Water use and management in an arid region: Fort Collins, Colorado, and vicinity

Submitted to the Water Resources Planning Fellowship Steering Committee, Colorado State University, in fulfillment of requirements for AE 795, Special study in planning.Bibliography: page 105

Temporal reference and intergenerational timescales of agricultural conservation under variable climate

Agriculture is a complex human-natural system with intricate and continuous feedback loops that bring the past forward into the present and the future. Like all humans, farmers learn from the past. Intergenerational narratives and experiences with recent past extreme weather events and variable climate patterns frequently become analog years used as benchmarks to build knowledge of the natural environment and guide decisions. However, there is a lack of knowledge about how individuals plan and structure the timescales between decision, action, and outcome. For example, why do some people seem to act on “shorter” timescales without regard for long-term consequences of their actions to themselves, others, or the environment? And why do others make decisions based on “longer” timescales in order to preserve resources for the sake of future use? Although agricultural and climate sciences are continuously advancing our understanding of crop management, fewer investments have been made to understand the crucial human element. There is a need to better understand the timescales of social and cultural factors which influence reception (or rejection) of advances in scientific knowledge. How do time perspectives—the orientation to time and pathways of time—influence interpretation of information and decisions made to implement conservation practices on agricultural lands? What are the disjunctures between how humans perceive and reference long-term timescales of changing climatic conditions and short-term timescales of annual crop production? This dissertation seeks to expand understanding of farmer decision-making as it relates to timescales, climate change, corn-based cropping systems, and advances in science for agricultural decision support. First a temporal reference framework is developed to explain the processes by which past experiences and intergenerational narratives are brought forward in time to inform current agricultural management decisions. Then, this theory is elaborated and empirically tested in Chapters 4 and 5. A purposeful sample of interviews with corn farmers (N=159) and climatologists (N=22) in nine upper Midwest states (Wisconsin, Indiana, Iowa, Minnesota, Michigan, Ohio, Illinois, South Dakota, and Illinois) and a random sample farmer survey (N=1,159) from the 2015 Iowa Farm and Rural Life Poll provide data for these analyses. Chapter 4 conducts binary logistic regression on the survey of Iowa farmers to evaluate the influence of previous generations and social pressures on decisions to decrease fall tillage, increase no tillage, and increase the use of cover crops on their farm. Family-level norms and pressures are shown to reinforce traditional crop production practices such as the action of post-harvest soil tillage. Chapter 5 explores the weight that farmers and climatologists give to historical experiences in interpreting climate conditions and their effects on production systems by analyzing in-person farmer interview data. Inductive reasoning is utilized to detect common themes involving temporal orientations and temporal pathways that influence agricultural decision-making. Findings suggest that farmers are influenced by historical intergenerational narratives of family farm management practices. Higher weights are often placed upon personally experienced past events and narratives of analogous historical conditions than predictions or expectations of future environmental conditions. Farmers are more likely to consider decisions relative to a past time orientation which reinforces pathways of time as socially-referenced to cyclical intergenerational events. This may result in farmers perceiving environmental conditions as maintaining stability through reoccurrence of environmental weather and climate risks. This suggests that scientific information describing early warning signals of future climate disruptions and opportunities for agricultural management adaption may not be resonating with the farming population. This research offers a contribution to further understand the role of timescales—temporal perspectives, orientations, and pathways—associated with decisions about agricultural production and climate. Implications of these findings may be helpful for scientists, educators, and other agricultural stakeholders who seek to connect advances in climate science with opportunities for agricultural adaptation. Recommendations involve building the capacity of information facilitators, or individuals skilled in communicating and framing science in messaging which resonates to intergenerational narratives of farm and soil conservation. Scientists should find ways to involve farmers in the co-production of knowledge to increase understanding of timescale perspectives in the interpretation of scientific knowledge. As agriculture adapts to changing climate and environmental conditions, decision-makers may need to continually assess and reconsider the trajectory of predominant corn-based cropping management

