Orientador: Ariovaldo Denis Granja / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e Urbanismo / Made available in DSpace on 2018-08-13T21:36:56Z (GMT). No. of bitstreams: 1 Yoshimura_EduardoKoiti_M.pdf: 1509592 bytes, checksum: 8e3fa8ff3f4d583a674b86a3beccaf65 (MD5) Previous issue date: 2008 / Resumo: Muitas empresas estão sendo forçadas a buscar uma rápida adaptação às mudanças, num ambiente de acirrada competição e incerteza futura, procurando investir em empreendimentos que proporcionem seu crescimento. Nesse novo cenário, os empreendimentos precisam criar alternativas que ofereçam maiores oportunidades. A incorporação de atributos de flexibilidade já na concepção do empreendimento permite que o risco e a incerteza sejam encarados como oportunidades estratégicas a serem exploradas. Recentemente, ressalvas têm sido direcionadas às técnicas convencionais de orçamento de capitais, tais como o Fluxo de Caixa Descontado (FCD). A análise de opções reais (ROA) destina-se à valoração e tomada de decisões de investimentos em ativos reais e, por sua vez, os investimentos da construção civil podem ser caracterizados como tais, pois normalmente apresentam longo prazo de maturação, são ricos em contingências, possuem irreversibilidade nos seus investimentos, e estão sujeitos a condições de incerteza tais como as econômicas. Nesta pesquisa, propõe-se uma abordagem exploratória por meio de um estudo de caso no setor da construção civil onde a ROA é utilizada. Estabelece-se como hipótese que a abordagem com base em ROA pode complementar os métodos tradicionais com base em FCD, bem como tornar mais aparentes as oportunidades contidas nestes tipos de investimento. Os resultados obtidos neste trabalho mostram que a abordagem com ROA produzem resultados superiores em relação ao valor do empreendimento quando comparada à análise restrita a métodos com base em FCD, de forma a proporcionar um efetivo gerenciamento e estabelecimento de uma nova cultura empresarial em empreendimentos da construção civil. / Abstract: Many companies are being forced to seek rapid adaptation to changes in a world of tough competition and uncertain future, trying to invest in enterprises that provide their growth. In this new scenario, ventures must create alternatives that offer greater opportunities. The incorporation of attributes of flexibility in the design of the project itself allows that risk and uncertainty be seen as strategic opportunities to be exploited. Recently, criticism has been directed at conventional techniques of capital budgeting, such as Discounted Cash Flow (DCF). The Real Options Analysis (ROA) is used for the evaluation and decision-making of investments in real assets and, in turn, investment in construction can be characterized as such, because they normally have long-term maturity, are rich in contingencies, have irreversibility in their investments, and are subject to conditions of uncertainty such as economic ones. In this research, it is proposed an exploratory through a case study where the ROA is used. It is like assuming that the approach based on ROA can complement traditional methods based on DCF, and making the opportunities contained in these types of investments more evident. The results in this paper show that the approach with ROA produce better results compared restricted analysis with methods based on FCD, in order to provide an effective management and establishment of a new enterprise culture in the civil. / Mestrado / Edificações / Mestre em Engenharia Civil

A study is made of the use of chemical analysis of groundwater samples to assess groundwater quality. Samples from forty-seven boreholes within and around ten landfill sites in the London Borough of Hounslow were analysed. Most of the landfill sites studied were filled prior to the implementation of the Control of Pollution Act 1974 and were not designed with environmental protection in mind. Boreholes were tested for methane, carbon dioxide and groundwater samples were analysed for electrical conductivity (EC), ammonium nitrogen (NI14-N), COD, TOC, Cl-, NO3, SO42-, Na+, K+, Ca2+, Ni2+, Cu2+, Pb2+, oxidation - reduction - potential (ORP) and pH. The first ten of these analytical parameters were considered for groundwater quality assessment and the most important selected for more detailed study namely CH4, C02, EC, NH4-N, COD and TOC. Pollution ratings are allocated on the basis of single pollutant analytical data and averaged for each of the bore hole samples. Goodness of fit values between the single analyte values and the average data were calculated and no set of single analyte data was found to provide good groundwater quality assessment. A series of formulae combining the six analyte parameters was considered and the data evaluated by goodness of fit calculations to provide an effective means of assessing groundwater quality. The ratings obtained from the best formula are compared with historical broad band classification of landfill sites and show how sites must be assessed on the basis of samples taken from within the site and not around the perimeter. The production of acetic acid in the acetogenic phase of landfill reactions will increase the leachability and mobility of many metal pollutants and particularly of lead. Studies on the leachability of lead in acetic acid media show how lead solubility is increased in acetic acid media by the formation of triacetatolead(II) species, Pb(CH3CO2)3-.

