Although there have been oscillations in airship interest since their use in the early 1900s, technological advancements and the need for more flexible and environmentally friendly transportation modes have caused a stream of study and surge in airship development in recent years. For companies and governments to understand how airships can be incorporated into their fleets to fulfil new or existing mission types, system design space exploration is an important step in understanding airships, their uses, and their design parameters. A decision support system (DSS), Design Exploration of Lighter-Than-Air Systems (DELTAS), was developed to help stakeholders with this task. DELTAS allows users to design airships and missions to determine how a design will perform in the scenario. Simulations can also be run for a given mission to find the Pareto-optimal designs for user-defined ranges of high-level airship design parameters. A case study is provided that demonstrates how DELTAS can be used to explore the airship design space for three specified missions. These three mission case studies show how design of experiments is important to more thoroughly cover the design space and to find and understand the relationships between airship design variables that lead to optimal mission times and costs. This research also explores the impacts of introducing an airship into operation. Engineered products have economic, environmental, and social impacts, which comprise the major dimensions of sustainability. This paper seeks to determine the interaction between design parameters when social impacts are incorporated into the concept development phase of the systems design process. Social impact evaluation is increasing in importance similar to what has happened in recent years with environmental impact consideration in the design of engineered products. Concurrently, research into new airship design has increased. Airships have yet to be reintroduced at a large scale or for a range of applications in society. Although airships have the potential for positive environmental and economic impacts, the social impacts are still rarely considered. This paper presents a case study of the hypothetical introduction of airships in the Amazon region of Brazil to help local farmers transport their produce to market. It explores the design space in terms of both engineering parameters and social impacts using a discrete-event simulation to model the system. The social impacts are found to be dependent not only on the social factors and airship design parameters, but also on the farmer-airship system, suggesting that socio-technical systems design will benefit from integrated social impact metric analysis. This thesis seeks to demonstrate how computer-aided engineering tools can be used to predict social impacts, to more effectively explore a system's design space, and to optimize the system design for maximum positive impact, using the modern airship as a case study.

Varje dag behöver individer, grupper och organisationer fatta olika typer av beslut relaterade till affärsverksamhet- och privatlivsaktiviteter. Framgången för dessa beslut beror ofta på mängden och kvalitén på information som är tillgänglig för beslutsfattaren. Om det finns en stor mängd information, utöver begränsad tid till exempel under kriser, blir beslutsprocessen så komplex att det blir nödvändigt att använda datoriserade beslutsstödsverktyg. I denna studie föreslås, designas och implementeras en prototyp för gruppbaserat beslutsfattande för att stödja beslutet att hitta liknande forskarprofiler i krissituationer för forskare som vill samarbeta. Syftet med studien är att undersöka den potentiella påverkan på hur ett gruppbaserat beslutsstödssystem kan möjliggöra matchning av liknande forskarprofiler. Syftet är även att beskriva hur processen går till när en artefakt skapas och att förtydliga de olika delarna av denna innovativa process. Studien vill kunna utvärdera systemets potential genom att studera systemets funktionalitet som prototyp och fördjupa vår kunskap om hur användarna upplever systemet genom en kvalitativ analys. Ett experiment genomförs som använder den kvantitativa metod som gör det möjligt för författarna att undersöka syftet med denna studie. Studien avslöjade, som förväntat, att den största fördelen med ett gruppbaserat beslutsstödsystem är att minska den genomsnittliga tiden som krävs för att hitta en matchning mellan liknande forskarprofiler. Resultaten visar att det gruppbaserade beslutsstödsystem som tagits fram hade en positiv inverkan på möjligheterna till tidsbesparingar samt potentialen för relevans och hög kvalitet på matchning av profiler. Om en mer utvecklad algoritm hade utvecklats, skulle det resultera i fler möjligheter att matcha profiler, bland annat skulle forskarprofiler kunna matchas på underkategorier. / Each day individuals, groups and organizations need to make different types of decisions related to business activities and private life activities. The success of these decisions often depends on the amount and quality of information available to the decision maker. If there is a large amount of information, in addition to limited time, e.g., during crises, the decision-making process becomes so complex that it becomes necessary to make use of computerized decision support tools. In this study, we propose, design and implement a prototype for group-based decision making to support the decision of finding similar researcher profiles in crisis situations for researchers that wish to collaborate. The purpose of the study is to investigate the potential impact on how a group-based decision support system can enable matching of similar researcher profiles. The purpose is also to describe how the process goes when an artifact is created and to clarify the different parts of this innovative process. The authors want to be able to evaluate the potential of the system by studying the system's functionality as a prototype and deepening our knowledge of how users experience the system through a qualitative analysis. An experiment was designed which allows the authors to investigate the purpose of this study. The study uncovered, as we expected, that the main benefit of a group-based decision support system is reducing the average time required to find a match between similar researcher profiles. The results show that the group-based decision support system that was developed had a positive impact on the potential for time savings as well as the potential for relevance and high quality of matching profiles. If a more developed algorithm had been developed, it could result in more opportunities to match profiles, e.g., based on subcategories. This thesis will be written in Swedish.

