Search results for: modelling techniques

Authors: Agarana Michael C., Akinlabi Esther T., Pule Kholopane

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Innovation in manufacturing process technologies and associated product design affects the prospects for manufacturing today and in near future. In this study some theoretical methods, useful as tools in advanced manufacturing, are considered. In particular, some basic Mathematical, Operational Research, Heuristic, and Statistical techniques are discussed. These techniques/methods are very handy in many areas of advanced manufacturing processes, including process planning optimization, modelling and analysis. Generally the production rate requires the application of Mathematical methods. The Emerging Trends Processes in Advanced Manufacturing can be enhanced by using Mathematical Modelling and Optimization techniques.

Keywords: mathematical modelling, optimization, emerging trends, advanced manufacturing

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Authors: Oluwaseun K. Oyebode, Josiah A. Adeyemo

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Hydrological modelling plays a crucial role in the planning and management of water resources, most especially in water stressed regions where the need to effectively manage the available water resources is of critical importance. However, due to the complex, nonlinear and dynamic behaviour of hydro-climatic interactions, achieving reliable modelling of water resource systems and accurate projection of hydrological parameters are extremely challenging. Although a significant number of modelling techniques (process-based and data-driven) have been developed and adopted in that regard, the field of hydrological modelling is still considered as one that has sluggishly progressed over the past decades. This is majorly as a result of the identification of some degree of uncertainty in the methodologies and results of techniques adopted. In recent times, evolutionary computation (EC) techniques have been developed and introduced in response to the search for efficient and reliable means of providing accurate solutions to hydrological related problems. This paper presents a comprehensive review of the underlying principles, methodological needs and applications of a promising evolutionary computation modelling technique – genetic programming (GP). It examines the specific characteristics of the technique which makes it suitable to solving hydrological modelling problems. It discusses the opportunities inherent in the application of GP in water related-studies such as rainfall estimation, rainfall-runoff modelling, streamflow forecasting, sediment transport modelling, water quality modelling and groundwater modelling among others. Furthermore, the means by which such opportunities could be harnessed in the near future are discussed. In all, a case for total embracement of GP and its variants in hydrological modelling studies is made so as to put in place strategies that would translate into achieving meaningful progress as it relates to modelling of water resource systems, and also positively influence decision-making by relevant stakeholders.

Keywords: computational modelling, evolutionary algorithms, genetic programming, hydrological modelling

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Authors: Mohammad Nadeem, Haider Banka, R. Venugopal

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Granulation or agglomeration is a size enlargement process to transform the fine particulates into larger aggregates since the fine size of available materials and minerals poses difficulty in their utilization. Though a long list of methods is available in the literature for the modeling of granulation process to facilitate the in-depth understanding and interpretation of the system, there is still scope of improvements using novel tools and techniques. Intelligent techniques, such as artificial neural network, fuzzy logic, self-organizing map, support vector machine and others, have emerged as compelling alternatives for dealing with imprecision and complex non-linearity of the systems. The present study tries to review the applications of intelligent techniques in the modeling of size enlargement process for fine materials.

Keywords: fine material, granulation, intelligent technique, modelling

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Authors: Kisan Sarda, Bhavika Shingote

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This paper aims to evaluate regression modelling for prediction of Energy storage of solar photovoltaic (PV) system using Semi parametric regression techniques because there are some parameters which are known while there are some unknown parameters like humidity, dust etc. Here irradiation of solar energy is different for different places on the basis of Latitudes, so by finding out areas which give more storage we can implement PV systems at those places and our need of energy will be fulfilled. This regression modelling is done for daily, monthly and seasonal prediction of solar energy storage. In this, we have used R modules for designing the algorithm. This algorithm will give the best comparative results than other regression models for the solar PV cell energy storage.

