Search results for: air pollution prediction.

Authors: Pradeep Kumar, Abdul Wahid

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Accurate software reliability prediction not only enables developers to improve the quality of software but also provides useful information to help them for planning valuable resources. This paper examines the performance of three well-known data mining techniques (CART, TreeNet and Random Forest) for predicting software reliability. We evaluate and compare the performance of proposed models with Cascade Correlation Neural Network (CCNN) using sixteen empirical databases from the Data and Analysis Center for Software. The goal of our study is to help project managers to concentrate their testing efforts to minimize the software failures in order to improve the reliability of the software systems. Two performance measures, Normalized Root Mean Squared Error (NRMSE) and Mean Absolute Errors (MAE), illustrate that CART model is accurate than the models predicted using Random Forest, TreeNet and CCNN in all datasets used in our study. Finally, we conclude that such methods can help in reliability prediction using real-life failure datasets.

Keywords: Classification, Cascade Correlation Neural Network, Random Forest, Software reliability, TreeNet.

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Authors: Yasir M. Alyazichi, Brian G. Jones, Errol McLean, Hamd N. Altalyan, Ali K. M. Al-Nasrawi

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The main purpose of this study is to assess the sediment quality and potential ecological risk in marine sediments in Gymea Bay located in south Sydney, Australia. A total of 32 surface sediment samples were collected from the bay. Current track trajectories and velocities have also been measured in the bay. The resultant trace elements were compared with the adverse biological effect values Effect Range Low (ERL) and Effect Range Median (ERM) classifications. The results indicate that the average values of chromium, arsenic, copper, zinc, and lead in surface sediments all reveal low pollution levels and are below ERL and ERM values. The highest concentrations of trace elements were found close to discharge points and in the inner bay, and were linked with high percentages of clay minerals, pyrite and organic matter, which can play a significant role in trapping and accumulating these elements. The lowest concentrations of trace elements were found to be on the shoreline of the bay, which contained high percentages of sand fractions. It is postulated that the fine particles and trace elements are disturbed by currents and tides, then transported and deposited in deeper areas. The current track velocities recorded in Gymea Bay had the capability to transport fine particles and trace element pollution within the bay. As a result, hydrodynamic measurements were able to provide useful information and to help explain the distribution of sedimentary particles and geochemical properties. This may lead to knowledge transfer to other bay systems, including those in remote areas. These activities can be conducted at a low cost, and are therefore also transferrable to developing countries. The advent of portable instruments to measure trace elements in the field has also contributed to the development of these lower cost and easily applied methodologies available for use in remote locations and low-cost economies.

Keywords: Current track velocities, Gymea Bay, surface sediments, trace elements.

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Authors: Peter Odero Akuon

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A rain cell ratio model is proposed that computes attenuation of the smallest rain cell which represents the maximum rain rate value i.e. the cell size when rainfall rate is exceeded 0.01% of the time, R_0.01 and predicts attenuation for other cells as the ratio with this maximum. This model incorporates the dependence of the path factor r on the ellipsoidal path variation of the Fresnel zone at different frequencies. In addition, the inhomogeneity of rainfall is modeled by a rain drop packing density factor. In order to derive the model, two empirical methods that can be used to find rain cell size distribution D_c are presented. Subsequently, attenuation measurements from different climatic zones for terrestrial radio links with frequencies F in the range 7-38 GHz are used to test the proposed model. Prediction results show that the path factor computed from the rain cell ratio technique has improved reliability when compared with other path factor and effective rain rate models, including the current ITU-R 530-15 model of 2013.

Keywords: Packing density of rain drops, prediction model, rain attenuation, rain cell ratio technique.