With an abundant average precipitation rate, Vietnam could be considered water-reach country. Unfortunately, the non-uniform spatial and temporal distribution of rainfall, coupled with a demographic and industrial development polarized on the two major river deltas, it makes the water resources extremely vulnerable. As consequence, severe depletions of groundwater table are reported all over the country, often in the range of 1-2 m per year and more. The subsequent land subsidence is just one of the drawbacks, another being the increasing salinity of coastal aquifers as sea water level continues to rise. Under these conditions, the natural groundwater replenishment alone is not anymore able to provide for a safe water supply, different studies indicating that the groundwater exploitation in major urban agglomerations like Hanoi or Ho Chi Minh City already passed the sustainability level. The solution presented in this paper implies making use of engineered methods for enhancing the natural groundwater recharge rates by enabling better percolation rates of surface water into subsurface and thus optimizing the regional water cycle. The method known as ‘managed aquifer recharge’ (MAR) is introduced, together with general guidelines and tools for planning of MAR schemes, such as the newly web-based decision support system INOWAS_DSS. / Với tốc độ lượng mưa trung bình dồi dào, Việt Nam có thể được coi là quốc gia có nguồn nước trong tầm tay. Thật không may, sự phân bố không gian và thời gian không đồng đều của lượng mưa, cùng với sự phát triển dân số và công nghiệp phân cực trên hai vùng châu thổ sông lớn làm cho các nguồn nước rất dễ bị tổn thương. Vì vậy, sự suy giảm nước ngầm nghiêm trọng được báo cáo trên khắp đất nước, thường mỗi năm giảm 1-2 m và nhiều hơn nữa. Hiện tượng sụt lún đất xảy ra sau đó chỉ là một trong những hạn chế, mặt khác là độ mặn ngày càng tăng của các tầng chứa nước ven biển do mực nước biển tiếp tục tăng. Dưới những điều kiện này, việc bổ sung nước ngầm tự nhiên đơn thuần không còn có thể cung ứng cho một nguồn cấp nước sạch an toàn. Các nghiên cứu khác nhau cho thấy rằng việc khai thác nước ngầm tại các đô thị lớn như Hà Nội hay thành phố Hồ Chí Minh đã vượt qua mức độ bền vững. Giải pháp được trình bày trong bài báo này gợi ý việc sử dụng các phương pháp thiết kế để nâng cao tỷ lệ tái nạp nước ngầm tự nhiên bằng cách cho phép tỷ lệ thẩm thấu tốt hơn nước mặt vào dưới bề mặt và do đó tối ưu hóa chu trình nước trong khu vực. Phương pháp được gọi là 'tái nạp nước ngầm có quản lý (MAR) được giới thiệu, cùng với các hướng dẫn chung và các công cụ để lập kế hoạch đề án MAR, ví dụ như hệ thống mớihỗ trợ quyết định dựa trên kết nối mạng INOWAS_DSS.

No description available.

No description available.

