Tesi sul tema "Linhai nong chang"

Water resources management in the Sahel region of West Africa is extremely difficult because of high inter-annual rainfall variability as well as a general reduction of water availability in the region. Observed changes in streamflow directly disturb key socioeconomic activities such as the agriculture sector, which constitutes one of the main survival pillars of the West African population. Seasonal rainfall forecasting is considered as one possible way to increase resilience to climate variability by providing information in advance about the amount of rainfall expected in each upcoming rainy season. Moreover, the availability of reliable information about streamflow magnitude a few months before a rainy season will immensely benefit water users who want to plan their activities. However, since the 90s, several studies have attempted to evaluate the predictability of Sahelian weather characteristics and develop seasonal rainfall and streamflow forecast models to help stakeholders take better decisions. Unfortunately, two decades later, forecasting is still difficult, and forecasts have a limited value for decision-making. It is believed that the low performance in seasonal forecasting is due to the limits of commonly used predictors and forecast approaches for this region. In this study, new seasonal forecasting approaches are developed and new predictors tested in an attempt to predict the seasonal rainfall over the Sirba watershed located in between Niger and Burkina Faso, in West Africa. Using combined statistical methods, a pool of 84 predictors with physical links with the West African monsoon and its dynamics were selected, with their optimal lag times. They were first reduced through screening using linear correlation with satellite rainfall over West Africa. Correlation analysis and principal component analysis were used to keep the most predictive principal components. Linear regression was used to get synthetic forecasts, and the model was assessed to rank the tested predictors. The three best predictors, air temperature (from Pacific Tropical North), sea level pressure (from Atlantic Tropical South) and relative humidity (from Mediterranean East) were retained and tested as inputs for seasonal rainfall forecasting models. In this thesis it has been chosen to depart from the stationarity and linearity assumptions used in most seasonal forecasting methods: 1. Two probabilistic non-stationary methods based on change point detection were developed and tested. Each method uses one of the three best predictors. Model M1 allows for changes in model parameters according to annual rainfall magnitude, while M2 allows for changes in model parameters with time. M1 and M2 were compared to the classical linear model with constant parameters (M3) and to the linear model with climatology (M4). The model allowing changes in the predictand-predictor relationship according to rainfall amplitude (M1) and using AirTemp as a predictor was the best model for seasonal rainfall forecasting in the study area. 2. Non-linear models including regression trees, feed-forward neural networks and non-linear principal component analysis were implemented and tested to forecast seasonal rainfall using the same predictors. Forecast performances were compared using coefficients of determination, Nash-Sutcliffe coefficients and hit rate scores. Non-linear principal component analysis was the best non-linear model (R2: 0.46; Nash: 0.45; HIT: 60.7), while the feed-forward neural networks and regression tree models performed poorly. All the developed rainfall forecasting methods were subsequently used to forecast seasonal annual mean streamflow and maximum monthly streamflow by introducing the rainfall forecasted in a SWAT model of the Sirba watershed, and the results are summarized as follows: 1. Non-stationary models: Models M1 and M2 were compared to models M3 and M4, and the results revealed that model M3 using RHUM as a predictor at a lag time of 8 months was the best method for seasonal annual mean streamflow forecasting, whereas model M1 using air temperature as a predictor at a lag time of 4 months was the best model to predict maximum monthly streamflow in the Sirba watershed. Moreover, the calibrated SWAT model achieved a NASH value of 0.83. 2. Non-linear models: The seasonal rainfall obtained from the non-linear principal component analysis model was disaggregated into daily rainfall using the method of fragment, and then fed into the SWAT hydrological model to produce streamflow. This forecast was fairly acceptable, with a Nash value of 0.58. The evaluation of the level of risk associated with each seasonal forecast was carried out using a simple risk measure: the probability of overtopping of the flood protection dykes in Niamey, Niger. A HEC-RAS hydrodynamic model of the Niger River around Niamey was developed for the 1980-2014 period, and a copula analysis was used to model the dependence structure of streamflows and predict the distribution of streamflow in Niamey given the predicted streamflow on the Sirba watershed. Finally, the probabilities of overtopping of the flood protection dykes were estimated for each year in the 1980-2014 period. The findings of this study can be used as a guideline to improve the performance of seasonal forecasting in the Sahel. This research clearly confirmed the possibility of rainfall and streamflow forecasting in the Sirba watershed at a seasonal time scale using potential predictors other than sea surface temperature.

Thesis (Ph.D.)--University of Cincinnati, 2008.
Advisor: Xinhao Wang PhD (Committee Chair), David Edelman PhD (Committee Member), Jan Fritz PhD (Committee Member), William Shuster PhD (Committee Member). Title from electronic thesis title page (viewed Apr. 18, 2010). Keywords: Land cover change; cellular automata; TN loading model; non-linear regression; open space conservation network. Includes abstract. Includes bibliographical references.