Keywords: semi parametric regression, photovoltaic (PV) system, regression modelling, irradiation

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Authors: Shawn Michael Condon

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Current research on teaching expressivity mainly involves instrumentalists. This study focuses on choral singers’ preference of priming techniques based on four methods for teaching expressivity. 112 choral singers answered the survey about their preferred methods for priming expressivity (vocal modelling, using metaphor, tapping into felt emotions, and drawing on past experiences) in three conditions (active, passive, and instructor). Analysis revealed higher preference for drawing on past experience among more experienced singers. The most preferred technique in the passive and instructor roles was vocal modelling, with metaphors and tapping into felt emotions favoured in an active role. Priming techniques are often used in combination with other methods to enhance singing technique or expressivity and are dependent upon the situation, repertoire, and the preferences of the instructor and performer.

Keywords: emotion, expressivity, performance, singing, teaching

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Authors: Bhim Kumar Dahal

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Transportation network development in the developing country is in rapid pace. The majority of the network belongs to railway and expressway which passes through diverse topography, landform and geological conditions despite the avoidance principle during route selection. Construction of such networks demand many low to high embankment which required improvement in the foundation soil. This paper is mainly focused on the various advanced ground improvement techniques used to improve the soft soil, modelling approach and its predictability for embankments construction. The ground improvement techniques can be broadly classified in to three groups i.e. densification group, drainage and consolidation group and reinforcement group which are discussed with some case studies.  Various methods were used in modelling of the embankments from simple 1-dimensional to complex 3-dimensional model using variety of constitutive models. However, the reliability of the predictions is not found systematically improved with the level of sophistication.  And sometimes the predictions are deviated more than 60% to the monitored value besides using same level of erudition. This deviation is found mainly due to the selection of constitutive model, assumptions made during different stages, deviation in the selection of model parameters and simplification during physical modelling of the ground condition. This deviation can be reduced by using optimization process, optimization tools and sensitivity analysis of the model parameters which will guide to select the appropriate model parameters.

Keywords: cement, improvement, physical properties, strength

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Authors: Baha Hasan, Jan Wikander

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This paper describes a part of the integrating work between assembly design and assembly process planning domains (APP). The work is based, in its first stage, on modelling assembly features to support APP. A multi-layer architecture, based on feature-based modelling, is proposed to establish a dynamic and adaptable link between product design using CAD tools and APP. The proposed approach is based on deriving “specific function” features from the “generic” assembly and form features extracted from the CAD tools. A hierarchal structure from “generic” to “specific” and from “high level geometrical entities” to “low level geometrical entities” is proposed in order to integrate geometrical and assembly data extracted from geometrical and assembly modelers to the required processes and resources in APP. The feature concept, feature-based modelling, and feature recognition techniques are reviewed.

Keywords: assembly feature, assembly process planning, feature, feature-based modelling, form feature, ontology

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Authors: Serhat Tuzun, Tufan Demirel

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Multi-Criteria Decision Making (MCDM) is the modelling of real-life to solve problems we encounter. It is a discipline that aids decision makers who are faced with conflicting alternatives to make an optimal decision. MCDM problems can be classified into two main categories: Multi-Attribute Decision Making (MADM) and Multi-Objective Decision Making (MODM), based on the different purposes and different data types. Although various MADM techniques were developed for the problems encountered, their methodology is limited in modelling real-life. Moreover, objective results are hard to obtain, and the findings are generally derived from subjective data. Although, new and modified techniques are developed by presenting new approaches such as fuzzy logic; comprehensive techniques, even though they are better in modelling real-life, could not find a place in real world applications for being hard to apply due to its complex structure. These constraints restrict the development of MADM. This study aims to conduct a comprehensive analysis of preference methods in MADM and propose an approach based on information. For this purpose, a detailed literature review has been conducted, current approaches with their advantages and disadvantages have been analyzed. Then, the approach has been introduced. In this approach, performance values of the criteria are calculated in two steps: first by determining the distribution of each attribute and standardizing them, then calculating the information of each attribute as informational energy.

Keywords: literature review, multi-attribute decision making, operations research, preference method, informational energy

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Authors: Aurelia Samuel

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Socio-economic modelling approaches linked to water management have contributed to impact assessments of agricultural policies and management practices on water quality at catchment level. With an increasing interest in informing water management policy that considers complex links between socioeconomic factors, climate change, agricultural production, and water quality, several models have been developed and applied in the literature to capture these relationships. This paper offers an overview of socio-economic approaches that have been incorporated within an integrated framework. It also highlights how data gaps on socio-economic factors have been addressed using forecasting techniques. Findings of the review show that while integrated frameworks have the potential to account for complexities within dynamic systems, they generally do not provide direct, measurable financial impact of socio-economic factors on biophysical water parameters that affect water quality. The paper concludes with a recommendation that modelling framework is kept simple to make it more transparent and easier to capture the most important relationship.