Great Lakes Areas of Concern (AOC) are designated geographical locations within the Great Lakes Basin with particularly degraded environmental conditions. There is a consensus among diverse sectors in the Great Lakes Basin that contaminated sediment is a major environmental problem and a key factor in many of the impairments of the human and nonhuman uses (beneficial uses) of the Great Lakes. This case study examines Randle Reef in the Hamilton Harbour (AOC) which is the largest Canadian contaminated sediment site in the Great Lakes containing 695,000 m3 of sediment contaminated with polycyclic aromatic hydrocarbons (PAH) and metals. The cleanup of the Randle Reef site is a major step in the process to restore Hamilton Harbour and remove it from the list of AOCs. The Randle Reef sediment remediation project is finally coming to fruition after more than thirty years of study, discussion, collaborations, and debate. As in the case of Randle Reef, environmental decisions are often complex and multi-faceted and involve many stakeholders with competing (sometimes conflicting) priorities or objectives representing exactly the type of problem that humans are poorly equipped to solve unaided. When professionals encounter complex issues, they often attempt to use approaches that simplify the complexity so that they can manage the problem at hand. During this process, valuable information may be lost, opposite points of view may be ignored and elements of uncertainty may be overlooked. A systematic methodology that combines both quantitative and qualitative data from scientific or engineering studies of risk, cost, and benefit, as well as stakeholder objectives and values to rank project alternatives, has yet to be fully developed for contaminated sediment decision-making. The main goal of this Ph.D. research was to develop a Decision Support System (DSS) framework to aid the complex decision-making in sediment remediation. The proposed DSS framework incorporates the five key themes that, through research, were found to be the most relevant for sediment remediation projects. These themes are 1)participation of appropriate actors with common objectives; 2)funding and resources; 3)decision-making process; 4)research and technology development; and 5)public and political support. There was a need to gather relevant information and data from various sources to develop the required DSS framework. For this purpose, expert interviews were conducted, responses were collected through a public survey, Qualitative Document Analysis (QDA) was performed on available policy and research documents, and a review was undertaken of how other jurisdictions have employed DSS to aid their decision-making process. The final DSS framework has six key components as follows: 1)data module; 2)communication module; 3)document module; 4)knowledge module; 5)tools module; and 6)DSS optimization module. This generic framework can assist practitioners in developing more systematic and structured decisions for sediment remediation by incorporating an Integrated Information Management System (IIMS) along with a DSS optimization module. This IIMS+DSS method can aid the decision-making process by making it documented, reproducible, robust, transparent and provide a coherent framework to explore and analyze available alternatives in an attempt to reach the preferred solution promptly. / Thesis / Doctor of Philosophy (PhD)

The increase in diagnostic and therapeutic procedures in the treatment of oncological diseases, as well as the limited capacity of experts to provide information, necessitates the development of therapy decision support systems (TDSS). We have developed a treatment decision model that integrates available patient information as well as tumor characteristics. They are assessed according to their relevance in evaluating the optimal therapy option. Our treatment model is based on Bayesian networks (BN) which integrate patient-specific data with expert-based implemented causalities to suggest the optimal therapy option and therefore potentially support the decision-making process for treatment of laryngeal carcinoma. To test the reliability of our model, we compared the calculations of our model with the documented therapy from our data set, which contained information on 97 patients with laryngeal carcinoma. Information on 92 patients was used in our analyses and the model suggested the correct treatment in 419 out of 460 treatment modalities (accuracy of 91%). However, unequally distributed clinical data in the test sets revealed weak spots in the model that require revision for future utilization.

Carbon accreditation of forest development projects is essential for sequestering atmospheric CO2 under the provisions of the Kyoto Protocol. The carbon sequestration potential of surface coal-mined lands is not well known. The purpose of this work was to determine how to measure carbon sequestration and estimate the additional amount that could be sequestered using different reforestation methods compared to the common practice of establishing grasslands. I developed a thermal oxidation technique for differentiating sequestered soil carbon from inorganic and fossilized carbon found at high levels in mine soils along with a geospatial and statistical protocol for carbon monitoring and accounting. I used existing tree, litter, and soil carbon data for 14 mined and 8 adjacent, non-mined forests in the Midwestern and Eastern coal regions to determine, and model sequestered carbon across the spectrum of site index and stand age in pine, mixed, and hardwood forest stands. Finally, I developed the framework of a decision support system consisting of the first iteration of a dynamic model to predict carbon sequestration for a 60-year period for three forest types (white pine, hybrid poplar, and native hardwoods) at three levels of management intensity: low (weed control), medium (weed control and tillage) and high (weed control, tillage, and fertilization). On average, the highest amount of ecosystem carbon on mined land was sequestered by pine stands (148 Mg ha-1), followed by hardwood (130 Mg ha-1) and mixed stands (118 Mg ha-1). Non-mined hardwood stands contained 210 Mg C ha-1, which was about 62% higher than the average of all mined stands. After 60 years, the net carbon in ecosystem components, wood products, and landfills ranged from 20 to 235 Mg ha-1 among all scenarios. The highest net amount of carbon was estimated under mixed hardwood vegetation established by the highest intensity treatment. Under this scenario, a surface-mined land of average site quality would sequester net carbon stock at 235 Mg C ha-1, at a rate of 3.9 Mg C ha-1 yr-1, which was 100% greater than a grassland scenario. Reforestation is a logical choice for mined land reclamation if carbon sequestration is a management objective. / Ph. D.

