No description available.

No description available.

No description available.

The aim of this thesis was to examine environmental covariate-related issues, the resolution dependency, the contribution of vegetation covariates, and the use of LiDAR data, in the purposive sampling design for fuzzy logic-based digital soil mapping. In this design fuzzy c-means (FCM) clustering of environmental covariates was employed to determine proper sampling sites and assist soil survey and inference. Two subsets of the Laurel Creek Conservation area were examined for the purposes of exploring the resolution and vegetation issues, respectively. Both conventional and LiDAR-derived digital elevation models (DEMs) were used to derive terrain covariates, and a vegetation index calculated from remotely sensed data was employed as a vegetation covariate. A basic field survey was conducted in the study area. A validation experiment was performed in another area. The results show that the choices of optimal numbers of clusters shift with resolution aggregated, which leads to the variations in the optimal partition of environmental covariates space and the purposive sampling design. Combining vegetation covariates with terrain covariates produces different results from the use of only terrain covariates. The level of resolution dependency and the influence of adding vegetation covariates vary with DEM source. This study suggests that DEM resolution, vegetation, and DEM source bear significance to the purposive sampling design for fuzzy logic-based digital soil mapping. The interpretation of fuzzy membership values at sampled sites also indicates the associations between fuzzy clusters and soil series, which lends promise to the applicability of fuzzy logic-based digital soil mapping in areas where fieldwork and data are limited.

The aim of this thesis was to examine environmental covariate-related issues, the resolution dependency, the contribution of vegetation covariates, and the use of LiDAR data, in the purposive sampling design for fuzzy logic-based digital soil mapping. In this design fuzzy c-means (FCM) clustering of environmental covariates was employed to determine proper sampling sites and assist soil survey and inference. Two subsets of the Laurel Creek Conservation area were examined for the purposes of exploring the resolution and vegetation issues, respectively. Both conventional and LiDAR-derived digital elevation models (DEMs) were used to derive terrain covariates, and a vegetation index calculated from remotely sensed data was employed as a vegetation covariate. A basic field survey was conducted in the study area. A validation experiment was performed in another area. The results show that the choices of optimal numbers of clusters shift with resolution aggregated, which leads to the variations in the optimal partition of environmental covariates space and the purposive sampling design. Combining vegetation covariates with terrain covariates produces different results from the use of only terrain covariates. The level of resolution dependency and the influence of adding vegetation covariates vary with DEM source. This study suggests that DEM resolution, vegetation, and DEM source bear significance to the purposive sampling design for fuzzy logic-based digital soil mapping. The interpretation of fuzzy membership values at sampled sites also indicates the associations between fuzzy clusters and soil series, which lends promise to the applicability of fuzzy logic-based digital soil mapping in areas where fieldwork and data are limited.

No description available.

Die Dissertation befasste sich mit der Entwicklung einer neuen diagnostischen Methode für in-situ-Gewebeuntersuchungen. Der Ansatzpunkt war die Untersuchung von pathologischen Veränderungen im Gewebe, die sich biochemisch in den Zellen widerspiegeln und deshalb mit schwingungsspektroskopischen Methoden, wie der IR-Spektroskopie, erfassbar sind. Das Ziel der Arbeit war die IR-spektroskopische Charakterisierung und Klassifizierung von benignen, präkanzerösen und malignen Geweben mittels chemometrischer Algorithmen auf der Basis multivariater Informationen der IR-Spektren. Um komplexe spektrale Veränderungen zu charakterisieren und die Ergebnisse statistisch abzusichern, ist für jeden Gewebetyp eine Vielzahl an Spektren erforderlich. Daher wurde zur Spektrenakkumulation das IR-mikrospektroskopische Imaging mittels Focal Plane Array Detektor (FPA) genutzt. Die Herausforderung liegt in der Datenanalyse. Der große Datenumfang macht die Anwendung multivariater Algorithmen notwendig. Angewendet wurden Clusteralgorithmen zur Spektrendifferenzierung und die SIMCA (Soft Independent Modelling of Class Analogies) zur Spektrenklassifizierung. Die Validierung der Ergebnisse erfolgt über die histologische Untersuchung der nach der spektroskopischen Messung gefärbten Gewebedünnschnitte. Die genaue Vorgehensweise bei der Auswertung wird in dieser Arbeit anhand humaner Gewebeproben dargestellt. Die untersuchten Plattenepithelkarzinome und Adenokarzinome gehören zu den epithelialen Tumoren, die oralen bzw. zervikalen Ursprungs sind. Die Übertragbarkeit der spektralen Modelle wurde mit Gewebeproben mehrerer Patienten innerhalb einer und zwischen verschiedenen Tumorarten untersucht. Das ist ein erster Schritt zum Einsatz spektroskopischer Methoden in der medizinischen Forschung und Diagnostik.