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Authors: Raeesa Moolla, Ryan S. Johnson

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Air travel, and the use of airports, has experienced proliferative growth in the past few decades, resulting in the concomitant release of air pollutants. Air pollution needs to be monitored because of the known relationship between exposure to air pollutants and increased adverse effects on human health. This study monitored a group of volatile organic compounds (VOCs); specifically BTEX (viz. benzene, toluene, ethyl-benzene and xylenes), as many are detrimental to human health. Through the use of passive sampling methods, the spatial variability of BTEX within an international airport was investigated, in order to determine ‘hotspots’ where occupational exposure to BTEX may be intensified. The passive sampling campaign revealed BTEX_total concentrations ranged between 12.95–124.04 µg m^-3. Furthermore, BTEX concentrations were dispersed heterogeneously within the airport. Due to the slow wind speeds recorded (1.13 m.s^-1); the hotspots were located close to their main BTEX sources. The main hotspot was located over the main apron of the airport. Employees working in this area may be chronically exposed to these emissions, which could be potentially detrimental to their health.

Keywords: Air pollution, air quality, hotspot monitoring, volatile organic compounds.

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Authors: Mahmoud R. Shaghaghian

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Amount of dissolve oxygen in a river has a great direct affect on aquatic macroinvertebrates and this would influence on the region ecosystem indirectly. In this paper it is tried to predict dissolved oxygen in rivers by employing an easy Fuzzy Logic Modeling, Wang Mendel method. This model just uses previous records to estimate upcoming values. For this purpose daily and hourly records of eight stations in Au Sable watershed in Michigan, United States are employed for 12 years and 50 days period respectively. Calculations indicate that for long period prediction it is better to increase input intervals. But for filling missed data it is advisable to decrease the interval. Increasing partitioning of input and output features influence a little on accuracy but make the model too time consuming. Increment in number of input data also act like number of partitioning. Large amount of train data does not modify accuracy essentially, so, an optimum training length should be selected.

Keywords: Dissolved oxygen, Au Sable, fuzzy logic modeling, Wang Mendel.

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Authors: Mochamad Chalid, Imam Prabowo

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Today, the pollution due to non-degradable material such as plastics, has led to studies about the development of environmental-friendly material. Because of biodegradability obtained from natural sources, polylactid acid (PLA) and ijuk fiber are interesting to modify into a composite. This material is also expected to reduce the impact of environmental pollution. Surface modification of ijuk fiber through alkalinization with 0.25 M NaOH solution for 30 minutes was aimed to enhance its compatibility to PLA, in order to improve properties of the composite such as the mechanical properties. Alkalinization of the ijuk fibers annihilates some surface components such as lignin, wax and hemicelloluse, so the pore on the surface clearly appeared, decreasing of the density and diameter of the ijuk fibers. The change of the ijuk fiber properties leads to increase the mechanical properties of PLA composites reinforced the ijuk fibers through strengthening of the mechanical interlocking with the PLA matrix. An addition to enhance the distribution of the fibers in the PLA matrix, the stirring during DCM solvent evaporation from the mixture of the ijuk fibers and the dissolved-PLA can reduce amount of the trapped-voids and fibers pull-out phenomena, which can decrease the mechanical properties of the composite.

Keywords: Polylactic acid, Arenga pinnata, alkalinization, compatibility, adhesion, morphology, mechanical properties, volume fraction, distributiom.

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Authors: Sanja Pfeifer, Nataša Šarlija, Marina Jeger, Ana Bilandžić

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The purpose of this study is to explore the different facets of growth among micro, small and medium-sized firms in Croatia and to analyze the differences between models designed for all micro, small and medium-sized firms and those in creative industries. Three growth prediction models were designed and tested using the growth of sales, employment and assets of the company as dependent variables. The key drivers of sales growth are: prudent use of cash, industry affiliation and higher share of intangible assets. Growth of assets depends on retained profits, internal and external sources of financing, as well as industry affiliation. Growth in employment is closely related to sources of financing, in particular, debt and it occurs less frequently than growth in sales and assets. The findings confirm the assumption that growth strategies of small and medium-sized enterprises (SMEs) in creative industries have specific differences in comparison to SMEs in general. Interestingly, only 2.2% of growing enterprises achieve growth in employment, assets and sales simultaneously.

Keywords: Creative industries, growth prediction model, growth determinants, growth measures.