Urban environments have been identified as a non-point source contributor of nutrient loadings into watersheds. Interannual surges of nutrient loadings into local water systems are more damaging than mean interannual nutrient loadings. Virginia has outlined the need to reduce urban nutrient loadings. Mean interannual nutrient loadings and interannual nutrient loadings variability are expected to increase under climate change (CC). However, there are few studies that provide a predictive framework for abating nutrient loadings under CC. Thus, there is a lack of information regarding how effective water quality policy will be in the future. Using the Difficult Run watershed in Fairfax County, VA, as a site of study, we used mathematical programming to compare how the costs of abating nutrient loads differed under differing climates in the Mid-Atlantic. We first compared the costs of abating mean interannual nutrient loadings in the watershed based on historical climate conditions to those predicted for CC. We then evaluated how changes in the interannual variability of nutrient loadings for CC affect the costs of meeting watershed goals. We found that abating mean interannual nutrient loadings was substantially costlier for CC relative to meeting the same goals under historical climate conditions. Further, we found that the costs of abating interannual nutrient loadings variability increased under CC relative to meeting the same goals under historical climate. One implication of this study suggests that policy makers seeking to meet water quality goals over time must front-load supplemental BMPs today in order to offset the changes predicted for CC.
Master of Science

This thesis is a population-based ecological study designed to investigate the issue of mortality displacement (or "harvesting" effect) in the assessment of temperature-related deaths in Brisbane, Australia. It examines the temperature impacts on mortality, and assesses the harvesting effects on the temperature–related deaths. This study contributes to the knowledge base of understanding the temperature-mortality relationship and assists in formulating and evaluating public health intervention strategies within the context of climate change.

The health impacts of exposure to ambient temperature have been drawing increasing attention from the environmental health research community, government, society, industries, and the public. Case-crossover and time series models are most commonly used to examine the effects of ambient temperature on mortality. However, some key methodological issues remain to be addressed. For example, few studies have used spatiotemporal models to assess the effects of spatial temperatures on mortality. Few studies have used a case-crossover design to examine the delayed (distributed lag) and non-linear relationship between temperature and mortality. Also, little evidence is available on the effects of temperature changes on mortality, and on differences in heat-related mortality over time. This thesis aimed to address the following research questions: 1. How to combine case-crossover design and distributed lag non-linear models? 2. Is there any significant difference in effect estimates between time series and spatiotemporal models? 3. How to assess the effects of temperature changes between neighbouring days on mortality? 4. Is there any change in temperature effects on mortality over time? To combine the case-crossover design and distributed lag non-linear model, datasets including deaths, and weather conditions (minimum temperature, mean temperature, maximum temperature, and relative humidity), and air pollution were acquired from Tianjin China, for the years 2005 to 2007. I demonstrated how to combine the case-crossover design with a distributed lag non-linear model. This allows the case-crossover design to estimate the non-linear and delayed effects of temperature whilst controlling for seasonality. There was consistent U-shaped relationship between temperature and mortality. Cold effects were delayed by 3 days, and persisted for 10 days. Hot effects were acute and lasted for three days, and were followed by mortality displacement for non-accidental, cardiopulmonary, and cardiovascular deaths. Mean temperature was a better predictor of mortality (based on model fit) than maximum or minimum temperature. It is still unclear whether spatiotemporal models using spatial temperature exposure produce better estimates of mortality risk compared with time series models that use a single site’s temperature or averaged temperature from a network of sites. Daily mortality data were obtained from 163 locations across Brisbane city, Australia from 2000 to 2004. Ordinary kriging was used to interpolate spatial temperatures across the city based on 19 monitoring sites. A spatiotemporal model was used to examine the impact of spatial temperature on mortality. A time series model was used to assess the effects of single site’s temperature, and averaged temperature from 3 monitoring sites on mortality. Squared Pearson scaled residuals were used to check the model fit. The results of this study show that even though spatiotemporal models gave a better model fit than time series models, spatiotemporal and time series models gave similar effect estimates. Time series analyses using temperature recorded from a single monitoring site or average temperature of multiple sites were equally good at estimating the association between temperature and mortality as compared with a spatiotemporal model. A time series Poisson regression model was used to estimate the association between temperature change and mortality in summer in Brisbane, Australia during 1996–2004 and Los Angeles, United States during 1987–2000. Temperature change was calculated by the current day's mean temperature minus the previous day's mean. In Brisbane, a drop of more than 3 �C in temperature between days was associated with relative risks (RRs) of 1.16 (95% confidence interval (CI): 1.02, 1.31) for non-external mortality (NEM), 1.19 (95% CI: 1.00, 1.41) for NEM in females, and 1.44 (95% CI: 1.10, 1.89) for NEM aged 65.74 years. An increase of more than 3 �C was associated with RRs of 1.35 (95% CI: 1.03, 1.77) for cardiovascular mortality and 1.67 (95% CI: 1.15, 2.43) for people aged < 65 years. In Los Angeles, only a drop of more than 3 �C was significantly associated with RRs of 1.13 (95% CI: 1.05, 1.22) for total NEM, 1.25 (95% CI: 1.13, 1.39) for cardiovascular mortality, and 1.25 (95% CI: 1.14, 1.39) for people aged . 75 years. In both cities, there were joint effects of temperature change and mean temperature on NEM. A change in temperature of more than 3 �C, whether positive or negative, has an adverse impact on mortality even after controlling for mean temperature. I examined the variation in the effects of high temperatures on elderly mortality (age . 75 years) by year, city and region for 83 large US cities between 1987 and 2000. High temperature days were defined as two or more consecutive days with temperatures above the 90th percentile for each city during each warm season (May 1 to September 30). The mortality risk for high temperatures was decomposed into: a "main effect" due to high temperatures using a distributed lag non-linear function, and an "added effect" due to consecutive high temperature days. I pooled yearly effects across regions and overall effects at both regional and national levels. The effects of high temperature (both main and added effects) on elderly mortality varied greatly by year, city and region. The years with higher heat-related mortality were often followed by those with relatively lower mortality. Understanding this variability in the effects of high temperatures is important for the development of heat-warning systems. In conclusion, this thesis makes contribution in several aspects. Case-crossover design was combined with distribute lag non-linear model to assess the effects of temperature on mortality in Tianjin. This makes the case-crossover design flexibly estimate the non-linear and delayed effects of temperature. Both extreme cold and high temperatures increased the risk of mortality in Tianjin. Time series model using single site’s temperature or averaged temperature from some sites can be used to examine the effects of temperature on mortality. Temperature change (no matter significant temperature drop or great temperature increase) increases the risk of mortality. The high temperature effect on mortality is highly variable from year to year.