Surface water is essential for the eco-environment and provides various purposes and functions in anthropogenic activities such as domestic water supply, agriculture irrigation and drainage, and energy creation. Due to its important role in human health and ecosystem, it is essential to establish effective models to study the water quantity and quality through systematic research. In this dissertation, machine learning (ML) has been investigated as a modelling approach to simulate water quality and quantity of rivers. It includes the analysis of river pollution sources, downstream river flow prediction, long-term river heavy metal prediction and the influence of environmental factors on river heavy metals. This research was conducted to combine ML methods to enhance the accuracy and applicability of models in water environment studies and further assist relevant sectors to strengthen the management of surface water quantity and quality. (1) The primary source contributors of trace metals in surface water were identified based on the trained optimal model. This study trained and evaluated the typical shallow and deep learning approaches to identifying and classifying source contributors based on a database and analyzed the source apportionment of trace metals in the main stream and tributaries of river basin by the proposed approaches. (2) The interpretable ML models were developed to overcome the under-appreciated issue of the model for predicting heavy metals that are both predictive and transparent. This study employed and compared five tree-based machine learning models and then performed global and local feature importance analyses with the optimum models to predict the most important environmental factors for heavy metals management. (3) The performance of conventional or hybrid ML models with time series decomposition technology was developed and compared for long term prediction of heavy metals. This study examined the effect of inputs time-series data of selection and division on the performance of conventional and hybrid models and evaluated the long-term fore-casts by standard metrics to select the optimum approaches for typical metal long term prediction. (4) The downstream river flow was predicted based on the combined ML models. This study established the hybrid CNN-LSTM model to process the 2D rainfall radar information by convolutional neural network (CNN) and the time series information by long short-term memory (LSTM), and explored the capacity of the hybrid model for river flow forecasting.:Table of Content List of Abbreviations V List of Publications on the Ph.D. Topic VII List of Co-authored Publications during the Ph.D. VII 1 General Introduction 1 1.1 Background 1 1.2 Aim and Objective 3 1.3 Innovation and Contribution 3 1.4 Outline 4 1.5 References 5 2 Traceability Study of Metals 9 2.1 Introduction 10 2.2 Materials and Methods 11 2.2.1 Study Area 11 2.2.2 Ecological Risk Assessment 12 2.2.3 Model Development 13 2.2.4 The Classification Supervised ML Models 14 2.2.5 Model Evaluation 15 2.3 Results 16 2.3.1 Metal Characteristics of the Registered Dataset and Applicable Area 16 2.3.2 Spatial Assessment of Ecological Risk 20 2.3.3 Temporal Trend in Ecologic Risk 21 2.3.4 Performance Analysis of Classification Models 22 2.3.5 Resource Analysis Based on the Trained Model 23 2.4 Discussion 24 2.4.1 RF Outperformed the Other Models 24 2.4.2 Potential Sources of Metals in the Given Area 25 2.5 Conclusion 27 2.6 References 27 3 Elucidation of Environmental Factors’ Influence on Metals 33 3.1 Introduction 34 3.2 Materials and Methods 36 3.2.1 Study Sites and Data 36 3.2.2 Indexing Approach 39 3.2.3 Regression Models 40 3.2.4 Model Performance Metrics and Hyperparameter Tuning 41 3.2.5 SHapley Additive exPlanations (SHAP) 42 3.2.6 The Partial Dependence Plot (PDP) 42 3.3 Results 43 3.3.1 HPI and Environmental Variables 43 3.3.2 Assessment of Approaches 44 3.3.3 Global Feature Importance 46 3.3.4 Local Feature Importance 47 3.3.5 Sensitive Factor Analysis 49 3.4 Discussion 49 3.4.1 SHAP Outperformed the Other Importance