The transition towards a sustainable and low-carbon future requires significant changes in energy behaviour among energy consumers. However, the question remains about how, by whom, and what changes are necessary to achieve this transition. While many policies have been implemented to overcome these barriers, current policies focus on technology market development and market failures. Policies that address behavioural anomalies are limited, leading to a lack of engagement and awareness among many energy consumers. This issue is particularly prominent among hard-to-reach (HTR) consumer groups, which can be found in both the commercial and residential sectors (Rotmann, Sea, 2019). The impact of HTR energy consumers on energy efficiency measures and the achievement of climate neutrality remains largely unexplored. It is necessary to identify and understand these groups to address this gap. By doing so, it is possible to help them change their energy consumption behaviour towards a more sustainable and low-carbon future (Rotmann, Sea, 2019).This project aims to provide insights into HTR energy consumers in Sweden and assess their impact on achieving climate neutrality. The primary objective is to identify the most common HTR groups, their main barriers, and the potential drivers that could help change their energy consumption behaviour. A detailed literature review is conducted to gather information on practical and theoretical cases globally to achieve these objectives. After the literature review, a semi-quantitative data analysis has identified and classified HTR energy user in Sweden, by using modelling software like Mental Modeler. A qualitative analysis was carried out to analyze the HTR groups, identify their main barriers and potential drivers, and to understand their energy behaviour better. From the literature review, it was found that high income, low income, and elderly are hard to reach, and there is a research gap of their behavior, needs and how to address their challenges to energy consumption. The findings revealed that high energy prices, subsidies, economic conditions, and energy production were key driver components that could not be controlled by individual energy consumers. Therefore, policymakers must implement policies that address these root causes, such as investing in renewable energy sources, increasing competition in the energy market, and offering targeted subsidies for energy-efficient products and services. Additionally, policies that promote economic growth and stability can help to improve energy efficiency by reducing the financial strain on households. Overall, this study highlights the need for comprehensive energy efficiency policies that target both the demand and supply side of energy consumption to promote sustainable and affordable energy use in households, which is crucial for achieving long-term energy and climate goals. / Övergången till en hållbar framtid med låga koldioxidutsläpp kräver betydande förändringar i energibeteendet bland energikonsumenter. Frågan kvarstår dock om hur, av vem och vilka förändringar som krävs för att uppnå denna övergång. Även om ett stort antal policyer har implementerats för att övervinna dessa barriärer, fokuserar nuvarande policyer främst på teknikmarknadsutveckling och marknadsmisslyckanden. Policyer som tar itu med beteendeavvikelser är begränsade, vilket leder till bristande engagemang och medvetenhet bland många energikonsumenter. Denna fråga är särskilt framträdande bland svåråtkomliga konsumentgrupper, som finns i både kommersiella och bostadssektorer (Rotmann, Sea, 2019). De svåråtkomliga energikonsumenters inverkan på energieffektivitetsåtgärder och uppnåendet av klimatneutralitet är fortfarande i stort sett outforskad. För att komma till rätta med denna klyfta är det nödvändigt att identifiera och förstå dessa grupper bättre. Genom att göra det kommer det att vara möjligt att hjälpa dem att ändra sitt energikonsumtionsbeteende mot en mer hållbar framtid med låga koldioxidutsläpp (Rotmann, Sea, 2019). Detta projekt syftar till att ge insikter om HTR energikonsumenter i Sverige och bedöma deras inverkan på att uppnå klimatneutralitet. Det primära målet är att identifiera de vanligaste HTR-grupperna, de huvudsakliga hindren de möter och de potentiella drivkrafterna som kan bidra till att förändra deras energiförbrukningsbeteende.En noggrann litteraturöversikt genomförs för att samla information om praktiska och teoretiska fall globalt för att uppnå dessa mål. Efter litteraturöversikten har en semi-kvantitativ dataanalys identifierat och klassificerat HTR-energianvändare i Sverige genom att använda modelleringsprogramvara som Mental Modeler. En kvalitativ analys utfördes för att analysera HTR-grupperna, identifiera deras huvudsakliga hinder och potentiella drivkrafter och för att bättre förstå deras energibeteende. Från litteraturgenomgången framgick det att höginkomsttagare, låginkomsttagare och äldre är svåra att nå, och att det finns en forskningslucka vad gäller deras beteende, behov och hur man kan hantera deras utmaningar i energikonsumtionen. Resultaten visade att höga energipriser, subventioner, ekonomiska förhållanden och energiproduktion var viktiga drivkomponenter som inte kunde kontrolleras av enskilda energikonsumenter. Därför måste beslutsfattare genomföra politiska åtgärder som adresserar dessa grundorsaker, såsom investeringar i förnybara energikällor, ökad konkurrens på energimarknaden och erbjudande om riktade subventioner för energieffektiva produkter och tjänster. Dessutom kan politiska åtgärder som främjar ekonomisk tillväxt och stabilitet hjälpa till att förbättra energieffektiviteten genom att minska den ekonomiska belastningen på hushållen. Sammanfattningsvis betonar denna studiebehovet av omfattande energieffektivitetspolitik som riktar sig mot både efterfråge- och tillförselssidan av energiförbrukningen för att främja hållbar och ekonomiskt överkomlig energianvändning i hushållen, vilket är avgörande för att uppnå långsiktiga energi- och klimatmål.