The ongoing European Refugee Crisis has been one of the most popular trending topics on Twitter for the past 8 months. This paper applies topic modeling on bulks of tweets to discover the hidden patterns within these social media discussions. In particular, we perform topic analysis through solving Non-negative Matrix Factorization (NMF) as an Inexact Alternating Least Squares problem. We accelerate the computation using techniques including tweet sampling and augmented NMF, compare NMF results with different ranks and visualize the outputs through topic representation and frequency plots. We observe that supportive sentiments maintained a strong presence while negative sentiments such as safety concerns have emerged over time.

Etude de problemes de commande d'un systeme thermique, le modele considere etant un systeme non lineaire a parametres repartis. La non-linearite peut etre due a la variation des caracteristiques thermophysiques du systeme en fonction de la temperature ou a un phenomene de changement de phase entrainant un saut dans l'enthalpie du systeme

As Earth's climate is rapidly changing, the impact of ambient temperature on health outcomes has attracted increasing attention in the recent time. Considerable number of excess deaths has been reported because of exposure to ambient hot and cold temperatures. However, relatively little research has been conducted on the relation between temperature and morbidity. The aim of this study was to characterize the relationship between both hot and cold temperatures and emergency hospital admissions in Brisbane, Australia, and to examine whether the relation varied by age and socioeconomic factors. It aimed to explore lag structures of temperature–morbidity association for respiratory causes, and to estimate the magnitude of emergency hospital admissions for cardiovascular diseases attributable to hot and cold temperatures for the large contribution of both diseases to the total emergency hospital admissions. A time series study design was applied using routinely collected data of daily emergency hospital admissions, weather and air pollution variables in Brisbane during 1996–2005. Poisson regression model with a distributed lag non-linear structure was adopted to assess the impact of temperature on emergency hospital admissions after adjustment for confounding factors. Both hot and cold effects were found, with higher risk of hot temperatures than that of cold temperatures. Increases in mean temperature above 24.2oC were associated with increased morbidity, especially for the elderly ≥ 75 years old with the largest effect. The magnitude of the risk estimates of hot temperature varied by age and socioeconomic factors. High population density, low household income, and unemployment appeared to modify the temperature–morbidity relation. There were different lag structures for hot and cold temperatures, with the acute hot effect within 3 days after hot exposure and about 2-week lagged cold effect on respiratory diseases. A strong harvesting effect after 3 days was evident for respiratory diseases. People suffering from cardiovascular diseases were found to be more vulnerable to hot temperatures than cold temperatures. However, more patients admitted for cardiovascular diseases were attributable to cold temperatures in Brisbane compared with hot temperatures. This study contributes to the knowledge base about the association between temperature and morbidity. It is vitally important in the context of ongoing climate change. The findings of this study may provide useful information for the development and implementation of public health policy and strategic initiatives designed to reduce and prevent the burden of disease due to the impact of climate change.

