Search results for: mixed methodology

Authors: Kasthurirangan Gopalakrishnan, Naga Shashidhar

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The mechanical properties of granular solids are dependent on the flow of stresses from one particle to another through inter-particle contact. Although some experimental methods have been used to study the inter-particle contacts in the past, preliminary work with these techniques indicated that they do not have the necessary resolution to distinguish between those contacts that transmit the load and those that do not, especially for systems with a wide distribution of particle sizes. In this research, computer simulations are used to study the nature and distribution of contacts in a compact with wide particle size distribution, representative of aggregate size distribution used in asphalt pavement construction. The packing fraction, the mean number of contacts and the distribution of contacts were studied for different scenarios. A methodology to distinguish and compute the fraction of load-bearing particles and the fraction of space-filling particles (particles that do not transmit any force) is needed for further investigation.

Keywords: Computer simulation, three-dimensional particlepacking, coordination number, asphalt concrete, aggregates.

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Authors: K. Kamalanand, S. Srinivasan

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Analysis of blood vessel mechanics in normal and diseased conditions is essential for disease research, medical device design and treatment planning. In this work, 3D finite element models of normal vessel and atherosclerotic vessel with 50% plaque deposition were developed. The developed models were meshed using finite number of tetrahedral elements. The developed models were simulated using actual blood pressure signals. Based on the transient analysis performed on the developed models, the parameters such as total displacement, strain energy density and entropy per unit volume were obtained. Further, the obtained parameters were used to develop artificial neural network models for analyzing normal and atherosclerotic blood vessels. In this paper, the objectives of the study, methodology and significant observations are presented.

Keywords: Blood vessel, atherosclerosis, finite element model, artificial neural networks

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Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

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In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other.

As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO.

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Authors: Rafenomananjara Tsinjo Nirina, Tomoo Sekito, Andrianaivoravelona Jaconnet Oliva

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Manufacturing a composite material from post-consumer bottles is an interesting outlet since Madagascar is still facing the challenges of managing plastic waste on the one hand and appropriate waste treatment facilities are not yet developed on the other hand. New waste management options are needed to divert End-Of-Life (EOL) soft plastic wastes from landfills and incineration. Waste polyethylene terephthalate (PET) bottles might be considered as a valuable resource and recovered into polymer concrete. The methodology is easy to implement and appropriate to the local context in Madagascar. This approach will contribute to the production of ecological building materials that might be profitable for the environment and the construction sector. This work aims to study the feasibility of using the post-consumer PET bottles as an alternative binding agent instead of the conventional Portland cement and water. Then, the mechanical and physical properties of the materials were evaluated.

Keywords: PET recycling, polymer concrete, ecological building materials, pollution mitigation.

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Authors: Tatsaporn Todhanakasem, Kamonchanok Areerat, Pornthap Thanonkeo, Roungdao KlinjapoandGlenn M. Young

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Rice bran has been abandoned as agricultural waste for million tonnes per year in Thailand, therefore they have been proposed to be utilized as a rich carbon source in the production of bioethanol. Many toxic compounds are possibly released during the pretreatment of rice bran prior the fermentation process. This study aims to analyze on the availability of toxic compounds and the amount of glucose obtained from 2 different pretreatments using sulfuric acid and mixed cellulase enzymes (without and with delignification/ activated charcoal). The concentration of furfural, 5- hydroxymethyl furfural (5-HMF), levulinic acid, vanillin, syringaldehyde and4-hydroxybenzaldehyde (4-HB) and the percent acetic acid were found to be 0.0517 ± 0.049 mg/L, 0.032 ± 0.06 mg/L, 21074 ± 1685.62 mg/L, 126.265 ± 6.005 mg/L, 2.89 ± 0.30 mg/L, 0.37 ± 0.031mg/L and 0.72% under the pretreatment process without delignification/ activated charcoal treatment and 384.47 ± 99.02 g/L, 0.068 mg/L, 142107.62 ± 8664.6 mg/L, 0.19 mg/L, 5.43 ± 3.29 mg/L, 4.80 ± 0.76 mg/L and 0.254% under the pretreatment process with delignification/ activated charcoal treatment respectively. The presence of high concentration of acetic acid was found to impede the growth of Zymomonas mobilis strain TISTR 551 despite the present of high concentration of levulinic acid. Z. mobilis strain TISTR 551 was found to produce 8.96 ± 4.06 g/L of ethanol under 4 days fementation period in biofilm stage in which represented 40% theoretical yield.