Method 49 3.4.2 Robust Association of Top Variables with HPI 50 3.5 Conclusion 51 3.6 References 51 4 Long-Term Prediction of Metals Concentration 57 4.1 Introduction 58 4.2 Materials and Methods 60 4.2.1 Study Area and Water Quality Data 60 4.2.2 Input Identification 61 4.2.3 Wavelet Transform 63 4.2.4 Back-Propagation Neural Network (BPNN) Model 63 4.2.5 Nonlinear Autoregressive Exogenous (NARX) Model 65 4.2.6 Wavelet and BPNN (WNN) Hybrid Model 65 4.2.7 Wavelet and NARX (WNARX) Hybrid Model 66 4.2.8 Model Performance Evaluation 66 4.3 Results 67 4.3.1 Model Establishment 67 4.3.2 Performance Analysis of the Optimal Scenarios 70 4.4 Discussion 74 4.5 Conclusion 75 4.6 References 76 5 Prediction of Downstream River Flow 79 5.1 Introduction 80 5.2 Materials and Methods 82 5.2.1 Study Area and Data Acquisition 82 5.2.2 Convolutional Neural Network (CNN) 83 5.2.3 Long Short-Term Memory (LSTM) 83 5.2.4 River Flow Simulation 84 5.2.5 Performance Evaluation 84 5.3 Results 85 5.3.1 Flow Time Series 85 5.3.2 Input Selection 86 5.3.3 Flow Simulation 87 5.4 Discussion 90 5.5 Conclusion 91 5.6 References 92 6 Conclusions and Future Research 97 6.1 Traceability Study of Metals 97 6.2 Elucidation of Environmental Factors’ Influence on Metals 97 6.3 Long-Term Prediction of Metals Concentration 98 6.4 Prediction of River Flow 98 6.5 Discussion and Future Research 98 6.5.1 Discussion 99 6.5.2 Future Research 100 7 Appendices 103 7.1 Supporting Information for Traceability Study of Metals 103 7.1.1 Naive Bayes 103 7.1.2 Support Vector Machine 103 7.1.3 Neural Network 105 7.1.4 Random Forest 106 7.1.5 Long Short-Term Memory 106 7.1.6 Convolutional Neural Network 107 7.2 Supporting Information for Elucidation of Environmental Factors’ Influence on Metals 108 7.3 Supporting Information for Long-Term Prediction of Metals Concentration 109 7.3.1 Figures 109 7.4 References 120

The information service sector e.g. utilities, telecommunications and banking has grown rapidly in recent years and is a significant contributor to the Gross Domestic Product (GDP) of the world’s leading economies. Though, the information service sector has grown significantly, there have been relatively few attempts by researchers to explore this sector. The lack of research in this sector has motivated my PhD research that aims to explore the pre-established relationships between dependability, quality and customer satisfaction (RQ1) within the context of information service sector. Literature looking at the interrelationship between the dependability and quality (RQ2a), and their further impact on customer satisfaction (RQ2b) is also limited. With the understanding that Business to Business (B2B) and Business to Customer (B2C) businesses are different, exploring these relationships in these two different types of information firms will further add to existing literature. This thesis also attempts to investigate the relative significance of dependability and quality in both B2B and B2C information service firms (RQ3a and RQ3b). To address these issues, this PhD research follows a theory testing approach and uses multiple case studies to address the research questions. In total five cases from different B2B and B2C information service firms are being investigated. To explore the causality, the time series data set of over 24 to 60 months time and the ‘Path Analysis’ method has been used. For the generalization of the findings, Cumulative Meta Analysis method has been applied. The findings of this thesis indicate that dependability significantly affects customer satisfaction and an interrelationship exists between dependability and quality that further impacts customer satisfaction. The findings from B2C cases challenges the traditional priority afforded to relational aspect of quality by showing that dependability is the key driver of customer satisfaction. However, B2B cases findings shows that both dependability and quality are key drivers of customer satisfaction. Therefore, the findings of this thesis add considerably to literature in B2B and B2C information services context.