Die vermiedene tropische Entwaldung und zusätzliche Aufforstungen sind von zentraler Bedeutung für die Klimawandelvermeidung, üben aber zusätzlichen Druck auf die globalen Landressourcen zur Produktion von Nahrungsgütern, Futtermittel, Fasern, Bioenergie und Rundholz dar. Die Ziele der Studie beziehen sich auf die Analyse der Verzichtskosten in der Land- und Forstwirtschaft sowie das Potential zur Klimawandelvermeidung in globalen Wäldern durch normative und marktbasierte Klimaschutzprogramme. Das globale ökonomische Landnutzungsmodell ''Model of Agricultural Production and its Impact on the Environment'' (MAgPIE) wurde um eine konsistente Landnutzungsdatenbank und den Forstsektor erweitert. Es simuliert die räumlich-explizite Landnutzung und deren Änderungen während die Kosten land- und forstwirtschaftlicher Produktion unter gegebener Nachfrage nach Gütern geschätzt werden. Szenarien zu Klimaschutzmaßnahmen werden verglichen mit Referenzszenarien über Zeithorizonte bis zum Jahr 2100. Die Ergebnisse verweisen auf ein begrenztes Mitigationspotential normativen tropischen Waldschutzes zu geringen zusätzlichen Kosten in der Landwirtschaft. Lateinamerika profitiert von ausreichenden Landreserven und geringem Anstieg in der Güternachfrage und geringer Referenzentwaldung. Die Verlagerung von Emissionen durch regionalen Waldschutz hat Auswirkungen auf die sektoralen Produktionskosten und verringert das globale ökonomische Potential. Die Schlussfolgerungen betreffen 1) den Bedarf an substantieller Ertragssteigerung in Sub-Sahara Afrika als Voraussetzung für die erfolgreiche Umsetzung vermiedener Entwaldung, 2) die erhöhte Gefahr der Verlagerung von Emissionen aus Entwaldung durch die Umsetzung regionaler Klimaschutzprogramme und der Liberalisierung des Holzhandels, 3) das hohe ökonomische Potential integrierter Klimaschutzprogramme zu moderaten Verzichtskosten, sowie 4) die Notwendigkeit zusätzlicher Forschung bezüglich der Unsicherheiten in Parametern und Modellprozessen.
Avoiding tropical deforestation and additional afforestation are of primary importance for climate change mitigation but exert additional pressure on global land resources for the production of food, feed, fibre, bioenergy and timber. The study objectives relate to the analysis of the foregone economic benefits, the opportunity costs, in agriculture and forestry and the climate change mitigation potential of global forests in normative and market-based programmes. The global economic ‘Model of Agricultural Production and its Impact on the Environment'' (MAgPIE) has been extended by a consistent land use database and the forestry sector. It simulates spatially-explicit land use and land use changes while estimating the costs of production in agriculture and forestry to satisfy a prescribed demand. Climate change mitigation scenarios are contrasted to baselines for time horizons up to the year 2100. The results show the limited mitigation potential of normative forest conservation in tropical regions at low additional costs in agriculture. Latin America benefits from sufficient land endowments and low increases in crop demand leading to relatively low baseline deforestation. The displacement of carbon emissions between regions impacts the regional agriculture and forestry production costs and reduces the global economic potential. The conclusions pertain to the 1) need for high rates of yield increase in Sub-Saharan Africa as a precondition for successfully avoided deforestation, 2) increased threat of regional carbon emission leakage from implementing mitigation programmes and liberalized trade of timber, 3) high economic potential of climate change mitigation from integrating afforestation and avoided deforestation at moderate costs, and 4) additional research needs to account for significant uncertainties from growth and cost parameters and model processes.

Nous étudions le comportement asymptotique de statistiques ou fonctionnelles liées à des processus à longue mémoire saisonnière. Nous nous concentrons sur les lignes de Donsker et sur le processus empirique. Les suites considérées sont de la forme $G(X_n)$ où $(X_n)$ est un processus gaussien ou linéaire. Nous montrons que les résultats que Taqqu et Dobrushin ont obtenus pour des processus à longue mémoire dont la covariance est à variation régulière à l'infini peuvent être en défaut en présence d'effets saisonniers. Les différences portent aussi bien sur le coefficient de normalisation que sur la nature du processus limite. Notamment nous montrons que la limite du processus empirique bi-indexé, bien que restant dégénérée, n'est plus déterminée par le degré de Hermite de la fonction de répartition des données. En particulier, lorsque ce degré est égal à 1, la limite n'est plus nécessairement gaussienne. Par exemple on peut obtenir une combinaison de processus de Rosenblatt indépendants. Ces résultats sont appliqués à quelques problèmes statistiques comme le comportement asymptotique des U-statistiques, l'estimation de la densité et la détection de rupture.

