando, Satoshi Yamashita

Abstract:

The objective of this study is to propose a statistical modeling method which enables simultaneous term structure estimation of the risk-free interest rate, hazard and loss given default, incorporating the characteristics of the bond issuing company such as credit rating and financial information. A reduced form model is used for this purpose. Statistical techniques such as spline estimation and Bayesian information criterion are employed for parameter estimation and model selection. An empirical analysis is conducted using the information on the Japanese bond market data. Results of the empirical analysis confirm the usefulness of the proposed method.

Keywords: Empirical Bayes, Hazard term structure, Loss given default.

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Authors: Sina Babadi, K. M. Ebrahimi

Abstract:

The aim of this paper is to determine the stress levels at the end of a long slender shaft such as a drilling assembly used in the oil or gas industry using a mathematical model in real-time. The torsional deflection experienced by this type of drilling shaft (about 4 KM length and 20 cm diameter hollow shaft with a thickness of 1 cm) can only be determined using a distributed modeling technique. The main objective of this project is to calculate angular velocity and torque at the end of the shaft by TLM method and also analyzing of the behavior of the system by transient response. The obtained result is compared with lumped modeling technique the importance of these results will be evident only after the mentioned comparison. Two systems have different transient responses and in this project because of the length of the shaft transient response is very important.

Keywords: Distributed Lumped modeling, Lumped modeling, Drill string, Angular Velocity, Torque.

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Authors: Amaral, António, Araújo, Madalena

Abstract:

This paper seeks to give a general idea of the universe of project portfolio management, from its multidisciplinary nature, to the many challenges it raises, passing through the different techniques, models and tools used to solve the multiple problems known. It is intended to contribute to the clarification, with great depth, of the impacts and relationships involved in managing the projects- portfolio. It aims at proposing a technique for the project alignment with the organisational strategy, in order to select projects that later on will be considered in the analysis and selection of the portfolio. We consider the development of a methodology for assessing the project alignment index very relevant in the global market scenario. It can help organisations to gain a greater awareness of market dynamics, speed up the decision process and increase its consistency, thus enabling the strategic alignment and the improvement of the organisational performance.

Keywords: Project Portfolio Management Cycle, Project Portfolio Selection, Resource Assignment, Strategy Alignment technique

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Authors: M. Kidouche, H. Habbi, M. Zelmat, S. Grouni

Abstract:

In analyzing large scale nonlinear dynamical systems, it is often desirable to treat the overall system as a collection of interconnected subsystems. Solutions properties of the large scale system are then deduced from the solution properties of the individual subsystems and the nature of the interconnections. In this paper a new approach is proposed for the stability analysis of large scale systems, which is based upon the concept of vector Lyapunov functions and the decomposition methods. The present results make use of graph theoretic decomposition techniques in which the overall system is partitioned into a hierarchy of strongly connected components. We show then, that under very reasonable assumptions, the overall system is stable once the strongly connected subsystems are stables. Finally an example is given to illustrate the constructive methodology proposed.

Keywords: Comparison principle, First integral, Large scale system, Lyapunov stability.

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Authors: Abdel-Samad M. Ali

Abstract:

Given the motivation of maps impact in enhancing the perception of the quality of life in a region, this work examines the use of spatial analytical techniques in exploring the role of space in shaping human development patterns in Assiut governorate. Variations of human development index (HDI) of the governorate-s villages, districts and cities are mapped using geographic information systems (GIS). Global and local spatial autocorrelation measures are employed to assess the levels of spatial dependency in the data and to map clusters of human development. Results show prominent disparities in HDI between regions of Assiut. Strong patterns of spatial association were found proving the presence of clusters on the distribution of HDI. Finally, the study indicates several "hot-spots" in the governorate to be area of more investigations to explore the attributes of such levels of human development. This is very important for accomplishing the development plan of poorest regions currently adopted in Egypt.

Keywords: Human development, Egypt, GIS, Spatial analysis.

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Authors: Piyush Samant, Ravinder Agarwal

Abstract:

Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier.

Keywords: Complementary and alternative medicine, Iridology, iris, feature extraction, classification, disease prediction.