Keywords: Rice bran, Zymomonas mobilis, biofilm, ethanol.

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Authors: Engin Yesil, Leon Urbas

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Modeling of complex dynamic systems, which are very complicated to establish mathematical models, requires new and modern methodologies that will exploit the existing expert knowledge, human experience and historical data. Fuzzy cognitive maps are very suitable, simple, and powerful tools for simulation and analysis of these kinds of dynamic systems. However, human experts are subjective and can handle only relatively simple fuzzy cognitive maps; therefore, there is a need of developing new approaches for an automated generation of fuzzy cognitive maps using historical data. In this study, a new learning algorithm, which is called Big Bang-Big Crunch, is proposed for the first time in literature for an automated generation of fuzzy cognitive maps from data. Two real-world examples; namely a process control system and radiation therapy process, and one synthetic model are used to emphasize the effectiveness and usefulness of the proposed methodology.

Keywords: Big Bang-Big Crunch optimization, Dynamic Systems, Fuzzy Cognitive Maps, Learning.

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Authors: Hrishikesh Purandare, Ashwini Pethe

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The built environment can have a significant effect on children’s cognitive and socio-emotional development. Children living in urban slums in India confront issues associated with poor living conditions and lack of access to basic service. It is a well-known fact that slums are places of extreme poverty, substandard housing, overcrowding, and poor sanitation. These challenges faced by children living in slums can have a significant impact on their physical, psychological, and social development. Despite the magnitude of the problem, the area of research particularly on the impact of the built environment of slums on children and adolescent well-being has been understudied in India. The impact of the built environment on children’s well-being has been understudied in the global south. Apart from issues of the limited access to health and education of these children, perception of children regarding the built environment which they inhabit is rarely addressed. A sample of 120 children living in the slums of Pune city between the ages 7 and 16 years participated in this study which employed a concurrent embedded approach of mixed method research. Questionnaires were administered to obtain quantitative data that included attributes of crowding, noise, privacy, territoriality, and housing quality in the built environment. The qualitative analysis of children’s sketches highlighted aspects of the built environment with which they associated themselves the most. The study sought to examine the perception of the deprived children living in the urban slums in the city of Pune (India) towards their built environment.

Keywords: Physical environment, poverty, underprivileged children, urban Indian slums.

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Authors: Ayed Salman, Fawaz Al-Anzi, Aseel Al-Minayes

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Heuristics-based search methodologies normally work on searching a problem space of possible solutions toward finding a “satisfactory" solution based on “hints" estimated from the problem-specific knowledge. Research communities use different types of methodologies. Unfortunately, most of the times, these hints are immature and can lead toward hindering these methodologies by a premature convergence. This is due to a decrease of diversity in search space that leads to a total implosion and ultimately fitness stagnation of the population. In this paper, a novel Decision Maturity framework (DMF) is introduced as a solution to this problem. The framework simply improves the decision on the direction of the search by materializing hints enough before using them. Ideas from this framework are injected into the particle swarm optimization methodology. Results were obtained under both static and dynamic environment. The results show that decision maturity prevents premature converges to a high degree.

Keywords: Heuristic Search, hints, Particle Swarm Optimization, Decision Maturity Framework.

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Authors: Hafizullah A. Hashim, Aniza. A

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The Spiral development model has been used successfully in many commercial systems and in a good number of defense systems. This is due to the fact that cost-effective incremental commitment of funds, via an analogy of the spiral model to stud poker and also can be used to develop hardware or integrate software, hardware, and systems. To support adaptive, semantic collaboration between domain experts and knowledge engineers, a new knowledge engineering process, called Spiral_OWL is proposed. This model is based on the idea of iterative refinement, annotation and structuring of knowledge base. The Spiral_OWL model is generated base on spiral model and knowledge engineering methodology. A central paradigm for Spiral_OWL model is the concentration on risk-driven determination of knowledge engineering process. The collaboration aspect comes into play during knowledge acquisition and knowledge validation phase. Design rationales for the Spiral_OWL model are to be easy-to-implement, well-organized, and iterative development cycle as an expanding spiral.

Keywords: Domain Expert, Knowledge Base, Ontology, Software Process.