Cette thèse s’intéresse d’une part aux effets macroéconomiques des régimes de change, et d’autre part, aux récentes évolutions sur la politique budgétaire et la décentralisation. La partie I met essentiellement l’accent sur l’interaction entre les régimes de change (RC) et la politique budgétaire, monétaire et fiscale. Tout d’abord, nous mettons en évidence que les RC peuvent avoir un effet stabilisateur sur la politique budgétaire (chapitre 1). Cependant, cet effet stabilisateur des RC n’est pas automatique mais dépendrait plutôt des conséquences d’une politique budgétaire laxiste. Le chapitre 2 s’intéresse quant à lui à la causalité entre RC et crises (bancaire/financière, de change et de dette) et remet en cause la vision bipolaire qui prétendait que les RC intermédiaires sont plus vulnérables aux crises que les solutions en coin (RC fixes/flexibles). Il ressort de notre analyse que les fondamentaux macroéconomiques (la volatilité du crédit au secteur privé, le financement du déficit, et le ratio dette sur PIB) jouent un rôle considérable. Le chapitre 3 met en évidence un lien entre les RC et la politique fiscale. Les pays à RC fixes montrent une plus grande dépendance aux recettes domestiques –telles que la TVA-, comparativement aux pays en change intermédiaires/flexibles pour compenser les pertes de recettes de seigneuriage (effet de substitution). De plus, ces pays avec RC fixes collectent plus de recettes domestiques en compensation de la perte de recettes douanières, suite à la libéralisation commerciale (effet de compétitivité). Dans les trois derniers chapitres (partie II), nous mettons le focus sur la politique budgétaire et les effets de la décentralisation. Le chapitre 4 révèle une relation non-linéaire entre la politique budgétaire et le cycle économique, qui dépend du niveau de la dette publique. Lorsque celle-ci dépasse un certain seuil (87%), la politique budgétaire perd toute propriété contra-cyclique. Nous montrons par ailleurs que l’effet disciplinaire ex-ante des règles budgétaires aide à restaurer la contra-cyclicité de la politique budgétaire. A travers le chapitre 5, nous montrons que la décentralisation budgétaire, dans un cadre politico-institutionnel sein et dépourvu de corruption, améliore l’offre de biens et services publics. Le chapitre 6 conclut que la décentralisation impacte positivement le solde structurel. Cependant une asymétrie entre la décentralisation des dépenses et celle des recettes accroit la dépendance des gouvernements locaux vis-à-vis du gouvernement central en termes de transferts, et amoindrirait considérablement à l’effet positif de la décentralisation
This thesis explored, in two parts, the macroeconomic impacts of exchange rate regimes (ERR), as well as the recent developments in fiscal policy and fiscal decentralization. Part I has reconsidered the role of ERR and its interplay with fiscal, monetary and tax policy. The first result that emerges (Chapter 1) is that fixed ERR can serve as a credible policy tool for stabilizing fiscal policy. However, this stabilizing effect is conditional upon the inter-temporal distribution of the costs of loose fiscal policy. In assessing the linkage between ERR and crises (banking/financial, currency and debt), Chapter 2 evidenced that the bipolar view is no longer valid, and that, crisis proneness rather depends on the macroeconomic fundamentals (the volatility of private sector credit, the deficit-financing mechanism, and the debt-to-GDP ratio). In Chapter 3, we unveiled a strong relationship between ERR and tax policy. Countries with pegged regimes have greater reliance on domestic taxation -such as the VAT- to make up for the loss of seigniorage revenue (substitution effect). Moreover, peggers tend to collect more VAT revenue to offset the shortfall in cross border taxes following the trade liberalization reform (competitiveness effect). Part II discussed the cyclical response of fiscal policy in high debt periods, and focused on fiscal decentralization issues. In Chapter 4, we showed that the reaction of fiscal policy to the business cycle is non-linear and conditional to the level of public debt. When the debt-to-GDP ratio goes beyond a certain threshold (87%), fiscal policy loses its counter-cyclical properties. Further, we highlighted that carefully-designed fiscal rules help maintaining counter-cyclicality through an ex ante disciplinary effect. Chapters 5 and 6 analyzed the impact of fiscal decentralization on the efficiency of public service delivery and fiscal policy performance, respectively. Chapter 5 revealed that a sufficient level of expenditure decentralization, coupled with revenue decentralization, improves the efficiency of public service delivery. However, the political and institutional environment is critical for reaping decentralization-led benefits. Lastly, Chapter 6 concluded that fiscal decentralization has destabilizing effect by reducing the counter-cyclicality of fiscal policy. In addition, we found that decentralization strengthens the structural fiscal balance; however, vertical fiscal imbalances reduce the benefits of decentralization. It is therefore critical to limit asymmetries between expenditure and revenue decentralization, so as to reduce the transfer-dependency of local governments to the central level, and thus prevent decentralization from weakening the fiscal stance at the general government level

Droop-Nose Leading Edge (DNLE) morphing wings are one of the most promising devices in order to achieve aerodynamic drag and noise reduction during take-off and landing phases. An accurate design of these structures could lead to the decrease of aircraft fuel consumption in the perspective of reaching a greener aviation, following the objectives indicated by Flightpath 2050 issued by the E.U. However, due to the challenges related to the realization of this technology and TRL reached, DNLE are more likely implemented in Unmanned Aerial Systems (UAS) for testing and evaluation purposes. In the present study, an optimization methodology for the DNLE composite laminate skin and morphing mechanism structure is proposed and applied to a study case represented by the UAS-S45 aircraft. The work starts from the morphing leading edge structure developed by the LARCASE laboratory at ETS Montreal. The results showed that by means of the optimization strategy adopted, the force required on the actuator mechanism is 88% lower than the original design. A significant improvement on the profile smoothness along its section and in the spanwise direction in morphing conditions has been obtained too. However, further investigations are still needed in order to achieve a more appropriate morphing shape. Despite this, it appears from the results obtained that the proposed methodology can be useful to tackle the DNLE design problem in an effective and efficient way. What developed in this work has been conceived to support the investigation of DNLE in the small leading edge profiles typical of the UAS. In this way, an easier procedure for the set up of the design flow, and a decrease in the computational effort for the optimization process can be obtained. An experimental validation of the results obtained is currently being performed at ETS, and future development regards the assessment of the errors of the numeric procedure herein presented respect to real data.