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Authors: Hakim Aguedal, Abdelkader Iddou, Abdallah Aziz, Djillali Reda Merouani, Ferhat Bensaleh, Saleh Bensadek

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The textile industry produces high amount of colored effluent each year. The management or treatment of these discharges depends on the applied techniques. Adsorption is one of wastewater treatment techniques destined to treat this kind of pollution, and the performance and efficiency predominantly depend on the nature of the adsorbent used. Therefore, scientific research is directed towards the development of new materials using different physical and chemical treatments to improve their adsorption capacities. In the same perspective, we looked at the effect of the heat treatment on the effectiveness of diatomite, which is found in abundance in Algeria. The textile dye Orange Bezaktiv (SRL-150) which is used as organic pollutants in this study is provided by the textile company SOITEXHAM in Oran city (west Algeria). The effect of different physicochemical parameters on the adsorption of SRL-150 on natural and modified diatomite is studied, and the results of the kinetics and adsorption isotherms were modeled.

Keywords: Wastewater treatment, diatomite, adsorption, dye pollution, kinetic, Isotherm.

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Authors: Azmi Ibrahim, Nor Azan Mat Zin, Noraidah Sahari Ashaari

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Dengue fever is prevalent in Malaysia with numerous cases including mortality recorded over the years. Public education on the prevention of the desease through various means has been carried out besides the enforcement of legal means to eradicate Aedes mosquitoes, the dengue vector breeding ground. Hence, other means need to be explored, such as predicting the seasonal peak period of the dengue outbreak and identifying related climate factors contributing to the increase in the number of mosquitoes. Simulation model can be employed for this purpose. In this study, we created a simulation of system dynamic to predict the spread of dengue outbreak in Hulu Langat, Selangor Malaysia. The prototype was developed using STELLA 9.1.2 software. The main data input are rainfall, temperature and denggue cases. Data analysis from the graph showed that denggue cases can be predicted accurately using these two main variables- rainfall and temperature. However, the model will be further tested over a longer time period to ensure its accuracy, reliability and efficiency as a prediction tool for dengue outbreak.

Keywords: dengue fever, prediction, system dynamic, simulation

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Authors: O. Belalia Douma, B. Boukhatem, M. Ghrici

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Self-compacting concrete (SCC) developed in Japan in the late 80s has enabled the construction industry to reduce demand on the resources, improve the work condition and also reduce the impact of environment by elimination of the need for compaction. Fuzzy logic (FL) approaches has recently been used to model some of the human activities in many areas of civil engineering applications. Especially from these systems in the model experimental studies, very good results have been obtained. In the present study, a model for predicting compressive strength of SCC containing various proportions of fly ash, as partial replacement of cement has been developed by using Fuzzy Inference System (FIS). For the purpose of building this model, a database of experimental data were gathered from the literature and used for training and testing the model. The used data as the inputs of fuzzy logic models are arranged in a format of five parameters that cover the total binder content, fly ash replacement percentage, water content, superplasticizer and age of specimens. The training and testing results in the fuzzy logic model have shown a strong potential for predicting the compressive strength of SCC containing fly ash in the considered range.

Keywords: Self-compacting concrete, fly ash, strength prediction, fuzzy logic.

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Authors: Rafał Adamczak

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State-of-the-art methods for secondary structure (Porter, Psi-PRED, SAM-T99sec, Sable) and solvent accessibility (Sable, ACCpro) predictions use evolutionary profiles represented by the position specific scoring matrix (PSSM). It has been demonstrated that evolutionary profiles are the most important features in the feature space for these predictions. Unfortunately applying PSSM matrix leads to high dimensional feature spaces that may create problems with parameter optimization and generalization. Several recently published suggested that applying feature extraction for the PSSM matrix may result in improvements in secondary structure predictions. However, none of the top performing methods considered here utilizes dimensionality reduction to improve generalization. In the present study, we used simple and fast methods for features selection (t-statistics, information gain) that allow us to decrease the dimensionality of PSSM matrix by 75% and improve generalization in the case of secondary structure prediction compared to the Sable server.

Keywords: Secondary structure prediction, feature selection, position specific scoring matrix.