Keywords: financial impact, integrated framework, socio-economic modelling, water quality

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Authors: Conrad Weber

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The modelling of railway noise over large projects areas can be very time consuming in terms of preparing the noise models and calculation time. An open-source GIS program has been utilised to assist with the modelling of operational noise levels for 675km of railway corridor. A range of GIS algorithms were utilised to break up the noise model area into manageable calculation sizes. GIS was utilised to prepare and filter a range of noise modelling inputs, including building files, land uses and ground terrain. A spreadsheet was utilised to manage the accuracy of key input parameters, including train speeds, train types, curve corrections, bridge corrections and engine notch settings. GIS was utilised to present the final noise modelling results. This paper explains the noise modelling process and how the spreadsheet and GIS were utilised to accurately model this massive project efficiently.

Keywords: noise, modeling, GIS, rail

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Authors: Dejan Tanikić, Jelena Đoković, Saša Kalinović, Miodrag Manić, Saša Ranđelović

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This paper deals with measuring and modelling of the quality of the machined surface of the metal machining process. The average surface roughness (R_a) which represents the quality of the machined part was measured during the dry turning of the AISI 4140 steel. A large number of factors with the unknown relations among them influences this parameter, and that is why mathematical modelling is extremely complicated. Different values of cutting speed, feed rate, depth of cut (cutting regime) and workpiece hardness causes different surface roughness values. Modelling with soft computing techniques may be very useful in such cases. This paper presents the usage of the fuzzy logic-based system for determining metal machining process parameter in order to find the proper values of cutting regimes.

Keywords: fuzzy logic, metal machining, process modeling, surface roughness

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Authors: Yusuf S. Dambatta, Ahmed A. D. Sarhan

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Fused deposition modelling (FDM) is one of the most prominent rapid prototyping (RP) technologies which is being used to efficiently fabricate CAD 3D geometric models. However, the process is coupled with many drawbacks, of which the surface quality of the manufactured RP parts is among. Hence, studies relating to improving the surface roughness have been a key issue in the field of RP research. In this work, a technique of modelling the surface roughness in FDM is presented. Using experimentally measured surface roughness response of the FDM parts, an ANFIS prediction model was developed to obtain the surface roughness in the FDM parts using the main critical process parameters that affects the surface quality. The ANFIS model was validated and compared with experimental test results.

Keywords: surface roughness, fused deposition modelling (FDM), adaptive neuro fuzzy inference system (ANFIS), orientation

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Authors: P. Agnivesh, P. V. Ponambala Moorthi

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Evolution of information modelling leads to the visualisation of well-organized built environment. Building Information Modelling (BIM) is considered as evolution in the off-site construction which essentially enhances and controls the present scenario of on-site construction paradigms. Promptness, sustainability and security are considered as the important characteristics of the building information modelling. Projects that uses BIM are tied firmly by technology but distributed organizationally. This allows different team members in the project to associate and integrate the works and work flows. This will in turn improve the efficiency of work breakdown structure. Internationally BIM had been accepted as modern computer aided way of information sharing by construction industry for efficient way of manipulation in order to avoid the on-site misperceptions. Even though, in developing countries like India BIM is in the phase of start and requires lot of mandates and policies to be brought about by the government for its widespread implementations. This paper reviews the current scenario of BIM worldwide and in India and suggests for the improved implementation of building modelling for Indian policy condition.

Keywords: building information modelling, Indian polity, information modelling, information sharing, mandates and policies, sustainability.

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Authors: R. Whalley

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The heat transfer modelling for a diffusion process will be considered. Difficulties in computing the time-distance dynamics of the representation will be addressed. Incomplete and irrational Laplace function will be identified as the computational issue. Alternative approaches to the response evaluation process will be provided. An illustration application problem will be presented. Graphical results confirming the theoretical procedures employed will be provided.