To survive and thrive in today’s business environment requires the ability to implement transformative change to meet market demands. However, the hidden and often mystical nature of implementing transformative change has scholars referring to it as a ‘black box’, with others highlighting the constant and significant failure rate at this stage, often pointing to the inability of linear methods to manage the environmental context. The aim of this study is to explore the process of implementing transformative change in today’s business environment. A conceptual framework guides the study to explore the ‘how’, ‘what’ and ‘why’ aspects, building off the real-world experience of change practitioners based across Australia and New Zealand. A mixed-methods approach is utilised, applying qualitative and quantitative analysis through semi-structured interviews and an online survey. The interviews provide in-depth insight from practitioners leading major transformation programs, whilst the online survey provides the ability to converge and corroborate findings. The findings from this study highlight the need to move to a non-linear process that can manage the dynamic nature of transformative change in the business world today. Firstly, the need for the future state goal to be articulated as a ‘Target Operating Model’ (TOM), the fulcrum of the implementation process. Second, the necessity of the planning design to manage the ‘Delta Effect’, the impacts from the constantly changing environment. Finally, presenting a model that supports a non-linear process, providing a framework for implementing transformative change in today’s business environment. The model and framework from this study provide a practical contribution for those involved with delivering transformative change. For academia, the research builds on and extends further insights and knowledge into this significant process used extensively across our world today.

The research focused using System Dynamics to model and simulate an engineering project with the main aim of understanding: - Why change orders are notorious for negatively impacting on project execution; - The root cause(s) of the behavior in order to find ways to better manage change orders in future projects; The research was carried out at a leading KZN-based engineering consultancy using data from a recently completed project as a basis for the model. The research took the following approach and sequence: Introduction: In this section I present the dominant school of thought, the reductionist scientific perspective and its strengths. I then highlight the weakness of the school and present systems thinking as an alternative way of viewing life issues. I then propose system dynamics as one of the better methodologies that can help us understand a dynamic and non-linear system. Literature Survey: In this section I review literature on project management with the primary aim of highlighting that projects, regardless of size, are complex non-linear systems. I then cover literature on system dynamics with the aim of justifying my perspective, that it is suitable for application in the project management context. Research Methodology and Results Analysis: This section presents the methodology I followed in executing the research. The research process started off with extensive data reviewing from a recently completed project. It also covered conversations with the research participants in order to help me fully understand the project that was to be modelled. The data reviewing and interviews culminated in a group model building exercise where a number of “what if” scenarios were explored and discussed with the participants. The final stage of the research was to get the participants to respond to a post-modelling questionnaire. The outcomes from these processes were then used to answer the original research questions and to draw any additional insights. The resultant model can now be used as a learning tool for teaching clients of the unintended consequences that can result from issuing change orders. Conclusion: I then close off the research by concluding that change orders do have a non-linear impact on project execution and they require careful management. I then suggest that the best way to manage this is by educating all the project participants, especially the client of how their well meaning requests can be detrimental to the project if not well managed. Additionally it was surprising to all participants that for some reason, exploration of change orders that are not approved is rarely ever charged for. This “work for no pay” can negatively impact on the financial situation of the service provider which may have a knock-on effect to other areas of the project. Value: This research eventually revealed itself to be about learning to effectively lead a group modeling exercise and what pitfalls to look out for when creating models. There is great value for people interested in finding progressive and well informed ways for model building and managing change orders in projects. This system dynamics in project management research is grounded on the concepts of the learning organization and systems thinking as the core drivers.
Thesis (M.Com.)-University of KwaZulu-Natal, Westville, 2011.

碩士
淡江大學
財務金融學系碩士班
100
This study uses oil price change rate as a threshold variable to analyze relationship between OTC stock returns and oil price change rate, exchange rate and S&P 500 change rate. The analysis can deduce whether exist smooth transition effect or not. The empirical results indicate that only with transportation stock and construction stock have structural change. Before structural change, oil price change rate and S&P 500 change rate affect stock returns positively. However, the relationships between exchange rate and stock returns are negative. After structural change, oil price change rate and exchange rate have negative effect on stock returns. The relationships between S&P 500 and stock returns are still positive.

碩士
國立臺灣大學
商學研究所
107
Brand image change becomes more important nowadays because consumers receive much more information on Internet on a daily basis, so the new needs of consumers is formed rapidly. In order to fulfill the rapidly emerging needs, firms need to promptly change their brand images. However, although the brand evolution and brand extension literature has revealed possible consequences of brand image change, scant studies have investigated whether high, median, and low levels brand image change will differently affect market reaction. Based on the brand incongruity theory, the current research posits that the relationship between brand image change and market reaction follows an inverted U shape. This research studies the fan reaction toward the image change of Korean pop music idol groups. According to the results of an ordinary linear regression analysis, this research reveals an inverted U relationship between brand image change and market reaction. Besides, this research captures the extent of brand image change and gives the idol groups’ company a lesson that the idol groups should not change their images extremely but gradually change their images so that they can ensure their original fandoms.

碩士
淡江大學
財務金融學系碩士班
104
The main purpose of this study is to understand the changes in macroeconomic variables on the impact of Taiwan''s textile industry profitability. Global variables use inflation rates, unemployment rate, and exchange rates to use Oil Change Rate as the threshold variable to study changes in the macroeconomic variables how to effect profitability of the textile industry, whether caused by non-linear effects. Using Granger and Teräsvirta (1993) and Teräsvirta (1994) proposed smooth transition regression model, results showed: when Oil Change Rate is greater than the threshold value, the Oil Change Rate, the unemployment rate, and exchange rate for textile industry showed positive effects on profitability, while inflation has negative effects; when Oil Change Rate is less than the threshold value, the Oil Change Rate, exchange rate for textile industry showed positive effects on profitability, but its influence has declined, the impact of the unemployment rate to negative to influence, while inflation is still showing a negative effect.