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Authors: Philip Symonds, Jon Taylor, Zaid Chalabi, Michael Davies

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Average temperatures worldwide are expected to continue to rise. At the same time, major cities in developing countries are becoming increasingly populated and polluted. Governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of a model, which is able to estimate the occupant exposure to extreme temperatures and high air pollution within domestic buildings. Building physics simulations were performed using the EnergyPlus building physics software. An accurate metamodel is then formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) have been compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus.

Keywords: Neural Networks, Radial Basis Functions, Metamodelling, Python machine learning libraries.

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Authors: Majid Mirzaei, Saeid Samiezadeh , Abbas Khodadadi, Mohammad R. Ghazavi

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This paper presents a novel method for prediction of the mechanical behavior of proximal femur using the general framework of the quantitative computed tomography (QCT)-based finite element Analysis (FEA). A systematic imaging and modeling procedure was developed for reliable correspondence between the QCT-based FEA and the in-vitro mechanical testing. A speciallydesigned holding frame was used to define and maintain a unique geometrical reference system during the analysis and testing. The QCT images were directly converted into voxel-based 3D finite element models for linear and nonlinear analyses. The equivalent plastic strain and the strain energy density measures were used to identify the critical elements and predict the failure patterns. The samples were destructively tested using a specially-designed gripping fixture (with five degrees of freedom) mounted within a universal mechanical testing machine. Very good agreements were found between the experimental and the predicted failure patterns and the associated load levels.

Keywords: Bone, Osteoporosis, Noninvasive methods, Failure Analysis

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Authors: Dhananjay C. Joshi, Jung-Hsin Lin

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Protein-protein interactions (PPI) play a crucial role in many biological processes such as cell signalling, transcription, translation, replication, signal transduction, and drug targeting, etc. Structural information about protein-protein interaction is essential for understanding the molecular mechanisms of these processes. Structures of protein-protein complexes are still difficult to obtain by biophysical methods such as NMR and X-ray crystallography, and therefore protein-protein docking computation is considered an important approach for understanding protein-protein interactions. However, reliable prediction of the protein-protein complexes is still under way. In the past decades, several grid-based docking algorithms based on the Katchalski-Katzir scoring scheme were developed, e.g., FTDock, ZDOCK, HADDOCK, RosettaDock, HEX, etc. However, the success rate of protein-protein docking prediction is still far from ideal. In this work, we first propose a more practical measure for evaluating the success of protein-protein docking predictions,the rate of first success (RFS), which is similar to the concept of mean first passage time (MFPT). Accordingly, we have assessed the ZDOCK bound and unbound benchmarks 2.0 and 3.0. We also createda new benchmark set for protein-protein docking predictions, in which the complexes have experimentally determined binding affinity data. We performed free energy calculation based on the solution of non-linear Poisson-Boltzmann equation (nlPBE) to improve the binding mode prediction. We used the well-studied thebarnase-barstarsystem to validate the parameters for free energy calculations. Besides,thenlPBE-based free energy calculations were conducted for the badly predicted cases by ZDOCK and ZRANK. We found that direct molecular mechanics energetics cannot be used to discriminate the native binding pose from the decoys.Our results indicate that nlPBE-based calculations appeared to be one of the promising approaches for improving the success rate of binding pose predictions.

Keywords: protein-protein docking, protein-protein interaction, molecular mechanics energetics, Poisson-Boltzmann calculations

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Authors: Edikan E. Akpanibah, Ogunmodimu Dupe Catherine

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The relevance of geometric Brownian motion (GBM) in modelling the behaviour of stock market prices (SMP) cannot be over emphasized taking into consideration the volatility of the SMP. Consequently, there is need to investigate how GBM models are being estimated and used in financial market to predict SMP. To achieve this, the GBM estimation and its application to the SMP of some selected companies are studied. The normal and log-normal distributions were used to determine the expected value, variance and co-variance. Furthermore, the GBM model was used to predict the SMP of some selected companies over a period of time and the mean absolute percentage error (MAPE) were calculated and used to determine the accuracy of the GBM model in predicting the SMP of the four companies under consideration. It was observed that for all the four companies, their MAPE values were within the region of acceptance. Also, the MAPE values of our data were compared to an existing literature to test the accuracy of our prediction with respect to time of investment. Finally, some numerical simulations of the graphs of the SMP, expectations and variance of the four companies over a period of time were presented using MATLAB programming software.