Keywords: heat, transfer, diffusion, modelling, computation

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Authors: Fatema Akram, Mohammad Golam Rasul, Mohammad Masud Kamal Khan, Md. Sharif Imam Ibne Amir

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A variety of routing techniques are available to develop surface runoff hydrographs from rainfall. The selection of runoff routing method is very vital as it is directly related to the type of watershed and the required degree of accuracy. There are different modelling softwares available to explore the rainfall-runoff process in urban areas. XPSTORM, a link-node based, integrated storm-water modelling software, has been used in this study for developing surface runoff hydrograph for a Golf course area located in Rockhampton in Central Queensland in Australia. Four commonly used methods, namely SWMM runoff, Kinematic wave, Laurenson, and Time-Area are employed to generate runoff hydrograph for design storm of this study area. In runoff mode of XPSTORM, the rainfall, infiltration, evaporation and depression storage for sub-catchments were simulated and the runoff from the sub-catchment to collection node was calculated. The simulation results are presented, discussed and compared. The total surface runoff generated by SWMM runoff, Kinematic wave and Time-Area methods are found to be reasonably close, which indicates any of these methods can be used for developing runoff hydrograph of the study area. Laurenson method produces a comparatively less amount of surface runoff, however, it creates highest peak of surface runoff among all which may be suitable for hilly region. Although the Laurenson hydrograph technique is widely acceptable surface runoff routing technique in Queensland (Australia), extensive investigation is recommended with detailed topographic and hydrologic data in order to assess its suitability for use in the case study area.

Keywords: ARI, design storm, IFD, rainfall temporal pattern, routing techniques, surface runoff, XPSTORM

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Authors: S. Dey, T. Mukhopadhyay, S. Adhikari

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This paper presents an exhaustive comparative investigation on sampling techniques of polynomial regression model based stochastic natural frequency of composite plates. Both individual and combined variations of input parameters are considered to map the computational time and accuracy of each modelling techniques. The finite element formulation of composites is capable to deal with both correlated and uncorrelated random input variables such as fibre parameters and material properties. The results obtained by Polynomial regression (PR) using different sampling techniques are compared. Depending on the suitability of sampling techniques such as 2k Factorial designs, Central composite design, A-Optimal design, I-Optimal, D-Optimal, Taguchi’s orthogonal array design, Box-Behnken design, Latin hypercube sampling, sobol sequence are illustrated. Statistical analysis of the first three natural frequencies is presented to compare the results and its performance.

Keywords: composite plate, natural frequency, polynomial regression model, sampling technique, uncertainty quantification

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Authors: Arun Goel

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The present paper attempts to investigate the prediction of air entrainment rate and aeration efficiency of a free over-fall jets issuing from a triangular sharp crested weir by using regression based modelling. The empirical equations, support vector machine (polynomial and radial basis function) models and the linear regression techniques were applied on the triangular sharp crested weirs relating the air entrainment rate and the aeration efficiency to the input parameters namely drop height, discharge, and vertex angle. It was observed that there exists a good agreement between the measured values and the values obtained using empirical equations, support vector machine (Polynomial and rbf) models, and the linear regression techniques. The test results demonstrated that the SVM based (Poly & rbf) model also provided acceptable prediction of the measured values with reasonable accuracy along with empirical equations and linear regression techniques in modelling the air entrainment rate and the aeration efficiency of a free over-fall jets issuing from triangular sharp crested weir. Further sensitivity analysis has also been performed to study the impact of input parameter on the output in terms of air entrainment rate and aeration efficiency.