abstract: City administrators and real-estate developers have been setting up rather aggressive energy efficiency targets. This, in turn, has led the building science research groups across the globe to focus on urban scale building performance studies and level of abstraction associated with the simulations of the same. The increasing maturity of the stakeholders towards energy efficiency and creating comfortable working environment has led researchers to develop methodologies and tools for addressing the policy driven interventions whether it’s urban level energy systems, buildings’ operational optimization or retrofit guidelines. Typically, these large-scale simulations are carried out by grouping buildings based on their design similarities i.e. standardization of the buildings. Such an approach does not necessarily lead to potential working inputs which can make decision-making effective. To address this, a novel approach is proposed in the present study. The principle objective of this study is to propose, to define and evaluate the methodology to utilize machine learning algorithms in defining representative building archetypes for the Stock-level Building Energy Modeling (SBEM) which are based on operational parameter database. The study uses “Phoenix- climate” based CBECS-2012 survey microdata for analysis and validation. Using the database, parameter correlations are studied to understand the relation between input parameters and the energy performance. Contrary to precedence, the study establishes that the energy performance is better explained by the non-linear models. The non-linear behavior is explained by advanced learning algorithms. Based on these algorithms, the buildings at study are grouped into meaningful clusters. The cluster “mediod” (statistically the centroid, meaning building that can be represented as the centroid of the cluster) are established statistically to identify the level of abstraction that is acceptable for the whole building energy simulations and post that the retrofit decision-making. Further, the methodology is validated by conducting Monte-Carlo simulations on 13 key input simulation parameters. The sensitivity analysis of these 13 parameters is utilized to identify the optimum retrofits. From the sample analysis, the envelope parameters are found to be more sensitive towards the EUI of the building and thus retrofit packages should also be directed to maximize the energy usage reduction.
Dissertation/Thesis
Masters Thesis Architecture 2017