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Authors: P. K. Singhal, R. Naresh, V. Sharma

Abstract:

This paper presents the three optimization models, namely New Binary Artificial Bee Colony (NBABC) algorithm, NBABC with Local Search (NBABC-LS), and NBABC with Genetic Crossover (NBABC-GC) for solving the Wind-Thermal Unit Commitment (WTUC) problem. The uncertain nature of the wind power is incorporated using the Weibull probability density function, which is used to calculate the overestimation and underestimation costs associated with the wind power fluctuation. The NBABC algorithm utilizes a mechanism based on the dissimilarity measure between binary strings for generating the binary solutions in WTUC problem. In NBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The local search operator exploits the neighboring region of the current solutions, whereas the integration of genetic crossover with the NBABC algorithm increases the diversity in the search space and thus avoids the problem of local trappings encountered with the NBABC algorithm. These models are then used to decide the units on/off status, whereas the lambda iteration method is used to dispatch the hourly load demand among the committed units. The effectiveness of the proposed models is validated on an IEEE 10-unit thermal system combined with a wind farm over the planning period of 24 hours.

Keywords: Artificial bee colony algorithm, economic dispatch, unit commitment, wind power.

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Authors: P. K. Singhal, R. Naresh, V. Sharma

Abstract:

This paper presents the three optimization models, namely New Binary Artificial Bee Colony (NBABC) algorithm, NBABC with Local Search (NBABC-LS), and NBABC with Genetic Crossover (NBABC-GC) for solving the Wind-Thermal Unit Commitment (WTUC) problem. The uncertain nature of the wind power is incorporated using the Weibull probability density function, which is used to calculate the overestimation and underestimation costs associated with the wind power fluctuation. The NBABC algorithm utilizes a mechanism based on the dissimilarity measure between binary strings for generating the binary solutions in WTUC problem. In NBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The local search operator exploits the neighboring region of the current solutions, whereas the integration of genetic crossover with the NBABC algorithm increases the diversity in the search space and thus avoids the problem of local trappings encountered with the NBABC algorithm. These models are then used to decide the units on/off status, whereas the lambda iteration method is used to dispatch the hourly load demand among the committed units. The effectiveness of the proposed models is validated on an IEEE 10-unit thermal system combined with a wind farm over the planning period of 24 hours.

Keywords: Artificial bee colony algorithm, economic dispatch, unit commitment, wind power.

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Authors: S. Farzi

Abstract:

Recently, a lot of attention has been devoted to advanced techniques of system modeling. PNN(polynomial neural network) is a GMDH-type algorithm (Group Method of Data Handling) which is one of the useful method for modeling nonlinear systems but PNN performance depends strongly on the number of input variables and the order of polynomial which are determined by trial and error. In this paper, we introduce GPNN (genetic polynomial neural network) to improve the performance of PNN. GPNN determines the number of input variables and the order of all neurons with GA (genetic algorithm). We use GA to search between all possible values for the number of input variables and the order of polynomial. GPNN performance is obtained by two nonlinear systems. the quadratic equation and the time series Dow Jones stock index are two case studies for obtaining the GPNN performance.

Keywords: GMDH, GPNN, GA, PNN.

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Authors: Andrea Tundis, Carlos García Cordero, Rolf Egert, Alfredo Garro, Max Mühlhäuser

Abstract:

Resilience is an important system property that relies on the ability of a system to automatically recover from a degraded state so as to continue providing its services. Resilient systems have the means of detecting faults and failures with the added capability of automatically restoring their normal operations. Mastering resilience in the domain of Cyber-Physical Systems is challenging due to the interdependence of hybrid hardware and software components, along with physical limitations, laws, regulations and standards, among others. In order to overcome these challenges, this paper presents a modeling approach, based on the concept of Dynamic Cells, tailored to the management of Smart Grids. Additionally, a heuristic algorithm that works on top of the proposed modeling approach, to find resilient configurations, has been defined and implemented. More specifically, the model supports a flexible representation of Smart Grids and the algorithm is able to manage, at different abstraction levels, the resource consumption of individual grid elements on the presence of failures and faults. Finally, the proposal is evaluated in a test scenario where the effectiveness of such approach, when dealing with complex scenarios where adequate solutions are difficult to find, is shown.

Keywords: Cyber-physical systems, energy management, optimization, smart grids, self-healing, resilience, security.