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Authors: Rajab Abdullah Hokoma

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Manufacturing, production and service industries within Libya have struggled with many problems during the past two decades due to many difficulties. These problems have created a negative impact on the productivity and utilization of many industries around the country. This paper studies the implementation levels of the manufacturing control systems known as Manufacturing Resource Planning (MRPII) being adapted within some Libyan industries. A survey methodology has been applied for this research, based on the survey analysis, the results pointed out that the system within these industries has a modest strategy towards most of the areas that are considered as being very crucial in implementing these systems successfully. The findings also show a variation within these implementation levels with a respect to the key-elements that related to MRPII, giving the highest levels in the emphasise on financial data accuracy. The paper has also identified limitations within the investigated manufacturing and managerial areas and has pointed to where senior managers should take immediate actions in order to achieve effective implementation of MRPII within their business area.

Keywords: Control, Industry, Manufacturing, Survey, System

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Authors: H. F. A. Abdul Ghani, R. R. Porle

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Speech to text in Malay language is a system that converts Malay speech into text. The Malay language recognition system is still limited, thus, this paper aims to investigate the performance of ten Malay words obtained from the online Malay news. The methodology consists of three stages, which are preprocessing, feature extraction, and speech classification. In preprocessing stage, the speech samples are filtered using pre emphasis. After that, feature extraction method is applied to the samples using Mel Frequency Cepstrum Coefficient (MFCC). Lastly, speech classification is performed using Feedforward Neural Network (FFNN). The accuracy of the classification is further investigated based on the hidden layer size. From experimentation, the classifier with 40 hidden neurons shows the highest classification rate which is 94%.  

Keywords: Feed-Forward Neural Network, FFNN, Malay speech recognition, Mel Frequency Cepstrum Coefficient, MFCC, speech-to-text.

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Authors: Paul Okonkwo, Howard Smith

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A study to estimate the size of the cabin and major aircraft components as well as detect and avoid interference between internally placed components and the external surface, during the conceptual design synthesis and optimisation to explore the design space of a BWB, was conducted. Sizing of components follows the Bradley cabin sizing and rubber engine scaling procedures to size the cabin and engine respectively. The interference detection and avoidance algorithm relies on the ability of the Class Shape Transform parameterisation technique to generate polynomial functions of the surfaces of a BWB aircraft configuration from the sizes of the cabin and internal objects using few variables. Interference detection is essential in packaging of non-conventional configuration like the BWB because of the non-uniform airfoil-shaped sections and resultant varying internal space. The unique configuration increases the need for a methodology to prevent objects from being placed in locations that do not sufficiently enclose them within the geometry.

Keywords: Packaging, Optimisation, BWB, Parameterisation, Aircraft Conceptual Design.

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Authors: P. Zamov, H. Spangenberg

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Presents a concept for a multidisciplinary process supporting effective task transitions between different technical domains during the architectural design stage. A system configuration challenge is the multifunctional driven increased solution space. As a consequence, more iteration is needed to find a global optimum, i.e. a compromise between involved disciplines without negative impact on development time. Since state of the art standards like ISO 15288 and VDI 2206 do not provide a detailed methodology on multidisciplinary design process, higher uncertainties regarding final specifications arise. This leads to the need of more detailed and standardized concepts or processes which could mitigate risks. The performed work is based on analysis of multidisciplinary interaction, of modeling and simulation techniques. To demonstrate and prove the applicability of the presented concept, it is applied to the design of aircraft high lift systems, in the context of the engineering disciplines kinematics, actuation, monitoring, installation and structure design.

Keywords: Systems engineering, multidisciplinary, architectural design, high lift system.

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Authors: S. K. Tomar, R. Prasad, S. Panda, C. Ardil

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Reduction of Single Input Single Output (SISO) discrete systems into lower order model, using a conventional and an evolutionary technique is presented in this paper. In the conventional technique, the mixed advantages of Modified Cauer Form (MCF) and differentiation are used. In this method the original discrete system is, first, converted into equivalent continuous system by applying bilinear transformation. The denominator of the equivalent continuous system and its reciprocal are differentiated successively, the reduced denominator of the desired order is obtained by combining the differentiated polynomials. The numerator is obtained by matching the quotients of MCF. The reduced continuous system is converted back into discrete system using inverse bilinear transformation. In the evolutionary technique method, Particle Swarm Optimization (PSO) is employed to reduce the higher order model. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical example.

Keywords: Discrete System, Single Input Single Output (SISO), Bilinear Transformation, Reduced Order Model, Modified CauerForm, Polynomial Differentiation, Particle Swarm Optimization, Integral Squared Error.