Made available in DSpace on 2016-04-28T14:58:38Z (GMT). No. of bitstreams: 1 2001 - Claudia de Souza.pdf: 251955 bytes, checksum: b69d1d8a6093e5f84e1aeb625a7a998e (MD5) Previous issue date: 2001-02-16 / The objective of this research was to study the relationship between non-conventional, or agroecological coffee production management and the quality of the product. This was a demand of the Association of Small Coffee Producers of Po?o Fundo, Minas Gerais State, Brazil. Six small farms were studied between the months of May and September 1996. Surveys were applied to farmers to find out about the main agricultural practices used by their coffee production. The following chemical and qualitative analyses were performed on the coffee drink from the studied areas: phenolics, poliphenoloxidases, color index and titrable acidity. Macro and micronutrients contents on coffee leaves and soil nutrient levels were evaluated. In addition, visual analysis was also carried out to assess the symptoms of leaf miner caused by Leucoptera coffeella Gu?rin-M?neville & Perrottet, 1842 Lepdoptera: Lyonetiidae (bicho-mineiro) and syndrome of caused by Phoma sp. The farms were typified according to the level of use of practices considered as agroecological or conventional. The results were analyzed by principal component analyses followed by cluster analyses. Two out of the six farms that used the most agroecological practices, formed a distinct group characterized by coffee drink of better quality. However, regarding soil fertility variables, a cluster was formed due to soil acidity. One of the farms using the most agroecological practices, clustered by itself for its lower soil pH, higher exchangeable Al+3 levels and lower Ca+2 levels. The variables related to leaf nutrient levels, were responsible for clustering farms with higher levels of agroecological practices. In this case, clustering took place by the lower levels of leaf S and B and higher levels of Ca and Mg. Also a trend of lower levels of leaf micronutrients on those properties was found. As a recommendation, more attention should be given to soil acidity and micronutrient replacement on those farms. Also, the agroecological practices used by the farms should be studied further since they resulted in better coffee quality. / O objetivo desse estudo foi estudar a rela??o entre o manejo n?o convencional, ou agroecol? gico e a qualidade da bebida de caf?, como resposta a demanda apresentada pela Associa??o de Pequenos Produtores de Po?o Fundo. Seis propriedades foram estudadas entre os meses de maio e setembro de 1996. Entrevistas semi-estruturadas foram aplicadas com objetivo de se conhecer o manejo efetuado nas lavouras de caf?. Foram realizadas an?lises qu?micas qualitativas da bebida do caf? nas ?reas amostradas quanto aos fen?licos totais, polifenoloxidase, ?ndice de cor e acidez titul?vel. Levantamento nutricional das propriedades tamb?m foram efetuados tanto no que diz respeito a an?lises foliares quanto de fertilidade do solo. Al?m disso, foi realizada a observa??o visual quanto ? incid?ncia de sintomas relacionados ? Leucoptera coffeella Gu?rin-M?neville & Perrottet, 1842 Lepdoptera: Lyonetiidae (bicho- mineiro) e da s?ndrome da seca de ponteiros, associados a Phoma sp. As propriedades foram tipificadas quanto ao grau de utiliza??o de pr?ticas consideradas como agroecol?gicas ou convencionais . Os resultados foram analisados por meio de an?lise de componentes principais, seguida de an?lise de conglomerados (clusters) indicaram que as duas propriedades que utilizavam o maior n?mero de pr?ticas agroecol?gicas formaram um grupo distinto, caracterizado por uma qualidade de bebida considerada superior ?s demais. Quanto ?s vari?veis de fertilidade do solo, observou-se um agrupamento relacionando ? acidez do solo; uma propriedade que utilizando o maior n?mero de pr?ticas agroecol?gicas se diferenciou das demais por formar um cluster na regi?o de baixa acidez no solo (pH mais elevado e menor teor de Al troc?vel), bem como menor teor de Ca. As vari?veis de teores foliares tamb?m separaram as duas propriedades de maior uso de pr?ticas agroecol?gicas das demais. Neste caso, a separa??o se deu devido aos menores teores de S e B e uma tend?ncia de maiores teores de Ca e Mg foliares. Os resultados indicam que, apesar do baixo uso de insumos industrializados, a boa qualidade da bebida foi mantida nas duas propriedades de maior grau de uso de pr?ticas agroecol?gicas , embora a acidez do solo estivesse alta (em uma das duas propriedades). H? uma tend?ncia de teores de alguns micronutrientes mais baixos nos cafeeiros dessas propriedades. Assim, como recomenda??es preliminares visando ? melhoria dos cafezais da regi?o estudada, uma maior aten??o deve ser dada aos aspectos de acidez do solo e restitui??o de S e B como tamb?m, as pr?ticas usadas na propriedade considerada como de maior uso de pr?ticas agroecol?gicas devem ser mais estudadas, pois, segundo este estudo relacionam-se a uma melhor qualidade de bebida de caf?.