Keywords: air entrainment rate, dissolved oxygen, weir, SVM, regression

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Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

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Authors: Zahreddine Hafsi, Manoranjan Mishra , Sami Elaoud

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Ensuring the maximum recovery rate of oil from reservoir rocks is a challenging task that requires preliminary numerical analysis of different techniques used to enhance the recovery process. After conventional oil recovery processes and in order to retrieve oil left behind after the primary recovery phase, water flooding in one of several techniques used for enhanced oil recovery (EOR). In this research work, EOR via water flooding is numerically modeled, and hydrodynamic instabilities resulted from immiscible oil-water flow in reservoir rocks are investigated. An oil reservoir is a porous medium consisted of many fractures of tiny dimensions. For modeling purposes, the oil reservoir is considered as a collection of capillary tubes which provides useful insights into how fluids behave in the reservoir pore spaces. Equations governing oil-water flow in oil reservoir rocks are developed and numerically solved following a finite element scheme. Numerical results are obtained using Comsol Multiphysics software. The two phase Darcy module of COMSOL Multiphysics allows modelling the imbibition process by the injection of water (as wetting phase) into an oil reservoir. Van Genuchten, Brooks Corey and Levrett models were considered as retention models and obtained flow configurations are compared, and the governing parameters are discussed. For the considered retention models it was found that onset of instabilities viz. fingering phenomenon is highly dependent on the capillary pressure as well as the boundary conditions, i.e., the inlet pressure and the injection velocity.

Keywords: capillary pressure, EOR process, immiscible flow, numerical modelling

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Authors: Selvakumar Ulaganathan, Ivo Couckuyt, Francesco Ferranti, Tom Dhaene, Eric Laermans

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Variable-fidelity surrogate modelling offers an efficient way to approximate function data available in multiple degrees of accuracy each with varying computational cost. In this paper, a Kriging-based variable-fidelity surrogate modelling approach is introduced to approximate such deterministic data. Initially, individual Kriging surrogate models, which are enhanced with gradient data of different degrees of accuracy, are constructed. Then these Gradient enhanced Kriging surrogate models are strategically coupled using a recursive CoKriging formulation to provide an accurate surrogate model for the highest fidelity data. While, intuitively, gradient data is useful to enhance the accuracy of surrogate models, the primary motivation behind this work is to investigate if it is also worthwhile incorporating gradient data of varying degrees of accuracy.

Keywords: Kriging, CoKriging, Surrogate modelling, Variable- fidelity modelling, Gradients

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Authors: Banan Aljumaiah

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In recent years, the construction industry has leveraged the information revolution, which makes it possible to view the entire construction process of new buildings before they are built with the advent of Building Information Modelling (BIM). Although BIM is an integration of the building model with the data and documents about the building, however, its implementation is limited to individual buildings missing the large picture of the city infrastructure. This limitation of BIM led to the birth of City Information Modelling. Three years ago, Eastern Province Municipality (EPM) in Saudi Arabia mandated that all major projects be delivered with collaborative 3D BIM. After three years of implementation, EPM started to implement City Information Modelling (CIM) as a part of the Smart City Plan to link infrastructure and public services and modelling how people move around and interact with the city. This paper demonstrates a local case study of BIM implementation in EPM and its future as a part of project management automation; the paper also highlights the ambitious plan of EPM to transform CIM towards building smart cities.

Keywords: BIM, BIM to CIM

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Authors: Aydin Azizi, Aburrahman Tanira

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The main source of energy used in this modern age is fossil fuels. There is a myriad of problems that come with the use of fossil fuels, out of which the issues with the greatest impact are its scarcity and the cost it imposes on the planet. Fossil fuels are the only plausible option for many vital functions and processes; the most important of these is transportation. Thus, using this source of energy wisely and as efficiently as possible is a must. The aim of this work was to explore utilising mathematical modelling and artificial intelligence techniques to enhance fuel consumption in passenger cars by focusing on the speed at which cars are driven. An artificial neural network with an error less than 0.05 was developed to be applied practically as to predict the rate of fuel consumption in vehicles.

Keywords: mathematical modeling, neural networks, fuel consumption, fossil fuel

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Authors: Vittorio Caffi, Maria Pignataro, Antonio Cosimo Devito, Marco Pesenti

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This paper deals with the integration of advanced digital design and modelling tools and methodologies, known as Building Information Modelling, into the traditional Academy educational programs for building engineers and architects. Nowadays, the challenge the Academy has to face is to present the new tools and their features to the pupils, making sure they acquire the proper skills in order to leverage the potential they offer also for the other courses embedded in the educational curriculum. The syllabus here presented refers to the “Drawing for building engineering”, “2D and 3D laboratory” and “3D modelling” curricula of the MSc in Building Engineering of the Politecnico di Milano. Such topics, included since the first year in the MSc program, are fundamental to give the students the instruments to master the complexity of an architectural or building engineering project with digital tools, so as to represent it in its various forms.