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Authors: Faisal Al Yahmadi, Muhammad R. Ahmed

Abstract:

Electric power is a fundamental necessity in the 21^st century. Consequently, any break in electric power is probably going to affect the general activity. To make the power supply smooth and efficient, a smart grid network is introduced which uses communication technology. In any communication network, security is essential. It has been observed from several recent incidents that adversary causes an interruption to the operation of networks. In order to resolve the issues, it is vital to understand the threats and vulnerabilities associated with the smart grid networks. In this paper, we have investigated the threats and vulnerabilities in Smart Grid Networks (SGN) and the few solutions in the literature. Proposed solutions showed developments in electricity theft countermeasures, Denial of services attacks (DoS) and malicious injection attacks detection model, as well as malicious nodes detection using watchdog like techniques and other solutions.

Keywords: Smart grid network, security, threats, vulnerabilities.

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Authors: T. Sattarpour, D. Nazarpour

Abstract:

This paper investigates the effect of simultaneous placement of DGs and smart meters (SMs), on voltage profile improvement in active distribution networks (ADNs). A substantial center of attention has recently been on responsive loads initiated in power system problem studies such as distributed generations (DGs). Existence of responsive loads in active distribution networks (ADNs) would have undeniable effect on sizing and siting of DGs. For this reason, an optimal framework is proposed for sizing and siting of DGs and SMs in ADNs. SMs are taken into consideration for the sake of successful implementing of demand response programs (DRPs) such as direct load control (DLC) with end-side consumers. Looking for voltage profile improvement, the optimization procedure is solved by genetic algorithm (GA) and tested on IEEE 33-bus distribution test system. Different scenarios with variations in the number of DG units, individual or simultaneous placing of DGs and SMs, and adaptive power factor (APF) mode for DGs to support reactive power have been established. The obtained results confirm the significant effect of DRPs and APF mode in determining the optimal size and site of DGs to be connected in ADN resulting to the improvement of voltage profile as well.

Keywords: Active distribution network (ADN), distributed generations (DGs), smart meters (SMs), demand response programs (DRPs), adaptive power factor (APF).

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Authors: M. N. Huda, S. Yamada, N. Sonehara

Abstract:

Probability-based identity disclosure risk measurement may give the same overall risk for different anonymization strategy of the same dataset. Some entities in the anonymous dataset may have higher identification risks than the others. Individuals are more concerned about higher risks than the average and are more interested to know if they have a possibility of being under higher risk. A notation of overall risk in the above measurement method doesn-t indicate whether some of the involved entities have higher identity disclosure risk than the others. In this paper, we have introduced an identity disclosure risk measurement method that not only implies overall risk, but also indicates whether some of the members have higher risk than the others. The proposed method quantifies the overall risk based on the individual risk values, the percentage of the records that have a risk value higher than the average and how larger the higher risk values are compared to the average. We have analyzed the disclosure risks for different disclosure control techniques applied to original microdata and present the results.

Keywords: Anonymization, microdata, disclosure risk, privacy.

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Authors: Chengjie Wang, Li Yin, Chuanmin Wang

Abstract:

This paper presents a physics-based model for the high-voltage fast recovery diodes. The model provides a good trade-off between reverse recovery time and forward voltage drop realized through a combination of lifetime control and emitter efficiency reduction techniques. The minority carrier lifetime can be extracted from the reverse recovery transient response and forward characteristics. This paper also shows that decreasing the amount of the excess carriers stored in the drift region will result in softer characteristics which can be achieved using a lower doping level. The developed model is verified by experiment and the measurement data agrees well with the model.

Keywords: Emitter efficiency, lifetime control, P-i-N diode, physics-based model

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Authors: Zixin Liu, Shu Lü, Shouming Zhong, Mao Ye

Abstract:

The problem of exponential stability and periodicity for a class of cellular neural networks (DCNNs) with time-varying delays is investigated. By dividing the network state variables into subgroups according to the characters of the neural networks, some sufficient conditions for exponential stability and periodicity are derived via the methods of variation parameters and inequality techniques. These conditions are represented by some blocks of the interconnection matrices. Compared with some previous methods, the method used in this paper does not resort to any Lyapunov function, and the results derived in this paper improve and generalize some earlier criteria established in the literature cited therein. Two examples are discussed to illustrate the main results.

Keywords: Cellular neural networks, exponential stability, time varying delays, partitioned matrices, periodic solution.