Cette thèse est organisée en trois chapitres où sont développées des méthodes de simulation à posteriori pour inférence Bayesienne dans des modèles espace-état ainsi que des modèles économétriques pour l’analyse de données financières. Au chapitre 1, nous considérons le problème de simulation a posteriori dans les modèles espace-état univariés et non-Gaussiens. Nous proposons une nouvelle méthode de Monte-Carlo par chaînes de Markov (MCMC) mettant à jour le vecteur de paramètres de la dynamique d’état ainsi que la séquence de variables d’état conjointement dans un bloc unique. La proposition MCMC est tirée en deux étapes: la distribution marginale du vecteur de paramètres de la dynamique d’état est construite en utilisant une approximation du gradient et du Hessien du logarithme de sa densité a posteriori, pour laquelle le vecteur de variables d’état a été intégré. La distribution conditionnelle de la séquence de variables d’état, étant donné la proposition du vecteur de paramètres, est telle que décrite dans McCausland (2012). Le calcul du gradient et du Hessien approximatif combine des sous-produits de calcul du tirage d’état avec une quantité modeste de calculs supplémentaires. Nous comparons l’efficacité numérique de notre simulation a posteriori à celle de la méthode Ancillarity-Sufficiency Interweaving Strategy (ASIS) décrite dans Kastner & Frühwirth-Schnatter (2014), en utilisant un modèle de volatilité stochastique Gaussien et le même panel de 23 taux de change quotidiens utilisé dans ce même article. Pour calculer la moyenne a posteriori du paramètre de persistance de la volatilité, notre efficacité numérique est de 6 à 27 fois plus élevée; pour la volatilité du paramètre de volatilité, elle est de 18 à 53 fois plus élevée. Nous analysons dans un second exemple des données de compte de transaction avec un modèle Poisson et Gamma-Poisson dynamique. Malgré la nature non Gaussienne des données de compte, nous obtenons une efficacité numérique élevée, guère inférieure à celle rapportée dans McCausland (2012) pour une méthode d’échantillonnage impliquant un calcul préliminaire de la forme de la distribution a posteriori statique des paramètres. Au chapitre 2, nous proposons un nouveau modèle de durée conditionnelle stochastique (SCD) pour l’analyse de données de transactions financières en haute fréquence. Nous identifions certaines caractéristiques indésirables des densités de durée conditionnelles paramétriques existantes et proposons une nouvelle famille de densités conditionnelles flexibles pouvant correspondre à une grande variété de distributions avec des fonctions de taux de probabilité modérément variable. Guidés par des considérations théoriques issues de la théorie des files d’attente, nous introduisons des déviations non-paramétriques autour d’une distribution exponentielle centrale, qui, selon nous, est un bon modèle de premier ordre pour les durées financières, en utilisant une densité de Bernstein. La densité résultante est non seulement flexible, dans le sens qu’elle peut s’approcher de n’importe quelle densité continue sur [0, ∞) de manière arbitraire, à condition qu’elle se compose d’un nombre suffisamment grand de termes, mais également susceptible de rétrécissement vers la distribution exponentielle. Grâce aux tirages très efficaces des variables d’état, l’efficacité numérique de notre simulation a posteriori se compare très favorablement à celles obtenues dans les études précédentes. Nous illustrons nos méthodes à l’aide des données de cotation d’actions négociées à la Bourse de Toronto. Nous constatons que les modèles utilisant notre densité conditionnelle avec moins de qua- tre termes offrent le meilleur ajustement. La variation régulière trouvée dans les fonctions de taux de probabilité, ainsi que la possibilité qu’elle ne soit pas monotone, aurait été impossible à saisir avec une spécification paramétrique couramment utilisée. Au chapitre 3, nous présentons un nouveau modèle de durée stochastique pour les temps de transaction dans les marchés d’actifs. Nous soutenons que les règles largement acceptées pour l’agrégation de transactions apparemment liées induisent une inférence erronée concernant les durées entre des transactions non liées: alors que deux transactions exécutées au cours de la même seconde sont probablement liées, il est extrêmement improbable que toutes paires de transactions le soient, dans un échantillon typique. En plaçant une incertitude sur les transactions liées dans notre modèle, nous améliorons l’inférence pour la distribution de la durée entre les transactions non liées, en particulier près de zéro. Nous proposons un modèle en temps discret pour les temps de transaction censurés permettant des valeurs nulles excessives résultant des durées entre les transactions liées. La distribution discrète des durées entre les transactions indépendantes découle d’une densité flexible susceptible de rétrécissement vers une distribution exponentielle. Dans un exemple empirique, nous constatons que la fonction de taux de probabilité conditionnelle sous-jacente pour des durées (non censurées) entre transactions non liées varie beaucoup moins que celles trouvées dans la plupart des études; une distribution discrète pour les transactions non liées basée sur une distribution exponentielle fournit le meilleur ajustement pour les trois séries analysées. Nous prétendons que c’est parce que nous évitons les artefacts statistiques qui résultent de règles déterministes d’agrégation des échanges et d’une distribution paramétrique inadaptée.
This thesis is organized in three chapters which develop posterior simulation methods for Bayesian inference in state space models and econometrics models for the analysis of financial data. In Chapter 1, we consider the problem of posterior simulation in state space models with non-linear non-Gaussian observables and univariate Gaussian states. We propose a new Markov Chain Monte Carlo (MCMC) method that updates the parameter vector of the state dynamics and the state sequence together as a single block. The MCMC proposal is drawn in two steps: the marginal proposal distribution for the parameter vector is constructed using an approximation of the gradient and Hessian of its log posterior density, with the state vector integrated out. The conditional proposal distribution for the state sequence given the proposal of the parameter vector is the one described in McCausland (2012). Computation of the approximate gradient and Hessian combines computational by-products of the state draw with a modest amount of additional computation. We compare the numerical efficiency of our posterior simulation with that of the Ancillarity-Sufficiency Interweaving Strategy (ASIS) described in Kastner & Frühwirth-Schnatter (2014), using the Gaus- sian stochastic volatility model and the panel of 23 daily exchange rates from that paper. For computing the posterior mean of the volatility persistence parameter, our numerical efficiency is 6-27 times higher; for the volatility of volatility parameter, 18-53 times higher. We analyse trans- action counts in a second example using dynamic Poisson and Gamma-Poisson models. Despite non-Gaussianity of the count data, we obtain high numerical efficiency that is not much lower than that reported in McCausland (2012) for a sampler that involves pre-computing the shape of a static posterior distribution of parameters. In Chapter 2, we propose a new stochastic conditional duration model (SCD) for the analysis of high-frequency financial transaction data. We identify undesirable features of existing parametric conditional duration densities and propose a new family of flexible conditional densities capable of matching a wide variety of distributions with moderately varying hazard functions. Guided by theoretical consideration from queuing theory, we introduce nonparametric deviations around a central exponential distribution, which we argue is a sound first-order model for financial durations, using a Bernstein density. The resulting density is not only flexible, in the sense that it can approximate any continuous density on [0,∞) arbitrarily closely, provided it consists of a large enough number of terms, but also amenable to shrinkage towards the exponential distribution. Thank to highly efficiency draws of state variables, numerical efficiency of our posterior simulation compares very favourably with those obtained in previous studies. We illustrate our methods using quotation data on equities traded on the Toronto Stock Exchange. We find that models with our proposed conditional density having less than four terms provide the best fit. The smooth variation found in the hazard functions, together with the possibility of it being non-monotonic, would have been impossible to capture using commonly used parametric specification. In Chapter 3, we introduce a new stochastic duration model for transaction times in asset markets. We argue that widely accepted rules for aggregating seemingly related trades mislead inference pertaining to durations between unrelated trades: while any two trades executed in the same second are probably related, it is extremely unlikely that all such pairs of trades are, in a typical sample. By placing uncertainty about which trades are related within our model, we improve inference for the distribution of duration between unrelated trades, especially near zero. We propose a discrete model for censored transaction times allowing for zero-inflation resulting from clusters of related trades. The discrete distribution of durations between unrelated trades arises from a flexible density amenable to shrinkage towards an exponential distribution. In an empirical example, we find that the underlying conditional hazard function for (uncensored) durations between unrelated trades varies much less than what most studies find; a discrete distribution for unrelated trades based on an exponential distribution provides a better fit for all three series analyzed. We claim that this is because we avoid statistical artifacts that arise from deterministic trade-aggregation rules and unsuitable parametric distribution.