Keywords: Stock Market, Geometric Brownian Motion, normal and log-normal distribution, mean absolute percentage error.

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Authors: M. R. Dahmardeh Ghaleno, M. Nohtani, S. Khaledi

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Flood simulation and prediction is one of the most active research areas in surface water management. WetSpa is a distributed, continuous, and physical model with daily or hourly time step that explains precipitation, runoff, and evapotranspiration processes for both simple and complex contexts. This model uses a modified rational method for runoff calculation. In this model, runoff is routed along the flow path using Diffusion-Wave equation which depends on the slope, velocity, and flow route characteristics. Golestan Dam Basin is located in Golestan province in Iran and it is passing over coordinates 55° 16´ 50" to 56° 4´ 25" E and 37° 19´ 39" to 37° 49´ 28"N. The area of the catchment is about 224 km², and elevations in the catchment range from 414 to 2856 m at the outlet, with average slope of 29.78%. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. Drawing upon Nash-Sutcliffe model efficiency coefficient for calibration periodic model estimated daily hydrographs and maximum flow rate with an accuracy up to 59% and 80.18%, respectively.

Keywords: Watershed simulation, WetSpa, stream flow, flood prediction.

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Authors: Akram M. Othman, May H. Riadh

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This research work is aimed at speech recognition using scaly neural networks. A small vocabulary of 11 words were established first, these words are “word, file, open, print, exit, edit, cut, copy, paste, doc1, doc2". These chosen words involved with executing some computer functions such as opening a file, print certain text document, cutting, copying, pasting, editing and exit. It introduced to the computer then subjected to feature extraction process using LPC (linear prediction coefficients). These features are used as input to an artificial neural network in speaker dependent mode. Half of the words are used for training the artificial neural network and the other half are used for testing the system; those are used for information retrieval. The system components are consist of three parts, speech processing and feature extraction, training and testing by using neural networks and information retrieval. The retrieve process proved to be 79.5-88% successful, which is quite acceptable, considering the variation to surrounding, state of the person, and the microphone type.

Keywords: Feature extraction, Liner prediction coefficients, neural network, Speech Recognition, Scaly ANN.

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Authors: Nazli Akhlaghinia, Ali Rajabzadeh Ghatari

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In today's world, with increasing environmental awareness, firms are facing severe pressure from various stakeholders, including the government and customers, to reduce their harmful effects on the environment. Over the past few decades, the increasing effects of global warming, climate change, waste, and air pollution have increased the global attention of experts to the issue of the green supply chain and led them to the optimal solution for greenery. Green supply chain management (GSCM) plays an important role in motivating the sustainability of the organization. With increasing environmental concerns, the main objective of the research is to use system thinking methodology and Vensim software for designing a dynamic system model for green supply chain and observing behaviors. Using this methodology, we look for the effects of a green supply chain structure on the behavioral dynamics of output variables. We try to simulate the complexity of GSCM in a period of 30 months and observe the complexity of behaviors of variables including sustainability, providing green products, and reducing energy consumption, and consequently reducing sample pollution.

Keywords: Supply chain management, green supply chain management, system dynamics, energy consumption.

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Authors: Woo-tai Jung, Sung-yong Choi, Young-hwan Park

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The composite pavement system considered in this paper is composed of a functional surface layer, a fiber reinforced asphalt middle layer and a fiber reinforced lean concrete base layer. The mix design of the fiber reinforced lean concrete corresponds to the mix composition of conventional lean concrete but reinforced by fibers. The quasi-absence of research on the durability or long-term performances (fatigue, creep, etc.) of such mix design stresses the necessity to evaluate experimentally the long-term characteristics of this layer composition. This study tests the creep characteristics as one of the long-term characteristics of the fiber reinforced lean concrete layer for composite pavement using a new creep device. The test results reveal that the lean concrete mixed with fiber reinforcement and fly ash develops smaller creep than the conventional lean concrete. The results of the application of the CEB-FIP prediction equation indicate that a modified creep prediction equation should be developed to fit with the new mix design of the layer.