Keywords: BIM, BIM curricula, computational design, digital modelling

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Authors: Vishnu S. Prasad, Preeti Aghalayam

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The major harmful automobile exhausts are nitric oxide (NO) and unburned hydrocarbon (HC). Reduction of NO using unburned fuel HC as a reductant is the technique used in hydrocarbon-selective catalytic reduction (HC-SCR). In this work, we study the microkinetic modelling of NO reduction using propene as a reductant on Pt catalysts. The selectivity of NO reduction to N₂O is detected in some ranges of operating conditions, whereas the effect of inlet O₂% causes a number of changes in the feasible regimes of operation.

Keywords: microkinetic modelling, NOx, platinum on alumina catalysts, selective catalytic reduction

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Authors: Mahesh Kumar Jat, Manisha Choudhary

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Rapid population and economic growth resulted in changes in large-scale land use land cover (LULC) changes. Changes in the biophysical properties of the Earth's surface and its impact on climate are of primary concern nowadays. Different approaches, ranging from location-based relationships or modelling earth surface - atmospheric interaction through modelling techniques like surface energy balance (SEB) have been used in the recent past to examine the relationship between changes in Earth surface land cover and climatic characteristics like temperature and precipitation. A remote sensing-based model i.e., Surface Energy Balance Algorithm for Land (SEBAL), has been used to estimate the surface heat fluxes over Mahi Bajaj Sagar catchment (India) from 2001 to 2020. Landsat ETM and OLI satellite data are used to model the SEB of the area. Changes in observed precipitation and temperature, obtained from India Meteorological Department (IMD) have been correlated with changes in surface heat fluxes to understand the relative contributions of LULC change in changing these climatic variables. Results indicate a noticeable impact of LULC changes on climatic variables, which are aligned with respective changes in SEB components. Results suggest that precipitation increases at a rate of 20 mm/year. The maximum and minimum temperature decreases and increases at 0.007 ℃ /year and 0.02 ℃ /year, respectively. The average temperature increases at 0.009 ℃ /year. Changes in latent heat flux and sensible heat flux positively correlate with precipitation and temperature, respectively. Variation in surface heat fluxes influences the climate parameters and is an adequate reason for climate change. So, SEB modelling is helpful to understand the LULC change and its impact on climate.

Keywords: remote sensing, GIS, object based, classification

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Authors: Kausik Halder, Nabendu Chaki, Ranjan Dasgupta

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We present a modelling framework that supports the engineering of early requirements specifications for design of learner centric dynamic Learning Management System. The framework is based on i* modelling tool and Means End Analysis, that adopts primitive concepts for modelling early requirements (such as actor, goal, and strategic dependency). We show how pedagogical and computational requirements for designing a learner centric Learning Management system can be adapted for the automatic early requirement engineering specifications. Finally, we presented a model on a Learner Quanta based adaptive Courseware. Our early requirement analysis shows that how means end analysis reveals gaps and inconsistencies in early requirements specifications that are by no means trivial to discover without the help of formal analysis tool.

Keywords: adaptive courseware, early requirement engineering, means end analysis, organizational modelling, requirement modelling

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Authors: I. V. Pinto, M. R. Sooriyarachchi

