Floods is a serious concern across Europe due to the enormous material damage and death toll. Of alltypes of floods, flash floods and large-scale river floods have become major natural hydrological hazardsin most countries. The city of Halmstad was chosen due to its placement on the southern west coast ofSweden, a region for which climate projections have indicated more precipitation and potential forflooding. In recent years a number of floods have also been observed with associated damages. Usingextreme value analysis on observed data these events can be interpreted in terms of return level valuesand their frequency of occurrence. The seasonal variation of the precipitation and discharge of thecatchment were analyzed based on 43-year precipitation and 25-year discharge observation data and therelationship to NAO index was investigated to give a preliminary overview of the hydrologicalconditions in Halmstad and its causes. The results showed that Halmstad was seasonally characterizedby high discharge in winter and lower discharge in summer with the highest rainfall. The effect of stormtracks represented by the NAO index on the precipitation and discharge in winter months was evident.This study focused on the analysis of extreme data of precipitation and discharge. The return levels forup to 50-year return period were estimated by GEV fitting. The estimated return level of discharge fora 50-year return period is 250 m³/s, and the return levels of precipitation for a 50-year flood was foundto be 68 mm/day. Two cases were selected from a compiled annual maxima discharge data set foranalyzing and comparing their weather conditions based on ERA5 data. The results showed that differentweather conditions do have an impact on the total rainfall, and there were similar patterns but largedifferences between ERA5 reanalysis data and observed SMHI data was also shown emphasizing theneed for long-term observational data sets and further evaluation of reanalysis data.

[pt] Um dos fatos estilizados mais pronunciados acerca das distribuições de retornos financeiros diz respeito à presença de caudas pesadas. Isso torna os modelos paramétricos tradicionais de cálculo de Valor em Risco (VaR) inadequados para a estimulação de VaR de baixas probabilidades (1% ou menos), dado que estes se baseiam na hipótese de normalidade para as distribuições dos retornos. Tais modelos não são capazes de inferir sobre as reais possibilidades de ocorrência retornos atípicos. Sendo assim, o objetivo de presente trabalho é investigar o desempenho de modelos baseados na Teoria dos Valores Extremos para cálculos de VaR, comparando-os com modelos tradicionais. Um modelo incondicional, proposto a caracterizar o comportamento de longo prazo da série, e um modelo condicional, sugerido por McNeil e Frey (1999), proposto a caracterizar a dependência presente na variância condicional dos retornos foram utilizados e testados em quatro séries de retornos de ações representativas do mercado brasileiro: retornos de Ibovespa, retornos de Ibovespa Futuro, retornos das ações da Telesp e retornos das ações da Petrobrás. Os resultados indicam que os modelos baseados na Teoria dos Valores Extremos são mais adequados para a modelagem das caudas, e conseqüente para a estimulação de Valor em Risco quando os níveis de probabilidade de interesse são baixos. Além disso, modelo condicional é mais adequado em épocas de crise, pois, ao contrário do modelo incondicional, tem a capacidade de responder rapidamente a mudanças na volatilidade. Medidas de risco, como a perda média e a perda mediana também foram propostas, a fim de fornecer estimativas para as perdas no caso do VaR ser violado. / [en] The existence of fat tails is one of the striking stylized facts of financial returns distribution. This fact makes the use of traditional parametric models for Value at Risk (VaR) stimulation unsuitable for the estimation of low probability events (1% or less). This is because traditional models are based on the conditional normality assumption for financial returns distributions, making them unsuitable to predict the actual probabilities of occurrence of atypical returns. The main purpose of this dissertation is to investigate the performance of VaR models based on extreme Value Theory (EVT), and to compare them to some traditional models. Two classes of models are investigated. The first class is based in an unconditional model, which characterizes the long-term behavior of the time series of returns. The other class of models is a conditional one, which incorporates the short-term behavior of the return series, characterized by the strong dependency observed on the conditional variance of the returns. Both models were applied to four representative time series of the Brazilian stock market: The Bovespa Index, Future of Bovespa Index, Telesp stocks and Petrobrás stocks. The results indicates that EVT based models are suitable for low probability VaR stimulation estimation. Besides that, its possible to conclude that the conditional model is more appropriate for crisis periods, because of its capacity to quickly respond to volatily changes. Alternative risk measures are also used, to give estimates losses magnitudes in the case of VaR violation.