Interactive graph drawing with constraints

This thesis investigates the requirements for graph drawing stemming from practical applications, and presents both theoretical as well as practical results and approaches to handle them. Many approaches to compute graph layouts in various drawing styles exist, but the results are often not sufficient for use in practice. Drawing conventions, graphical notation standards, and user-defined requirements restrict the set of admissible drawings. These restrictions can be formalized as constraints for the layout computation. We investigate the requirements and give an overview and categorization of the corresponding constraints. Of main importance for the readability of a graph drawing is the number of edge crossings. In case the graph is planar it should be drawn without crossings, otherwise we should aim to use the minimum number of crossings possible. However, several types of constraints may impose restrictions on the way the graph can be embedded in the plane. These restrictions may have a strong impact on crossing minimization. For two types of such constraints we present specific solutions how to consider them in layout computation: We introduce the class of so-called embedding constraints, which restrict the order of the edges around a vertex. For embedding constraints we describe approaches for planarity testing, embedding, and edge insertion with the minimum number of crossings. These problems can be solved in linear time with our approaches. The second constraint type that we tackle are clusters. Clusters describe a hierarchical grouping of the graph's vertices that has to be reflected in the drawing. The complexity of the corresponding clustered planarity testing problem for clustered graphs is unknown so far. We describe a technique to compute a maximum clustered planar subgraph of a clustered graph. Our solution is based on an Integer Linear Program (ILP) formulation and includes also the first practical clustered planarity test for general clustered graphs. The resulting subgraph can be used within the first step of the planarization approach for clustered graphs. In addition, we describe how to improve the performance for pure clustered planarity testing by implying a branch-and-price approach. Large and complex graphs nowadays arise in many application domains. These graphs require interaction and navigation techniques to allow exploration of the underlying data. The corresponding concepts are presented and solutions for three practical applications are proposed: First, we describe Scaffold Hunter, a tool for the exploration of chemical space. We show how to use a hierarchical classification of molecules for the visual navigation in chemical space. The resulting visualization is embedded into an interactive environment that allows visual analysis of chemical compound databases. Finally, two interactive visualization approaches for two types of biological networks, protein-domain networks and residue interaction networks, are presented.In zahlreichen Anwendungsgebieten werden Informationen als Graphen modelliert und mithilfe dieser Graphen visualisiert. Eine übersichtliche Darstellung hilft bei der Analyse und unterstützt das Verständnis bei der Präsentation von Informationen mittels graph-basierter Diagramme. Neben allgemeinen ästhetischen Kriterien bestehen für eine solche Darstellung Anforderungen, die sich aus der Charakteristik der Daten, etablierten Darstellungskonventionen und der konkreten Fragestellung ergeben. Zusätzlich ist häufig eine individuelle Anpassung der Darstellung durch den Anwender gewünscht. Diese Anforderungen können mithilfe von Nebenbedingungen für die Berechnung eines Layouts formuliert werden. Trotz einer Vielzahl unterschiedlicher Anforderungen aus zahlreichen Anwendungsgebieten können die meisten Anforderungen über einige generische Nebenbedingungen formuliert werden. In dieser Arbeit untersuchen wir die Anforderungen aus der Praxis und beschreiben eine Zuordnung zu Nebenbedingungen für die Layoutberechnung. Wir geben eine Übersicht über den aktuellen Stand der Behandlung von Nebenbedingungen beim Zeichnen von Graphen und kategorisieren diese nach grundlegenden Eigenschaften. Von besonderer Wichtigkeit für die Qualität einer Darstellung ist die Anzahl der Kreuzungen. Planare Graphen sollten kreuzungsfrei gezeichnet werden, bei nicht-planaren Graphen sollte die minimale Anzahl Kreuzungen erreicht werden. Einige Nebenbedingungen beschränken jedoch die Möglichkeit, den Graph in die Ebene einzubetten. Dies kann starke Auswirkungen auf das Ergebnis der Kreuzungsminimierung haben. Zwei wichtige Typen solcher Nebenbedingungen werden in dieser Arbeit näher untersucht. Mit den Embedding Constraints führen wir eine Klasse von Nebenbedingungen ein, welche die mögliche Reihenfolge der Kanten um einen Knoten beschränken. Für diese Klasse präsentieren wir Linearzeitalgorithmen für das Testen der Planarität und das optimale Einfügen von Kanten unter Beachtung der Einbettungsbeschränkungen. Der zweite Typ von Nebenbedingungen sind Cluster, die eine hierarchische Gruppierung von Knoten vorgeben. Für das Testen der Cluster-Planarität unter solchen Nebenbedingungen ist die Komplexität bisher unbekannt. Wir beschreiben ein Verfahren, um einen maximalen Cluster-planaren Untergraphen zu berechnen. Wir nutzen dabei eine Formulierung als ganzzahliges lineares Programm sowie einen Branch-and-Cut Ansatz zur Lösung. Das Verfahren erlaubt auch die Bestimmung der Cluster-Planarität und stellt damit den ersten praktischen Ansatz zum Testen allgemeiner Clustergraphen dar. Zusätzlich beschreiben wir eine Verbesserung für den Fall, dass lediglich Cluster-Planarität getestet werden muss, der maximale Cluster-planare Untergraph aber nicht von Interesse ist. Für dieses Szenario geben wir eine vereinfachte Formulierung und präsentieren ein Lösungsverfahren, das auf einem Branch-and-Price Ansatz beruht. In der Praxis müssen häufig sehr große oder komplexe Graphen untersucht werden. Dazu werden entsprechende Interaktions- und Navigationsmethoden benötigt. Wir beschreiben die entsprechenden Konzepte und stellen Lösungen für drei Anwendungsbereiche vor: Zunächst beschreiben wir Scaffold Hunter, eine Software zur Navigation im chemischen Strukturraum. Scaffold Hunter benutzt eine hierarchische Klassifikation von Molekülen als Grundlage für die visuelle Navigation. Die Visualisierung ist eingebettet in eine interaktive Oberfläche die eine visuelle Analyse von chemischen Strukturdatenbanken erlaubt. Für zwei Typen von biologischen Netzwerken, Protein-Domänen Netzwerke und Residue-Interaktionsnetzwerke, stellen wir Ansätze für die interaktive Visualisierung dar. Die entsprechenden Layoutverfahren unterliegen einer Reihe von Nebenbedingungen für eine sinnvolle Darstellung