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It can be frequently observed that the data arising in our environment have a hierarchical or a nested structure attached with the data. Multilevel modelling is a modern approach to handle this kind of data. When multilevel modelling is combined with a binary response, the estimation methods get complex in nature and the usual techniques are derived from quasi-likelihood method. The estimation methods which are compared in this study are, marginal quasi-likelihood (order 1 & order 2) (MQL1, MQL2) and penalized quasi-likelihood (order 1 & order 2) (PQL1, PQL2). A statistical model is of no use if it does not reflect the given dataset. Therefore, checking the adequacy of the fitted model through a goodness-of-fit (GOF) test is an essential stage in any modelling procedure. However, prior to usage, it is also equally important to confirm that the GOF test performs well and is suitable for the given model. This study assesses the suitability of the GOF test developed for binary response multilevel models with respect to the method used in model estimation. An extensive set of simulations was conducted using MLwiN (v 2.19) with varying number of clusters, cluster sizes and intra cluster correlations. The test maintained the desirable Type-I error for models estimated using PQL2 and it failed for almost all the combinations of MQL. Power of the test was adequate for most of the combinations in all estimation methods except MQL1. Moreover, models were fitted using the four methods to a real-life dataset and performance of the test was compared for each model.

Keywords: goodness-of-fit test, marginal quasi-likelihood, multilevel modelling, penalized quasi-likelihood, power, quasi-likelihood, type-I error

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Authors: Alime Cengiz, Talip Kahyaoglu

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Roasting process has the major importance to obtain desired aromatic taste of nuts. In this study, two kinds of roasting process were applied to hulled sesame seeds - vacuum oven and hot air roasting. Efficiency of Gene Expression Programming (GEP), a new soft computing technique of evolutionary algorithm that describes the cause and effect relationships in the data modelling system, and response surface methodology (RSM) were examined in the modelling of roasting processes over a range of temperature (120-180°C) for various times (30-60 min). Color attributes (L*, a*, b*, Browning Index (BI)), textural properties (hardness and fracturability) and moisture content were evaluated and modelled by RSM and GEP. The GEP-based formulations and RSM approach were compared with experimental results and evaluated according to correlation coefficients. The results showed that both GEP and RSM were found to be able to adequately learn the relation between roasting conditions and physical and textural parameters of roasted seeds. However, GEP had better prediction performance than the RSM with the high correlation coefficients (R2 >0.92) for the all quality parameters. This result indicates that the soft computing techniques have better capability for describing the physical changes occuring in sesame seeds during roasting process.

Keywords: genetic expression programming, response surface methodology, roasting, sesame seed

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Authors: K. Bokreta, D. Benanaya

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The objective of this study is to examine the relative effectiveness of monetary and fiscal policy in Algeria using the econometric modelling techniques of cointegration and vector error correction modelling to analyse and draw policy inferences. The chosen variables of fiscal policy are government expenditure and net taxes on products, while the effect of monetary policy is presented by the inflation rate and the official exchange rate. From the results, we find that in the long-run, the impact of government expenditures is positive, while the effect of taxes is negative on growth. Additionally, we find that the inflation rate is found to have little effect on GDP per capita but the impact of the exchange rate is insignificant. We conclude that fiscal policy is more powerful then monetary policy in promoting economic growth in Algeria.

Keywords: economic growth, monetary policy, fiscal policy, VECM

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Authors: Anto Antony Samy, Atefeh Golbang, Edward Archer, Alistair McIlhagger

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Fused deposition modelling (FDM) is one of the additive manufacturing techniques that has become highly attractive in the industrial and academic sectors. However, parts fabricated through FDM are highly susceptible to geometrical defects such as warpage, shrinkage, and delamination that can severely affect their function. Among the thermoplastic polymer feedstock for FDM, semi-crystalline polymers are highly prone to part distortion due to polymer crystallization. In this study, the influence of FDM processing conditions such as chamber temperature and print bed temperature on the induced thermal residual stress and resulting warpage are investigated using the 3D transient thermal model for a semi-crystalline polymer. The thermo-mechanical properties and the viscoelasticity of the polymer, as well as the crystallization physics, which considers the crystallinity of the polymer, are coupled with the evolving temperature gradient of the print model. From the results, it was observed that increasing the chamber temperature from 25°C to 75°C lead to a decrease of 1.5% residual stress, while decreasing bed temperature from 100°C to 60°C, resulted in a 33% increase in residual stress and a significant rise of 138% in warpage. The simulated warpage data is validated by comparing it with the measured warpage values of the samples using 3D scanning.

Keywords: finite element analysis, fused deposition modelling, residual stress, warpage

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