In applications spanning across aerospace, marine, automotive, energy, and space travel domains, composite materials have become ubiquitous because of their superior stiffness-to-weight ratios as well as corrosion and fatigue resistance. However, from a manufacturing perspective, these advanced materials have introduced new challenges that demand the development of new tools. Due to the complex anisotropic and nonlinear material properties, composite materials are more difficult to model than conventional materials such as metals and plastics. Furthermore, there exist ultra-high precision requirements in safety critical applications that are yet to be reliably met in production. Towards developing new tools addressing these challenges, this dissertation aims to (i) build high-fidelity numerical simulations of composite assembly processes, (ii) bridge these simulations to machine learning tools, and (iii) apply data-driven solutions to process control problems while identifying and overcoming their shortcomings. This is accomplished in case studies that model the fixturing, shape control, and fastening of composite fuselage components. Therein, simulation environments are created that interact with novel implementations of modified proximal policy optimization, based on a newly developed reinforcement learning algorithm. The resulting reinforcement learning agents are able to successfully address the underlying optimization problems that underpin the process and quality requirements. / Doctor of Philosophy / Within the manufacturing domain, there has been a concerted effort to transition towards Industry 4.0. To a large degree, this term refers Klaus Schwab's vision presented at the World Economic Forum in 2015, in which he outlined fundamental systemic changes that would incorporate ubiquitous computing, artificial intelligence (AI), big data, and the internet-of-things (IoT) into all aspects of productive activities within the economy. Schwab argues that rapid change will be driven by fusing these new technologies in existing and emerging applications. However, this process has only just begun and there still exist many challenges to realize the promise of Industry 4.0. One such challenge is to create computer models that are not only useful during early design stages of a product, but that are connected to its manufacturing processes, thereby guiding and informing decisions in real-time. This dissertation explores such scenarios in the context of composite structure assembly in aerospace manufacturing. It aims to link computer simulations that characterize the assembly of product components with their physical counterparts, and provides data-driven solutions to control problems that cannot typically be solved without tedious trial-and-error approaches or expert knowledge.

Insurance companies are grappling with the rising frequency and severity of extreme weather-related flooding events, which currently pose the highest financial burden both in total and per individual case. The existing insurance model isn't economically sustainable if such events continue to increase. To assess future needs and challenges in flooding insurances, research on changing weather patterns and interviews with employees at major firms were conducted. The research indicates that climate change has and will further worsen extreme rain events in Sweden, leading to more frequent and intense flooding events. Interviews revealed that firms are aware of impending changes in the insurance industry due to climate change but lack proactive measures to address them. Responsibility is fragmented, and communication between stakeholders is suboptimal. To address these challenges, I look at recent research regarding flood risk assessment and if these are applicable for the insurance industry in Sweden to ensure future profitability.

京都大学 / 新制・課程博士 / 博士(地球環境学) / 甲第23556号 / 地環博第213号 / 新制||地環||41(附属図書館) / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)教授 星野 敏, 准教授 鬼塚 健一郎, 教授 西前 出 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM

Plantation and managed forests are major sink of atmospheric CO2 in North America and across the world. If properly managed, these forests may help to offset anthropogenic greenhouse gas emissions to mitigate climate change. This study investigated the impacts of climate variability, extreme weather events, and disturbance (thinning) on the growth and carbon (C) exchanges of a young temperate coniferous plantation forest (48-year-old white pine (Pinus strobus)) in the Great Lakes region in Canada using long-term eddy covariance flux observations. CO2 fluxes, as well as meteorological and soil variables were continuously measured from 2008 to 2021 (14 years) to estimate net ecosystem productivity (NEP), ecosystem respiration (RE), and gross ecosystem productivity (GEP). Soil respiration (Rs) was also measured using automatic soil chambers from 2017 to 2019. Selective thinning was conducted first time in this stand in January 2021 to remove approximately 1/3 of the basal area. Study results showed that climate conditions in the early growing season, from late May to mid-July, determined the overall strength of C uptake in any given year. However, above-average temperature and precipitation in the late growing season significantly reduced NEP and even in some cases, transformed the forest into a net C source for short periods due to large pulses of RE. Mean annual GEP, RE and NEP values were 1660 ±199, 1087 ±96 and 592 ±169 g C m-2 yr-1, respectively, from 2008 to 2021. Thinning did not significantly impact the C uptake of the forest as the stand remained a net C sink with an annual NEP of 648 g C m-2 yr-1 in 2021. Changes in annual GEP, RE and NEP in 2021 remained within the range of interannual variability over the study period. Overall, Rs accounted for roughly 89% of the annual RE in this stand. A complete understanding of the response of forest C dynamics to climate variability and thinning in young plantation forests is critical to guiding future forest management efforts for enhancing the growth and C uptake of these forest plantations to maximize their potential in support of providing nature-based climate solutions. / Thesis / Master of Science (MSc)