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Authors: Matthew J. Zagumny

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The QUEST is an assessment of scientific epistemic beliefs and was developed to measure students’ intellectual development in regards to beliefs about knowledge and knowing. The QUEST utilizes Q-sort methodology, which requires participants to rate the degree to which statements describe them personally. As a measure of personal theories of knowledge, the QUEST instrument is described with the Q-sort distribution and scoring explained. A preliminary demonstration of the QUEST assessment is described with two samples of undergraduate students (novice/lower division compared to advanced/upper division students) being assessed and their average QUEST scores compared. The usefulness of an assessment of epistemology is discussed in terms of the principle that assessment tends to drive educational practice and university mission. The critical need for university and academic programs to focus on development of students’ scientific epistemology is briefly discussed.

Keywords: Scientific epistemology, critical thinking, Q-sort method, STEM undergraduates.

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Authors: Catherine Maware, Olufemi Adetunji

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The paper is aimed at developing a model to measure the impact of Lean manufacturing deployment on organizational performance. The model will help industry practitioners to assess the impact of implementing Lean constructs on organizational performance. It will also harmonize the measurement models of Lean performance with the house of Lean that seems to have become the industry standard. The sheer number of measurement models for impact assessment of Lean implementation makes it difficult for new adopters to select an appropriate assessment model or deployment methodology. A literature review is conducted to classify the Lean performance model. Pareto analysis is used to select the Lean constructs for the development of the model. The model is further formalized through the use of Structural Equation Modeling (SEM) in defining the underlying latent structure of a Lean system. An impact assessment measurement model developed can be used to measure Lean performance and can be adopted by different industries.

Keywords: Impact measurement model, lean bundles, lean manufacturing, organizational performance.

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Authors: N. Šarlija, S. Pfeifer, M. Jeger, A. Bilandžić

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Enterprise growth is generally considered as a key driver of competitiveness, employment, economic development and social inclusion. As such, it is perceived to be a highly desirable outcome of entrepreneurship for scholars and decision makers. The huge academic debate resulted in the multitude of theoretical frameworks focused on explaining growth stages, determinants and future prospects. It has been widely accepted that enterprise growth is most likely nonlinear, temporal and related to the variety of factors which reflect the individual, firm, organizational, industry or environmental determinants of growth. However, factors that affect growth are not easily captured, instruments to measure those factors are often arbitrary, causality between variables and growth is elusive, indicating that growth is not easily modeled. Furthermore, in line with heterogeneous nature of the growth phenomenon, there is a vast number of measurement constructs assessing growth which are used interchangeably. Differences among various growth measures, at conceptual as well as at operationalization level, can hinder theory development which emphasizes the need for more empirically robust studies. In line with these highlights, the main purpose of this paper is twofold. Firstly, to compare structure and performance of three growth prediction models based on the main growth measures: Revenues, employment and assets growth. Secondly, to explore the prospects of financial indicators, set as exact, visible, standardized and accessible variables, to serve as determinants of enterprise growth. Finally, to contribute to the understanding of the implications on research results and recommendations for growth caused by different growth measures. The models include a range of financial indicators as lag determinants of the enterprises’ performances during the 2008-2013, extracted from the national register of the financial statements of SMEs in Croatia. The design and testing stage of the modeling used the logistic regression procedures. Findings confirm that growth prediction models based on different measures of growth have different set of predictors. Moreover, the relationship between particular predictors and growth measure is inconsistent, namely the same predictor positively related to one growth measure may exert negative effect on a different growth measure. Overall, financial indicators alone can serve as good proxy of growth and yield adequate predictive power of the models. The paper sheds light on both methodology and conceptual framework of enterprise growth by using a range of variables which serve as a proxy for the multitude of internal and external determinants, but are unlike them, accessible, available, exact and free of perceptual nuances in building up the model. Selection of the growth measure seems to have significant impact on the implications and recommendations related to growth. Furthermore, the paper points out to potential pitfalls of measuring and predicting growth. Overall, the results and the implications of the study are relevant for advancing academic debates on growth-related methodology, and can contribute to evidence-based decisions of policy makers.

Keywords: Growth measurement constructs, logistic regression, prediction of growth potential, small and medium-sized enterprises.