The ability of humans to perceive significant pattern and structure of an image is something which humans take for granted. We can recognize objects and patterns independent of changes in image contrast and illumination. In the past decades, it has been widely recognized in both biology and computer vision that phase contains critical information in characterizing the structures in images. Despite the importance of local phase information and its significant success in many computer vision and image processing applications, the coherence behavior of local phases at scale-space is not well understood. This thesis concentrates on developing an invariant image representation method based on local phase information. In particular, considerable effort is devoted to study the coherence relationship between local phases at different scales in the vicinity of image features and to develop robust methods to measure the strength of this relationship. A computational framework that computes local phase coherence (LPC) intensity with arbitrary selections in the number of coefficients, scales, as well as the scale ratios between them has been developed. Particularly, we formulate local phase prediction as an optimization problem, where the objective function computes the closeness between true local phase and the predicted phase by LPC. The proposed framework not only facilitates flexible and reliable computation of LPC, but also broadens the potentials of LPC in many applications. We demonstrate the potentials of LPC in a number of image processing applications. Firstly, we have developed a novel sharpness assessment algorithm, identified as LPC-Sharpness Index (LPC-SI), without referencing the original image. LPC-SI is tested using four subject-rated publicly-available image databases, which demonstrates competitive performance when compared with state-of-the-art algorithms. Secondly, a new fusion quality assessment algorithm has been developed to objectively assess the performance of existing fusion algorithms. Validations over our subject-rated multi-exposure multi-focus image database show good correlations between subjective ranking score and the proposed image fusion quality index. Thirdly, the invariant properties of LPC measure have been employed to solve image registration problem where inconsistency in intensity or contrast patterns are the major challenges. LPC map has been utilized to estimate image plane transformation by maximizing weighted mutual information objective function over a range of possible transformations. Finally, the disruption of phase coherence due to blurring process is employed in a multi-focus image fusion algorithm. The algorithm utilizes two activity measures, LPC as sharpness activity measure along with local energy as contrast activity measure. We show that combining these two activity measures result in notable performance improvement in achieving both maximal contrast and maximal sharpness simultaneously at each spatial location.

The ability of humans to perceive significant pattern and structure of an image is something which humans take for granted. We can recognize objects and patterns independent of changes in image contrast and illumination. In the past decades, it has been widely recognized in both biology and computer vision that phase contains critical information in characterizing the structures in images. Despite the importance of local phase information and its significant success in many computer vision and image processing applications, the coherence behavior of local phases at scale-space is not well understood. This thesis concentrates on developing an invariant image representation method based on local phase information. In particular, considerable effort is devoted to study the coherence relationship between local phases at different scales in the vicinity of image features and to develop robust methods to measure the strength of this relationship. A computational framework that computes local phase coherence (LPC) intensity with arbitrary selections in the number of coefficients, scales, as well as the scale ratios between them has been developed. Particularly, we formulate local phase prediction as an optimization problem, where the objective function computes the closeness between true local phase and the predicted phase by LPC. The proposed framework not only facilitates flexible and reliable computation of LPC, but also broadens the potentials of LPC in many applications. We demonstrate the potentials of LPC in a number of image processing applications. Firstly, we have developed a novel sharpness assessment algorithm, identified as LPC-Sharpness Index (LPC-SI), without referencing the original image. LPC-SI is tested using four subject-rated publicly-available image databases, which demonstrates competitive performance when compared with state-of-the-art algorithms. Secondly, a new fusion quality assessment algorithm has been developed to objectively assess the performance of existing fusion algorithms. Validations over our subject-rated multi-exposure multi-focus image database show good correlations between subjective ranking score and the proposed image fusion quality index. Thirdly, the invariant properties of LPC measure have been employed to solve image registration problem where inconsistency in intensity or contrast patterns are the major challenges. LPC map has been utilized to estimate image plane transformation by maximizing weighted mutual information objective function over a range of possible transformations. Finally, the disruption of phase coherence due to blurring process is employed in a multi-focus image fusion algorithm. The algorithm utilizes two activity measures, LPC as sharpness activity measure along with local energy as contrast activity measure. We show that combining these two activity measures result in notable performance improvement in achieving both maximal contrast and maximal sharpness simultaneously at each spatial location.

Requirements Engineering and Software Quality Management is nowadays considered to be one of the important parts of the software development process. This thesis deals with connecting the theoretical concept of requirements engineering and usage of functions offered by Enterprise Architect tool, which should support the requirements engineering and software quality management processes. The main goal is to propose procedures of Enterprise Architect usage during the project in such a way, which will help to raise quality of the final product. The purpose is to offer a systematic and conscious way of implementation Enterprise Architect elements into software development processes, because this tool is able to support the whole cycle of the product. The methodology was designed as a result of theoretical concept, existing requirements engineering approaches and my own experience acquired during working on projects as a business and test analyst. The contribution of the designed methodology is in bringing recommendations, practices and procedures saying how to use Enterprise Architect during the software development process in order to gain the highest possible software quality. The thesis is structured in four sections describing the appropriate theory, analyzed requirement engineering approaches, critical mistakes during this process and the methodology design.

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