Keywords: Creep, Lean concrete, Pavement, Fiber reinforced concrete, Base.

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Authors: Woo-Tai Jung, Sung-Yong Choi, Young-Hwan Park

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The composite pavement system considered in this paper is composed of a functional surface layer, a fiber reinforced asphalt middle layer and a fiber reinforced lean concrete base layer. The mix design of the fiber reinforced lean concrete corresponds to the mix composition of conventional lean concrete but reinforced by fibers. The quasi-absence of research on the durability or long-term performances (fatigue, creep, etc.) of such mix design stresses the necessity to evaluate experimentally the long-term characteristics of this layer composition. This study tests the creep characteristics as one of the long-term characteristics of the fiber reinforced lean concrete layer for composite pavement using a new creep device. The test results reveal that the lean concrete mixed with fiber reinforcement and fly ash develops smaller creep than the conventional lean concrete. The results of the application of the CEB-FIP prediction equation indicate that a modified creep prediction equation should be developed to fit with the new mix design of the layer.

Keywords: Creep, Lean concrete, Pavement, Fiber reinforced concrete, Base.

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Authors: Emel Kızılkaya Aydogan, Mihrimah Ozmen, Yılmaz Delice

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In recent days, there is a change and the ongoing development of the telecommunications sector in the global market. In this sector, churn analysis techniques are commonly used for analysing why some customers terminate their service subscriptions prematurely. In addition, customer churn is utmost significant in this sector since it causes to important business loss. Many companies make various researches in order to prevent losses while increasing customer loyalty. Although a large quantity of accumulated data is available in this sector, their usefulness is limited by data quality and relevance. In this paper, a cost-sensitive feature selection framework is developed aiming to obtain the feature reducts to predict customer churn. The framework is a cost based optional pre-processing stage to remove redundant features for churn management. In addition, this cost-based feature selection algorithm is applied in a telecommunication company in Turkey and the results obtained with this algorithm.

Keywords: Churn prediction, data mining, decision-theoretic rough set, feature selection.

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Authors: Ana Vieira, Fernando Lau, João Pedro Mortágua, Luís Cruz, Rui Santos

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The air transport impact on environment is more than ever a limitative obstacle to the aeronautical industry continuous growth. Over the last decades, considerable effort has been carried out in order to obtain quieter aircraft solutions, whether by changing the original design or investigating more silent maneuvers. The noise propagated by rotating surfaces is one of the most important sources of annoyance, being present in most aerial vehicles. Bearing this is mind, CEIIA developed a new computational chain for noise prediction with in-house software tools to obtain solutions in relatively short time without using excessive computer resources. This work is based on the new acoustic tool, which aims to predict the rotor noise generated during steady and maneuvering flight, making use of the flexibility of the C language and the advantages of GPU programming in terms of velocity. The acoustic tool is based in the Formulation 1A of Farassat, capable of predicting two important types of noise: the loading and thickness noise. The present work describes the most important features of the acoustic tool, presenting its most relevant results and framework analyses for helicopters and UAV quadrotors.

Keywords: Rotor noise, acoustic tool, GPU Programming, UAV noise.

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Authors: Do Hoai Nam, Keiko Udo, Akira Mano

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This paper presents the combination of different precipitation data sets and the distributed hydrological model, in order to examine the flood runoff reproductivity of scattered observation catchments. The precipitation data sets were obtained from observation using rain-gages, satellite based estimate (TRMM), and numerical weather prediction model (NWP), then were coupled with the super tank model. The case study was conducted in three basins (small, medium, and large size) located in Central Vietnam. Calculated hydrographs based on ground observation rainfall showed best fit to measured stream flow, while those obtained from TRMM and NWP showed high uncertainty of peak discharges. However, calculated hydrographs using the adjusted rainfield depicted a promising alternative for the application of TRMM and NWP in flood modeling for scattered observation catchments, especially for the extension of forecast lead time.

Keywords: Flood forecast, rainfall-runoff model, satellite rainfall estimate, numerical weather prediction, quantitative precipitation forecasting.