Evaluation of the new Design Summer Year weather data using parametrical buildings

The Charted Institution of Building Services Engineers (CIBSE) updated the near extreme weather (Design Summer Year – DSY) for all 14 locations in the UK in 2016. This new release attempts to address the underlying shortcomings of the previous definition where the averaged dry bulb temperature was the sole metric to choose DSY among source weather years. The aim of this research is to evaluate whether the new definition of the probabilistic DSYs can consistently represent near extreme condition. London historical weather data and their correspondent DSYs were used in this research. Dynamic thermal modelling using EnergyPlus was carried out on large number single zone offices (parametric study) which represent a large portion of cellular offices in the UK. The predicted indoor warmth from the sample building models show that these new definitions are not always able to represent near extreme conditions. Using multiple years as DSY is able to capture different types of summer warmth but how to use one or all of these DSYs to make informed judgement on overheating is rather challenging. The recommended practice from this research is to use more warm years for the evaluation of overheating and choose the near extreme weather from the predicted indoor warmt

Age composition and survival of public housing stock in Hong Kong

Emerging notably in more developed regions, building stock ageing which is characterised by shrinking new completions and falling “mortality” has been posing challenges to various stakeholders in built environment. To find way out of this transition, we need to know how long buildings will last these days and the factors leading to their “mortality”. By using data from 1950s till to date, a comprehensive investigation is conducted to analyse the age composition and life expectancy of public housing stock in Hong Kong. What comes after are survival analysis and empirical analysis of those demolished to identify the key factors leading to demolition. Presented in this paper are the preliminary findings as well as the research agenda on the theme to model age composition and survival of both private and public building stocks in Hong Kong and other similar cities in Asia Pacific Rim such as Adelaide and Singapore, together with research activities to formulate policies for sustainable urban management

Making Mitochondrial Haplogroup and DNA Sequence Predictions from Low-Density Genotyping data