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Authors: JanuszKwaśniewski, IreneuszDominik, KrzysztofLalik

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The knowledge about rock layers thickness,especially above drilled mining pavements is crucial for workers safety. The measuring systems used nowadays are generally imperfect and there is a strong demand for improvement. The application of a new type of a measurement system called Self-excited Acoustical System is presentedin the paper. The system was applied until now to monitor stress changes in metal and concrete constructions. The change in measurement methodology resulted in possibility of measuring the thickness of the rocks above the tunnels as well as thickness of a singular rocklayer. The idea is to find two resonance frequencies of the self-exited system,which consists of a vibration exciter and vibration receiver placed at a distance, which are coupled with a proper power amplifier, and which operate in a closed loop with a positive feedback. The resonance with the higher amplitude determines thickness of the whole rock, whereas the lower amplitude resonance indicates thickness of a singular layer. The results of the laboratory tests conducted on a group of different rock materials are also presented.

Keywords: Autooscillator, non-destructive testing, rock thickness measurement.

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Authors: Catherine Equey Balzli

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Old age retirement pensions are an important concern among the Swiss but estimating one’s income after retirement is difficult due to the Swiss insurance system’s complexity. This project’s aim is to prepare for developing a digital platform that will allow individuals to plan for retirement in a simplified manner. The main objective of the platform will be to give individuals the tools to check that their savings and retirement benefits will allow them to continue the lifestyle to which they are accustomed once they are retired. The research results from qualitative (focus group) and quantitative (survey) methodologies, recommend the scope and functionalities for a digital platform to be developed. A main outcome is the need to limit the platform’s scope to old-age pension only (excluding survivors’ or disability pensions, for instance). Furthermore, an outcome regarding the functionalities is the proposition of scenarios such as early retirement, changes to income, or modifications to personal status. The development of the digital platform will be a subsequent project.

Keywords: Benefit statement, digital platform, retirement financial planning, social insurances.

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Authors: Susan J. Simmons, Fang Fang, Qijun Fang, Karl Ricanek

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Quantitative trait loci (QTL) experiments have yielded important biological and biochemical information necessary for understanding the relationship between genetic markers and quantitative traits. For many years, most QTL algorithms only allowed one observation per genotype. Recently, there has been an increasing demand for QTL algorithms that can accommodate more than one observation per genotypic distribution. The Bayesian hierarchical model is very flexible and can easily incorporate this information into the model. Herein a methodology is presented that uses a Bayesian hierarchical model to capture the complexity of the data. Furthermore, the Markov chain Monte Carlo model composition (MC3) algorithm is used to search and identify important markers. An extensive simulation study illustrates that the method captures the true QTL, even under nonnormal noise and up to 6 QTL.

Keywords: Bayesian hierarchical model, Markov chain MonteCarlo model composition, quantitative trait loci.

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Authors: Shohei Nakai, Yasuhiro Yamada

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Weed suppression and weeding are necessary measures for rice cultivation. Weed suppression precedes the process of weeding. It means suppressing the growth of young weeds and creating a weed-less environment. If we suppress the growth of weeds, we can reduce the number of weeds in a paddy field. This would result in a reduction of the weeding work load. In this paper, we will show how we developed a weed suppression robot for the purpose of reducing the weeding work load. The robot has a laser range finder for autonomous mobility and a robot arm for weed suppression. It travels along the rice rows without stepping on and injuring the rice plants in a paddy field. The robot arm applies force to the weed seedlings and thereby suppresses the growth of weeds. This paper will explain the methodology of the autonomous mobile, the experiment in weed suppression, and the method of controlling the robot’s posture on uneven ground.

Keywords: Mobile robot, Paddy field, Robot arm, Weed.

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Authors: Fengxia Zheng, Shouming Zhong

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ANNARIMA that combines both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This paper provides a hybrid methodology that combines both radial basis function (RBF) neural network and auto regression (AR) model based on binomial smoothing (BS) technique which is efficient in data processing, which is called BSRBFAR. This method is examined by using the data of Canadian Lynx data. Empirical results indicate that the over-fitting problem can be eased using RBF neural network based on binomial smoothing which is called BS-RBF, and the hybrid model–BS-RBFAR can be an effective way to improve forecasting accuracy achieved by BSRBF used separately.

Keywords: Binomial smoothing (BS), hybrid, Canadian Lynx data, forecasting accuracy.