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Authors: S. A. Nta, M. J. Ayotamuno, A. H. Igoni, R. N. Okparanma

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The work sought to understand the pattern of movement of contaminant from a continuous point source through soil. The soil used was sandy-loam in texture. The contaminant used was municipal solid waste landfill leachate, introduced as a point source through an entry point located at the center of top layer of the soil tank. Analyses were conducted after maturity periods of 50 and 80 days. The maximum change in chemical concentration was observed on soil samples at a radial distance of 0.25 m. Finite element approximation based model was used to assess the future prediction, management and remediation in the polluted area. The actual field data collected for the case study were used to calibrate the modeling and thus simulated the flow pattern of the pollutants through soil. MATLAB R2015a was used to visualize the flow of pollutant through the soil. Dispersion coefficient at 0.25 and 0.50 m radial distance from the point of application of leachate shows a measure of the spreading of a flowing leachate due to the nature of the soil medium, with its interconnected channels distributed at random in all directions. Surface plots of metals on soil after maturity period of 80 days shows a functional relationship between a designated dependent variable (Y), and two independent variables (X and Z). Comparison of measured and predicted profile transport along the depth after 50 and 80 days of leachate application and end of the experiment shows that there were no much difference between the predicted and measured concentrations as they were all lying close to each other. For the analysis of contaminant transport, finite difference approximation based model was very effective in assessing the future prediction, management and remediation in the polluted area. The experiment gave insight into the most likely pattern of movement of contaminant as a result of continuous percolations of the leachate on soil. This is important for contaminant movement prediction and subsequent remediation of such soils.

Keywords: Contaminant, dispersion, point or leaky source, surface plot, soil.

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Authors: Tea Poklepović, Zdravka Aljinović, Branka Marasović

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Due to the importance of yield curve and its estimation it is inevitable to have valid methods for yield curve forecasting in cases when there are scarce issues of securities and/or week trade on a secondary market. Therefore in this paper, after the estimation of weekly yield curves on Croatian financial market from October 2011 to August 2012 using Nelson-Siegel and Svensson models, yield curves are forecasted using Vector autoregressive model and Neural networks. In general, it can be concluded that both forecasting methods have good prediction abilities where forecasting of yield curves based on Nelson Siegel estimation model give better results in sense of lower Mean Squared Error than forecasting based on Svensson model Also, in this case Neural networks provide slightly better results. Finally, it can be concluded that most appropriate way of yield curve prediction is Neural networks using Nelson-Siegel estimation of yield curves.

Keywords: Nelson-Siegel model, Neural networks, Svensson model, Vector autoregressive model, Yield curve.

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Authors: Seyed Hossein Iranmanesh, Zahra Mokhtari

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Estimation time and cost of work completion in a project and follow up them during execution are contributors to success or fail of a project, and is very important for project management team. Delivering on time and within budgeted cost needs to well managing and controlling the projects. To dealing with complex task of controlling and modifying the baseline project schedule during execution, earned value management systems have been set up and widely used to measure and communicate the real physical progress of a project. But it often fails to predict the total duration of the project. In this paper data mining techniques is used predicting the total project duration in term of Time Estimate At Completion-EAC (t). For this purpose, we have used a project with 90 activities, it has updated day by day. Then, it is used regular indexes in literature and applied Earned Duration Method to calculate time estimate at completion and set these as input data for prediction and specifying the major parameters among them using Clem software. By using data mining, the effective parameters on EAC and the relationship between them could be extracted and it is very useful to manage a project with minimum delay risks. As we state, this could be a simple, safe and applicable method in prediction the completion time of a project during execution.

Keywords: Data Mining Techniques, Earned Duration Method, Earned Value, Estimate At Completion.