The mitochondrial genome (mtDNA) is inherited differently and mutates more frequently than the genetic material residing in the cells’ nucleus. Whilst the genome of the mtDNA is small, at only 16.5 kilobases, it contains key components of the metabolic chain, and must communicate in a precise and timely way with the genes in the nuclear genome and sense the minute-to-minute needs of its host cell. MtDNA is an underexplored place to search for health-related variants. Unlike the time-consuming and expensive methods of whole genome sequencing, genotyping examines certain positions in the genome allowing imputation of the other variants typically linked to these positions. Current methods, which use nuclear genome data to model their predictions, do not tailor imputation to take advantage of the different inheritance patterns of the mtDNA. I present a novel method, using an open-source library of fully sequenced mtDNA samples with manually assigned haplogroups, to take genotyping data and predict the other variants present in the sample’s mtDNA sequence, a two-stage method referred to as in silico genotyping and barcode matching. The method has been assessed for performance on a test data set to explore inconsistencies across the mitochondrial genome and the human mtDNA phylogeny. The first use of in silico genotyping and barcode matching is presented; extending the use of UKBiobank’s data [22]. The UKBiobank represents data which is not only rich in detail but also covers a large population of individuals aged between 51 and 84 in 2021. The phenotypic data is health-focussed, including general health records, which is being augmented by new diagnoses or events in the participants’ medical history. Extensive use is being made of the data in UKBiobank with the exception of the mitochondrial DNA (mtDNA). The scale of the phenotypic data collected by the UKBiobank is proving a valuable resource, values all the more because of the difficulty and expense of its collection. Making further use of phenotyping by extending potential associations into the mtDNA is vital, and likely to offer substantial rewards. Using the method described below to transform genotypes into predicted mtDNA sequence opens the doors for mitochondrial variation to be put to considerable use too. The introduction presents evidence that: (a) the mitochondrion is essential for cell and organism function, (b) mtDNA can harbour variations associating with phenotypes, and (c) the current methods of mtDNA imputation can be improved upon. The method presented mimics any genotyping microarray to produce a library of data transformed to appear as if it had been genotyped by the physical array. The effectiveness and accuracy of this transformation have been investigated and the results are presented. Finally, the transformed library is used to predict the UKBiobank participant data to greatly extend a data set with huge reserves of potential especially for mitochondrial data. My development of in silico genotyping and barcode matching has allowed me to make weighted prediction for test samples, guessing their haplogroups and the variants they carry. Whilst I admit to the significant potential to improve algorithms, the overall accuracy of these predictions is at a level high enough to search for links between UKBiobank samples and their phenotypic data in a GWAS-style search

Smart Water Utilities

Today there is increasing pressure on the water infrastructure and although unsustainable water extraction and wastewater handling can continue for a while, at some point water needs to be managed in a way that is sustainable in the long-term. We need to handle water utilities “smarter”. New and effective tools and technologies are becoming available at an affordable cost and these technologies are steadily changing water infrastructure options. The quality and robustness of sensors are increasing rapidly and their reliability makes the automatic handling of critical processes viable. Online and real-time control means safer and more effective operation. The combination of better sensors and new water treatment technologies is a strong enabler for decentralised and diversified water treatment. Plants can be run with a minimum of personnel attendance. In the future, thousands of sensors in the water utility cycle will handle all the complexity in an effective way. Smart Water Utilities: Complexity Made Simple provides a framework for Smart Water Utilities based on an M-A-D (Measurement-Analysis-Decision). This enables the organisation and implementation of “Smart” in a water utility by providing an overview of supporting technologies and methods. The book presents an introduction to methods and tools, providing a perspective of what can and could be achieved. It provides a toolbox for all water challenges and is essential reading for the Water Utility Manager, Engineer and Director and for Consultants, Designers and Researchers

Smart Water Utilities

Today there is increasing pressure on the water infrastructure and although unsustainable water extraction and wastewater handling can continue for a while, at some point water needs to be managed in a way that is sustainable in the long-term. We need to handle water utilities “smarter”. New and effective tools and technologies are becoming available at an affordable cost and these technologies are steadily changing water infrastructure options. The quality and robustness of sensors are increasing rapidly and their reliability makes the automatic handling of critical processes viable. Online and real-time control means safer and more effective operation. The combination of better sensors and new water treatment technologies is a strong enabler for decentralised and diversified water treatment. Plants can be run with a minimum of personnel attendance. In the future, thousands of sensors in the water utility cycle will handle all the complexity in an effective way. Smart Water Utilities: Complexity Made Simple provides a framework for Smart Water Utilities based on an M-A-D (Measurement-Analysis-Decision). This enables the organisation and implementation of “Smart” in a water utility by providing an overview of supporting technologies and methods. The book presents an introduction to methods and tools, providing a perspective of what can and could be achieved. It provides a toolbox for all water challenges and is essential reading for the Water Utility Manager, Engineer and Director and for Consultants, Designers and Researchers