Harmonic distortion in a power system can excite non-characteristic harmonics from converter interfaced loads and generators which can then propagate back into the system, exciting other harmonics in the system. In this thesis, a harmonic analysis tool is presented that is designed to perform high accuracy, computationally efficient, steady-state harmonic analysis of distribution systems when multiple converter interfaced loads and generators exist in the system. The harmonic analysis tool requires less detail and engineering time than PSCAD/EMTDC while offering reliable assessment of harmonic coupling problems that are not captured by existing frequency-domain harmonic analysis tools. To do this, 5 harmonic models of common power electronic converter topologies were developed and implemented into this tool. The harmonic models are shown to be highly accurate; when tested in an unbalanced system with even and odd harmonic distortion, the harmonic models showed a maximum error of less than 0.4% when compared to PSCAD/EMTDC.

Harmonic distortion in a power system can excite non-characteristic harmonics from converter interfaced loads and generators which can then propagate back into the system, exciting other harmonics in the system. In this thesis, a harmonic analysis tool is presented that is designed to perform high accuracy, computationally efficient, steady-state harmonic analysis of distribution systems when multiple converter interfaced loads and generators exist in the system. The harmonic analysis tool requires less detail and engineering time than PSCAD/EMTDC while offering reliable assessment of harmonic coupling problems that are not captured by existing frequency-domain harmonic analysis tools. To do this, 5 harmonic models of common power electronic converter topologies were developed and implemented into this tool. The harmonic models are shown to be highly accurate; when tested in an unbalanced system with even and odd harmonic distortion, the harmonic models showed a maximum error of less than 0.4% when compared to PSCAD/EMTDC.