The thesis aims to introduce an efficient resource development strategy in electric utility long term planning under uncertainty considerations. In recent years, electric utilities have recognized the concepts of robustness, flexibility, and risk exposure, to be considered in their resource development strategy. The concept of robustness means to develop resource plans that can perform well for most, if not all futures, while flexibility is to allow inexpensive changes to be made if the future conditions deviate from the base assumptions. A risk exposure concept is used to quantify the risk hazards in planning alternatives for different kinds of future conditions. This study focuses on two technical issues identified to be important to the process of efficient resource development: decision-making analysis considering robustness and flexibility, and decision-making analysis considering risk exposure. The technique combines probabilistic methods and tradeoff analysis, thereby producing a decision set analysis concept to determine robustness that includes flexibility measures. In addition, risk impact analysis is incorporated to identify the risk exposure in planning alternatives. Contributions of the work are summarized as follows. First, an efficient resource development framework for planning under uncertainty is developed that combines features of utility function, tradeoff analysis, and the analytical hierarchy process, incorporating a performance evaluation approach. Second, the multi-attribute risk-impact analysis method is investigated to handle the risk hazards exposed in power system resource planning. Third, the penetration levels of wind and photovoltaic generation technologies into the total generation system mix, with their constraints, are determined using the decision-making model. The results from two case studies show the benefits of the proposed framework by offering the decision makers various options for lower cost, lower emission, better reliability, and higher efficiency plans. / Ph. D.

Few current modeling tools are designed to predict short-term, high-risk runoff from critical source areas (CSAs) in watersheds which are significant sources of non point source (NPS) pollution. This study couples the Soil and Water Assessment Tool-Variable Source Area (SWAT-VSA) model with the Climate Forecast System Reanalysis (CFSR) model and the Global Forecast System (GFS) model short-term weather forecast, to develop a CSA prediction tool designed to assist producers, landowners, and planners in identifying high-risk areas generating storm runoff and pollution. Short-term predictions for streamflow, runoff probability, and soil moisture levels were estimated in the South Fork of the Shenandoah river watershed in Virginia. In order to allow land managers access to the CSA predictions a free and open source software based web was developed. The forecast system consists of three primary components; (1) the model, which preprocesses the necessary hydrologic forcings, runs the watershed model, and outputs spatially distributed VSA forecasts; (2) a data management structure, which converts high resolution rasters into overlay web map tiles; and (3) the user interface component, a web page that allows the user, to interact with the processed output. The resulting framework satisfied most design requirements with free and open source software and scored better than similar tools in usability metrics. One of the potential problems is that the CSA model, utilizing physically based modeling techniques requires significant computational time to execute and process. Thus, as an alternative, a deep learning (DL) model was developed and trained on the process based model output. The DL model resulted in a 9% increase in predictive power compared to the physically based model and a ten-fold decrease in run time. Additionally, DL interpretation methods applicable beyond this study are described including hidden layer visualization and equation extractions describing a quantifiable amount of variance in hidden layer values. Finally, a large-scale analysis of soil phosphorus (P) levels was conducted in the Chesapeake Bay watershed, a current location of several short-term forecast tools. Based on Bayesian inference methodologies, 31 years of soil P history at the county scale were estimated, with the associated uncertainty for each estimate. These data will assist in the planning and implantation of short term forecast tools with P management goals. The short term modeling and communication tools developed in this work contribute to filling a gap in scientific tools aimed at improving water quality through informing land manager's decisions. / PHD / Water pollution in the United States costs billions of dollars every year. Surface water pollution is caused by excess nutrients and effects the value of fisheries, recreational activities, and commercial operations, and can even lead to health hazards such as dangerous algal blooms. A major source of water pollution is from agricultural activities such as fertilizing crops. This type of pollution is called non-point source, as there is no obvious point where excess nutrients from fertilizers or manure enters the water, such as a discharge pipe, instead the pollution flows over the land first and then into the waterways following the rainfall-runoff patterns. One way to prevent non-point source pollution from agricultural activities is to give farmers tools to optimize operations in a way that can help lower the chance that pollution will occur. Scientific models, like a weather forecast, can help, but there is a lack of tools made specifically for reducing water pollution that are available to farmers. This work focuses on creating a free to use, high resolution and rapid update forecast delivered over the internet, capable of informing agricultural management practices to reduce water pollution. Over the course of this study, published advances in watershed modeling were made filling gaps in existing forecast technology. The final product combines cutting edge watershed science, machine learning and statistical models, web mapping tools, and terabytes of data to meet design goals.