Block copolymer (BCP) nanocomposites are an important class of hybrid materials in which the BCP guides the spatial location and the periodic assembly of the additives. High loadings of well-dispersed nanofillers are generally important for many applications including mechanical reinforcing of polymers. In particular the composites shown in this work might find use as etch masks in nanolithography, or for enabling various phase selective reactions for new materials development. This work explores the use of hydrogen bonding interactions between various additives (such as homopolymers and non-polymeric additives) and small, disordered BCPs to cause the formation of well-ordered morphologies with small domains. A detailed study of the organization of homopolymer chains and the evolution of structure during the process of ordering is performed. The results demonstrate that by tuning the selective interaction of the additive with the incorporating phase of the BCP, composites with significantly high loadings of additives can be formed while maintaining order in the BCP morphology. The possibility of high and selective loading of additives in one of the phases of the ordered BCP composite opens new avenues due to high degree of functionalization and the proximity of the additives within the incorporating phase. This aspect is utilized in one case for the formation of a network structure between adjoining additive cores to derive mesoporous inorganic materials with their structures templated by the BCP. The concept of additive-driven assembly is extended to formulate BCP-additive blends with an ability to undergo photo-induced ordering. Underlying this strategy is the ability to transition a weakly interacting additive to its strongly interacting form. This strategy provides an on-demand, non-intrusive route for formation of well-ordered nanostructures in arbitrarily defined regions of an otherwise disordered material. The second area explored in this dissertation deals with the incorporation of additives into photoresists for next generation extreme ultra violet (EUV) photolithography applications. The concept of hydrogen bonding between the additives and the polymeric photoresist was utilized to cause formation of a physical network that is expected to slow down the diffusion of photoacid leading to better photolithographic performance (25-30 nm resolution obtained).

Introduction: High levels of bilirubin in newborns can cause permanent neurodevelopmental disabilities, and it is crucial to keep the incidence low. However, the Swedish Neonatal Register revealed a high incidence of extreme neonatal hyperbilirubinemia (bilirubin ≥425 umol/L) in Region Örebro County during 2014-2019, and the reason behind this is unknown. Aim: This study aimed to review cases of extreme neonatal hyperbilirubinemia regarding the compliance to the local guidelines, and to explore potential benefits of an alternative method considering bilirubin’s rate of rise, the ruler method. Method: In this case series, a retrospective medical record review was performed on 63 newborns who were delivered at ≥35 gestational weeks and developed extreme hyperbilirubinemia before or during an admission to a hospital in Region Örebro County within the first 14 days of life (2014-2020). Results: The incidence was 2.7 cases per 1000 live births during 2014-2020. Forty-three (68.3%) cases were related to failed detection/treatment initiation and 20 (31.7%) to failed treatment. Out of the newborns classified as failed detection/treatment initiation, 27 individual newborns (62.8%) could potentially have been prevented from developing extreme hyperbilirubinemia if there were no cases of non-compliance (30.2%), if a pre-discharge screening had been performed (14.0%) and if the ruler method had been applied (19/31 investigated). Conclusion: The local guidelines used in Region Örebro County might not be sufficient in preventing the development of extreme neonatal hyperbilirubinemia. However, mandatory pre-discharge screening and a consideration of bilirubin’s current rate of rise when scheduling follow-ups could potentially lower the incidence further.