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Authors: Nahid Ardalani, Ahmadreza Khoogar, H. Roohi

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In this paper we compare the response of linear and nonlinear neural network-based prediction schemes in prediction of received Signal-to-Interference Power Ratio (SIR) in Direct Sequence Code Division Multiple Access (DS/CDMA) systems. The nonlinear predictor is Multilayer Perceptron MLP and the linear predictor is an Adaptive Linear (Adaline) predictor. We solve the problem of complexity by using the Minimum Mean Squared Error (MMSE) principle to select the optimal predictors. The optimized Adaline predictor is compared to optimized MLP by employing noisy Rayleigh fading signals with 1.8 GHZ carrier frequency in an urban environment. The results show that the Adaline predictor can estimates SIR with the same error as MLP when the user has the velocity of 5 km/h and 60 km/h but by increasing the velocity up-to 120 km/h the mean squared error of MLP is two times more than Adaline predictor. This makes the Adaline predictor (with lower complexity) more suitable than MLP for closed-loop power control where efficient and accurate identification of the time-varying inverse dynamics of the multi path fading channel is required.

Keywords: Power control, neural networks, DS/CDMA mobilecommunication systems.

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Authors: Saeed Sayyad Hagh Shomar

Abstract:

Traffic congestion pricing – as a strategy in travel demand management in urban areas to reduce traffic congestion, air pollution and noise pollution – has drawn many attentions towards itself. Unlike the satisfying findings in this method, there are still problems in determining the best functional congestion pricing scheme with regard to the situation. The so-called problems in this process will result in further complications and even the scheme failure. That is why having proper knowledge of the significance of congestion pricing schemes and the effective factors in choosing them can lead to the success of this strategy. In this study, first, a variety of traffic congestion pricing schemes and their components are introduced; then, their functional usage is discussed. Next, by analyzing and comparing the barriers, limitations and advantages, the selection criteria of pricing schemes are described. The results, accordingly, show that the selection of the best scheme depends on various parameters. Finally, based on examining the effective parameters, it is concluded that the implementation of area-based schemes (cordon and zonal) has been more successful in non-diversion of traffic. That is considering the topology of the cities and the fact that traffic congestion is often created in the city centers, area-based schemes would be notably functional and appropriate.

Keywords: Congestion pricing, demand management, flat toll, variable toll.

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Authors: Worachat Wannawong, Usa W. Humphries, Prungchan Wongwises, Suphat Vongvisessomjai, Wiriya Lueangaram

Abstract:

A numerical analysis of wave and hydrodynamic models is used to investigate the influence of WAve and Storm Surge (WASS) in the regional and coastal zones. The numerical analyzed system consists of the WAve Model Cycle 4 (WAMC4) and the Princeton Ocean Model (POM) which used to solve the energy balance and primitive equations respectively. The results of both models presented the incorporated surface wave in the regional zone affected the coastal storm surge zone. Specifically, the results indicated that the WASS generally under the approximation is not only the peak surge but also the coastal water level drop which can also cause substantial impact on the coastal environment. The wave–induced surface stress affected the storm surge can significantly improve storm surge prediction. Finally, the calibration of wave module according to the minimum error of the significant wave height (Hs) is not necessarily result in the optimum wave module in the WASS analyzed system for the WASS prediction.

Keywords: energy balance equation, numerical analysis, primitiveequation, storm surge, wave.

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Authors: M.O. Makhmalbaf, F. Mohajeri Nav, M. Zabihi Samani

Abstract:

The recent drive for use of performance-based methodologies in design and assessment of structures in seismic areas has significantly increased the demand for the development of reliable nonlinear inelastic static pushover analysis tools. As a result, the adaptive pushover methods have been developed during the last decade, which unlike their conventional pushover counterparts, feature the ability to account for the effect that higher modes of vibration and progressive stiffness degradation might have on the distribution of seismic storey forces. Even in advanced pushover methods, little attention has been paid to the Unsymmetric structures. This study evaluates the seismic demands for three dimensional Unsymmetric-Plan buildings determined by the Displacement-based Adaptive Pushover (DAP) analysis, which has been introduced by Antoniou and Pinho [2004]. The capability of DAP procedure in capturing the torsional effects due to the irregularities of the structures, is investigated by comparing its estimates to the exact results, obtained from Incremental Dynamic Analysis (IDA). Also the capability of the procedure in prediction the seismic behaviour of the structure is discussed.

Keywords: Nonlinear static procedures, Unsymmetric-PlanBuildings, Torsional effects, IDA.

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