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Authors: Christian Doppler, Gernot Hirschl, Gerhard Zsiga

Abstract:

Starting in 2020, an EU-wide CO2-limitation of 95 g/km is scheduled for the average of an OEMs passenger car fleet. Taking that into consideration additional improvement measures of the Diesel cycle are necessary in order to reduce fuel consumption and emissions while boosting, or at the least, keeping performance values at the same time. The present article deals with the possibilities of an optimized air/water charge air cooler, also called iCAC (indirect Charge Air Cooler) for a Diesel passenger car amongst extreme-boundary conditions. In this context, the precise objective was to show the impact of improved intercooling with reference to the engine working process (fuel consumption and NOx-emissions). Several extremeboundaries - e.g. varying ambient temperatures or mountainous routes - that will become very important in the near future regarding RDE (Real Driving emissions) were subject of the investigation. With the introduction of RDE in 2017 (EU6c measure), the controversial NEDC (New European Driving Cycle) will belong to the past and the OEMs will have to avoid harmful emissions in any conceivable real life situation. This is certainly going to lead to optimization-measurements at the powertrain, which again is going to make the implementation of iCACs, presently solely used for the premium class, more and more attractive for compact class cars. The investigations showed a benefit in FC between 1 and 3% for the iCAC in real world conditions.

Keywords: Air/Water-Charge Air Cooler, Co-Simulation, Diesel Working Process, EURO VI Fuel Consumption.

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Authors: F. Sarabchi, M. E. Kalantari

Abstract:

In this paper, Optimum adaptive loading algorithms are applied to multicarrier system with Space-Time Block Coding (STBC) scheme associated with space-time processing based on singular-value decomposition (SVD) of the channel matrix over Rayleigh fading channels. SVD method has been employed in MIMO-OFDM system in order to overcome subchannel interference. Chaw-s and Compello-s algorithms have been implemented to obtain a bit and power allocation for each subcarrier assuming instantaneous channel knowledge. The adaptive loaded SVD-STBC scheme is capable of providing both full-rate and full-diversity for any number of transmit antennas. The effectiveness of these techniques has demonstrated through the simulation of an Adaptive loaded SVDSTBC system, and the comparison shown that the proposed algorithms ensure better performance in the case of MIMO.

Keywords: OFDM, MIMO, SVD, STBC, Adaptive Loading.

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Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul

Abstract:

Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.

Keywords: Landcover classification, artificial neural network, remote sensing, SPOT-5.

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Authors: Qais M. Yousef, Yasmeen A. Alshaer

Abstract:

Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: Artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization.

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Authors: Liang-Ta Cheng, Ching-Yu Yang

Abstract:

Based on fast discrete cosine transform (FDCT), the authors present a high capacity and high perceived quality method for electrocardiogram (ECG) signal. By using a simple adjusting policy to the 1-dimentional (1-D) DCT coefficients, a large volume of secret message can be effectively embedded in an ECG host signal and be successfully extracted at the intended receiver. Simulations confirmed that the resulting perceived quality is good, while the hiding capability of the proposed method significantly outperforms that of existing techniques. In addition, our proposed method has a certain degree of robustness. Since the computational complexity is low, it is feasible for our method being employed in real-time applications.

Keywords: Data hiding, ECG steganography, fast discrete cosine transform, 1-D DCT bundle, real-time applications.

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Authors: M. Nazmunnahar, J. J. Del Val, A. Vimmrova, J. González

Abstract:

We report the microstructural and magnetic properties of Ni50Mn39Sn11 and Ni50Mn36Sn14 ribbon Heusler alloys. Experimental results were obtained by differential scanning calorymetry, X-ray diffraction and vibrating sample magnetometry techniques. The Ni-Mn-Sn system undergoes a martensitic structural transformation in a wide temperature range. For example, for Ni50Mn39Sn11 the start and finish temperatures of the martensitic and austenite phase transformation for ribbon alloy were Ms=336K, Mf=328K, As=335K and Af=343K whereas no structural transformation is observed for Ni50Mn36Sn14 alloys. Magnetic measurements show the typical ferromagnetic behavior with Curie temperature 207 K at low applied field of 50 Oe. The complex behavior exhibited by these Heusler alloys should be ascribed to the strong coupling between magnetism and structure, being their magnetic behavior determined by the distance between Mn atoms.

Keywords: Structural transformation, as-cast ribbon, Heusler alloys, Magnetic properties.