The increasing length of the daily commute is a major issue for many commuters in the Greater Toronto and Hamilton Area (GTHA). In order to alleviate this problem through policy, the policy makers require more in-depth understanding of this issue. This study explores different travel behaviour, socioeconomic and labour market determinants of commuting distance for resident workers in the GTHA, especially those having normal commutes and those having extreme commutes. This study also explores which areas of the GTHA are most self-contained, and what are the average commuting distances of each sub-region of the GTHA. The primary data source for this study was Transportation Tomorrow Survey (TTS) for the year 2011. Supplementary data were obtained from InfoCanada and Statistics Canada. Descriptive analysis in this study, focused at the Census Sub-Division (CSD), examined self-containment, outbound commutes, inbound commutes, resident employees and jobs densities, and average commute distances for place of residence and place of work. Study results showed that Toronto and Hamilton CSDs are the most self-contained areas in the GTHA, whereas areas located in the north and northwest of Toronto are major sources of outgoing commutes. Toronto and its adjacent CSDs have the lowest average commuting distance, whereas residents of Georgina and Brock commute exceptionally long distances. Multivariate regression analyses were applied to a disaggregate dataset (TTS). Workers older than 15 years of age living in the GTHA were divided into two major categories based on the length of their commute: (i) normal commuters (those having a mean commuting distance of 10.8 km) and (ii) extreme commuters (those having a mean commuting distance of 40.9 km). Factors affecting commuting distance for these two groups were examined. Similarly, residents living and working in the GTHA were divided into two categories: Resident workers living in (i) Jobs-rich areas or (ii) Resident-rich areas. Factors affecting commuting distance of these resident workers were also examined. The key controlling factors of commuting distance include gender, age, mode of transportation, employment status, ratio of jobs to employed residents, age of youngest child, auto availability in household, multi-worker household, median income, jobs and population density, and distance from CBD. Significant socioeconomic, travel behaviour and land use determinants for normal commute distances were also applicable to extreme commute distance. Transit was the preferred mode of transportation for long distance commuters in the GTHA, except for those living in job-rich areas. Workers associated with Sales and Service occupation and living in jobs-rich areas exhibited shorter commute than those in General/Clerical occupation. These findings are important to understand the changing travel patterns and behaviours of commuters in the GTHA. These results will be of interest to transportation planners, engineers, and policy makers as it highlights the inclination of long distance commuters to use transit. / Thesis / Master of Science (MSc)

Plants and wildlife are being affected by climate change and human activities. We need to understand the patterns in these impacts to develop management strategies and policy solutions that will help us conserve ecosystems. Climate change is shifting the timing of key life stages in plants, but we do not fully understand the extent and implications of phenological shifts – or changes in the timing of seasonal events – for understudied stages like fruiting or for potential mismatches between plants in different canopy levels. Human activities and climate change impact and harm wildlife in many ways, from wildlife-vehicle collisions and lead poisoning to hurricanes and infectious diseases, but it has been difficult to form a comprehensive picture of these threats across many species and regions, and to discern which factors pose the greatest threat to at-risk species. Here, I collected and curated data from herbarium specimens and wildlife rehabilitation records to advance our understanding of the effects of climate change and human activities on plants and wildlife in North America. First, I found that metrics of first, peak, and last fruiting dates were strongly correlated between two historical datasets, suggesting that field observations and herbarium collections capture similar orders of fruiting times among plant species in New England. However, I found differences in the exact timing of first and last fruiting dates, indicating that researchers should match methodology when selecting historical records of phenology for present-day comparisons, especially when the exact timing is important. Next, I found that native trees, native shrubs, and non-native shrubs advanced their leaf-out or flowering times faster than native wildflowers advanced their flowering times with warming temperatures. As climate warming progresses, some native wildflower species, especially in warmer regions, are likely to be affected by phenological mismatch and lose access to early-season sunlight. Last, I found that human disturbances accounted for the largest proportion of wildlife injury and sickness in animals admitted to wildlife rehabilitation centers, and I identified the predominant reason for admittance for many species; these reasons included vehicle collisions, fishing incidents, and window or building collisions. I recommended possible interventions to help conserve wildlife, including using or changing wildlife road crossings, fishing and hunting regulations, lead and pesticide regulations, and disaster management plans. In this research, I compiled and analyzed innovative, newly-digitized data sources to provide new insights into the effects of climate change and human activities on plants and wildlife in North America. / 2024-09-18T00:00:00Z