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Authors: Marios M. Hadjiandreou, Raul Conejeros, Ian Wilson

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This study presents a mathematical modeling approach to the planning of HIV therapies on an individual basis. The model replicates clinical data from typical-progressors to AIDS for all stages of the disease with good agreement. Clinical data from rapid-progressors and long-term non-progressors is also matched by estimation of immune system parameters only. The ability of the model to reproduce these phenomena validates the formulation, a fact which is exploited in the investigation of effective therapies. The therapy investigation suggests that, unlike continuous therapy, structured treatment interruptions (STIs) are able to control the increase in both the drug-sensitive and drug-resistant virus population and, hence, prevent the ultimate progression from HIV to AIDS. The optimization results further suggest that even patients characterised by the same progression type can respond very differently to the same treatment and that the latter should be designed on a case-by-case basis. Such a methodology is presented here.

Keywords: AIDS, chemotherapy, mathematical modeling, optimal control, progression.

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Authors: Mohd Hasmadi Ismail, Kamaruzaman Jusoff

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In order to develop forest management strategies in tropical forest in Malaysia, surveying the forest resources and monitoring the forest area affected by logging activities is essential. There are tremendous effort has been done in classification of land cover related to forest resource management in this country as it is a priority in all aspects of forest mapping using remote sensing and related technology such as GIS. In fact classification process is a compulsory step in any remote sensing research. Therefore, the main objective of this paper is to assess classification accuracy of classified forest map on Landsat TM data from difference number of reference data (200 and 388 reference data). This comparison was made through observation (200 reference data), and interpretation and observation approaches (388 reference data). Five land cover classes namely primary forest, logged over forest, water bodies, bare land and agricultural crop/mixed horticultural can be identified by the differences in spectral wavelength. Result showed that an overall accuracy from 200 reference data was 83.5 % (kappa value 0.7502459; kappa variance 0.002871), which was considered acceptable or good for optical data. However, when 200 reference data was increased to 388 in the confusion matrix, the accuracy slightly improved from 83.5% to 89.17%, with Kappa statistic increased from 0.7502459 to 0.8026135, respectively. The accuracy in this classification suggested that this strategy for the selection of training area, interpretation approaches and number of reference data used were importance to perform better classification result.

Keywords: Image Classification, Reference Data, Accuracy Assessment, Kappa Statistic, Forest Land Cover

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Authors: K. Hema Shankari, R. Thirumalaiselvi, N. V. Balasubramanian

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The present paper addresses to the research in the area of regression testing with emphasis on automated tools as well as prioritization of test cases. The uniqueness of regression testing and its cyclic nature is pointed out. The difference in approach between industry, with business model as basis, and academia, with focus on data mining, is highlighted. Test Metrics are discussed as a prelude to our formula for prioritization; a case study is further discussed to illustrate this methodology. An industrial case study is also described in the paper, where the number of test cases is so large that they have to be grouped as Test Suites. In such situations, a genetic algorithm proposed by us can be used to reconfigure these Test Suites in each cycle of regression testing. The comparison is made between a proprietary tool and an open source tool using the above-mentioned metrics. Our approach is clarified through several tables.

Keywords: APFD metric, genetic algorithm, regression testing, RFT tool, test case prioritization, selenium tool.

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Authors: Pushpa N. Rathie, Prabhata K. Swamee, André L. B. Cavalcante, Luan Carlos de S. M. Ozelim

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Implicit equations play a crucial role in Engineering. Based on this importance, several techniques have been applied to solve this particular class of equations. When it comes to practical applications, in general, iterative procedures are taken into account. On the other hand, with the improvement of computers, other numerical methods have been developed to provide a more straightforward methodology of solution. Analytical exact approaches seem to have been continuously neglected due to the difficulty inherent in their application; notwithstanding, they are indispensable to validate numerical routines. Lagrange-s Inversion Theorem is a simple mathematical tool which has proved to be widely applicable to engineering problems. In short, it provides the solution to implicit equations by means of an infinite series. To show the validity of this method, the tree-parameter infiltration equation is, for the first time, analytically and exactly solved. After manipulating these series, closed-form solutions are presented as H-functions.