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Authors: Kakali Bhadra

Abstract:

Harmalol administration caused remarkable reduction in proliferation of HepG₂ cells with GI₅₀ of 14.2 mM, without showing much cytotoxicity in embryonic liver cell line, WRL-68. Data from circular dichroism and differential scanning calorimetric analysis of harmalol-CT DNA complex shows conformational changes with prominent CD perturbation and stabilization of CT DNA by 8 ^oC. Binding constant and stoichiometry was also calculated using the above biophysical techniques. Further, dose dependent apoptotic induction ability of harmalol was studied in HepG₂ cells using different biochemical assays. Generation of ROS, DNA damage, changes in cellular external and ultramorphology, alteration of membrane, formation of comet tail, decreased mitochondrial membrane potential and a significant increase in Sub G_o/G₁ population made the cancer cell, HepG₂, prone to apoptosis. Up regulation of p53 and caspase 3 further indicated the apoptotic role of harmalol.

Keywords: Apoptosis, beta carboline alkaloid, comet assay, cytotoxicity, ROS.

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Authors: A. R. Binesh, S. Hossainpour

Abstract:

Due to the stringent legislation for emission of diesel engines and also increasing demand on fuel consumption, the importance of detailed 3D simulation of fuel injection, mixing and combustion have been increased in the recent years. In the present work, FIRE code has been used to study the detailed modeling of spray and mixture formation in a Caterpillar heavy-duty diesel engine. The paper provides an overview of the submodels implemented, which account for liquid spray atomization, droplet secondary break-up, droplet collision, impingement, turbulent dispersion and evaporation. The simulation was performed from intake valve closing (IVC) to exhaust valve opening (EVO). The predicted in-cylinder pressure is validated by comparing with existing experimental data. A good agreement between the predicted and experimental values ensures the accuracy of the numerical predictions collected with the present work. Predictions of engine emissions were also performed and a good quantitative agreement between measured and predicted NOx and soot emission data were obtained with the use of the present Zeldowich mechanism and Hiroyasu model. In addition, the results reported in this paper illustrate that the numerical simulation can be one of the most powerful and beneficial tools for the internal combustion engine design, optimization and performance analysis.

Keywords: Diesel engine, Combustion, Pollution, CFD.

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Authors: Sumith Matharage, Damminda Alahakoon

Abstract:

Textual data plays an important role in the modern world. The possibilities of applying data mining techniques to uncover hidden information present in large volumes of text collections is immense. The Growing Self Organizing Map (GSOM) is a highly successful member of the Self Organising Map family and has been used as a clustering and visualisation tool across wide range of disciplines to discover hidden patterns present in the data. A comprehensive analysis of the GSOM’s capabilities as a text clustering and visualisation tool has so far not been published. These functionalities, namely map visualisation capabilities, automatic cluster identification and hierarchical clustering capabilities are presented in this paper and are further demonstrated with experiments on a benchmark text corpus.

Keywords: Text Clustering, Growing Self Organizing Map, Automatic Cluster Identification, Hierarchical Clustering.

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Authors: Edina Koch, Péter Hudacsek

Abstract:

In railways transition zone is present at the boundaries of zones with different stiffness. When a train rides from an embankment onto a stiff structure, such as a bridge, tunnel or culvert, an abrupt change in the support stiffness occurs possibly inducing differential settlements. This in long term can yield to the degradation of the tracks and foundations in the transition zones. A number of techniques have been proposed or implemented to provide gradual stiffness transition at the problem zones, such as methods to ensure gradually changing pad stiffness, application of long sleepers or installation of auxiliary rails in the transition zone. Aim of the research presented in this paper is to analyze the 3D and the dynamic effects induced by the passing train over an area where significant difference in the support stiffness exists. The effects were analyzed for different arrangements associated with certain differential settlement mitigation strategies of the transition zones.

Keywords: Culvert, dynamic load, HS small model, railway transition zone.

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Authors: Siti Norul Huda Sheikh Abdullah, Farshid Pirahan Siah, Nor Hanisah Haji Zainal Abidin, Shahnorbanun Sahran

Abstract:

The objective of this paper is to propose an adaptive multi threshold for image segmentation precisely in object detection. Due to the different types of license plates being used, the requirement of an automatic LPR is rather different for each country. The proposed technique is applied on Malaysian LPR application. It is based on Multi Layer Perceptron trained by back propagation. The proposed adaptive threshold is introduced to find the optimum threshold values. The technique relies on the peak value from the graph of the number object versus specific range of threshold values. The proposed approach has improved the overall performance compared to current optimal threshold techniques. Further improvement on this method is in progress to accommodate real time system specification.

Keywords: Multi-threshold approach, license plate recognition system.

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