Keywords: Green-Ampt Equation, Lagrange's Inversion Theorem, Talsma-Parlange Equation, Three-Parameter Infiltration Equation

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Authors: Rama Shanker, Anil Kumar Sachan

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Basic ingredients of concrete are cement, fine aggregate, coarse aggregate and water. To produce a concrete of certain specific properties, optimum proportion of these ingredients are mixed. The important factors which govern the mix design are grade of concrete, type of cement and size, shape and grading of aggregates. Concrete mix design method is based on experimentally evolved empirical relationship between the factors in the choice of mix design. Basic draw backs of this method are that it does not produce desired strength, calculations are cumbersome and a number of tables are to be referred for arriving at trial mix proportion moreover, the variation in attainment of desired strength is uncertain below the target strength and may even fail. To solve this problem, a lot of cubes of standard grades were prepared and attained 28 days strength determined for different combination of cement, fine aggregate, coarse aggregate and water. An artificial neural network (ANN) was prepared using these data. The input of ANN were grade of concrete, type of cement, size, shape and grading of aggregates and output were proportions of various ingredients. With the help of these inputs and outputs, ANN was trained using feed forward back proportion model. Finally trained ANN was validated, it was seen that it gave the result with/ error of maximum 4 to 5%. Hence, specific type of concrete can be prepared from given material properties and proportions of these materials can be quickly evaluated using the proposed ANN.

Keywords: Aggregate Proportions, Artificial Neural Network, Concrete Grade, Concrete Mix Design.

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Authors: S. S. Bhandari, N. Chakpitak, K. Meksamoot, T. Chandarasupsang

Abstract:

Under the limitation of investment budget, a utility company is required to maximize the utilization of their existing assets during their life cycle satisfying both engineering and financial requirements. However, utility does not have knowledge about the status of each asset in the portfolio neither in terms of technical nor financial values. This paper presents a knowledge based model for the utility companies in order to make an optimal decision on power transformer with their utilization. CommonKADS methodology, a structured development for knowledge and expertise representation, is utilized for designing and developing knowledge based model. A case study of One MVA power transformer of Nepal Electricity Authority is presented. The results show that the reusable knowledge can be categorized, modeled and utilized within the utility company using the proposed methodologies. Moreover, the results depict that utility company can achieve both engineering and financial benefits from its utilization.

Keywords: CommonKADS, Knowledge Engineering, LifeCycle Management, Power Transformer.

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Authors: Rayeheh Khatami, Toktam Hanaei, Mohammad Reza Mansouri Daneshvar

Abstract:

The growth of the agricultural sector is known as an essential way to achieve development goals in developing countries. Urban agriculture is a way to reduce the vulnerability of urban populations of the world toward global environmental change. It is a sustainable and efficient system to respond to the environmental, social and economic needs of the city, which leads to urban sustainability. Today, many local and national governments are developing urban agriculture as an effective tool in responding to challenges such as poverty, food security, and environmental problems. In this study, we follow a perspective based on urban agriculture literature in order to indicate the urban agriculture’s benefits in environmental planning strategies in non-western countries like Iran. The methodological approach adopted is based on qualitative approach and documentary studies. A total of 35 articles (mixed quantitative and qualitative methods studies) were studied in final analysis, which are published in relevant journals that focus on this subject. Studies show the wide range of positive benefits of urban agriculture on food security, nutrition outcomes, health outcomes, environmental outcomes, and social capital. However, there was no definitive conclusion about the negative effects of urban agriculture. This paper provides a conceptual and theoretical basis to know about urban agriculture and its roles in environmental planning, and also conclude the benefits of urban agriculture for researchers, practitioners, and policymakers who seek to create spaces in cities for implementation urban agriculture in future.

Keywords: Urban agriculture, environmental planning, urban planning, literature.

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Authors: Ahmed Tawfik, Fausto Giunchiglia, Vincenzo Maltese

Abstract:

Ontologies provide a common understanding of a specific domain of interest that can be communicated between people and used as background knowledge for automated reasoning in a wide range of applications. In this paper, we address the design of multilingual ontologies following well-defined knowledge engineering methodologies with the support of novel collaborative development approaches. In particular, we present a collaborative platform which allows ontologies to be developed incrementally in multiple languages. This is made possible via an appropriate mapping between language independent concepts and one lexicalization per language (or a lexical gap in case such lexicalization does not exist). The collaborative platform has been designed to support the development of the Universal Knowledge Core, a multilingual ontology currently in English, Italian, Chinese, Mongolian, Hindi and Bangladeshi. Its design follows a workflow-based development methodology that models resources as a set of collaborative objects and assigns customizable workflows to build and maintain each collaborative object in a community driven manner, with extensive support of modern web 2.0 social and collaborative features.

Keywords: Knowledge Diversity, Knowledge Representation, Ontology Development.

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