In arid countries both water scarcity and salinity represent the key factors which drastically limit crop yield in irrigated agriculture. In addition, relatively poor management practices with pretty low water productivity (WP) seriously aggravate the situation. In order to get “more crop per drop', i.e., to substantially improve water use efficiency, this thesis proposes the novel strategy NEMO (Nested Experimental, Modeling, and Optimization Strategy) for reliably evaluating an optimal irrigation schedule. The proposed methodology relies upon a close interaction between in-depth field investigations and physically based process modeling. It is tailored specifically to fit the requirements in resource-restricted regions. Comprehensive field experiments, on site measurements as well as various laboratory analyses provide a representative database for characterizing the relevant environmental parameters as e.g. the soil properties at the considered location and the prevailing climate. A substantial part of the data obtained from the field experiments provided the input for the internationally recognized SVAT software DAISY1 or APSIM2, both physically based irrigation models which have already been successfully applied in arid regions. APSIM - which is used in the advanced parts of the study - includes not only a process based model for soil moisture transport but also a plant physiological model which describes the plant behavior under specific irrigation scenarios for a selected crop throughout a growing season. The adaption of the irrigation model to local conditions and its preliminary parameterization firstly follows available guidelines and data for areas with similar climate and soil conditions. Reference data and deterministic weather data served to build up DAISY’s basic model files. DAISY is then used within the framework of the custom made and problem oriented optimization software GET-OPTIS for evaluating the corresponding optimal irrigation schedule for a first preliminary series of experiments (IrrEx1). A second series of field experiments (IrrEx2) was accompanied by transient soil moisture measurements, which served for evaluating the soil hydraulic parameters, while the obtained yield was used for calibrating the plant physiological model of APSIM. Taking still into account the stochastic nature of weather phenomena, a stochastic optimization with GET-OPTIS was then applied not only for the traditional full irrigation but also for the most important deficit irrigation and the irrigation with saline water. The obtained optimal irrigation schedules are subsequently used for a final series of rigorous irrigation experiments (IrrEx3) which specifically focused on: (1) full irrigation for high yields with most economic water application, (2) deficit irrigation aiming at a maximum yield with only a limited amount of irrigation water, and (3) full irrigation with saline irrigation water for maximum yield. At the harvesting time, the observed crop yield and the water productivity were compared - together with other plant characteristics - with the corresponding calculated values. The agreement between calculated and measured crop data was excellent. All the field experiments have been performed following a parallel use of the common traditional FAO class A-Pan method and the novel NEMO technology. Based on the outcome of the field experiments, the NEMO applications demonstrated a striking superiority throughout all scenarios as compared to the FAO method as regards economic efficiency and sustainable use of irrigation water in both aspects water quantity and salt accumulation. Contrary to common practice, the optimal NEMO irrigation schedule - which relies on stochastic weather data - has an extended validity. Together with the use of physical data and adequate process models, the developed methodology features a highly promising potential for generalizing the experimental findings for other, environmentally similar, regions. NEMO thus opens wide possibilities for a cost effective and sustainable long-term application to other arid or semi-arid areas.

Simulation-based design optimization and finite element method are used in this research to investigate weight reduction of car body structures made of metallic and composite materials under different design criteria. Besides crashworthiness in full frontal, offset frontal, and side impact scenarios, vibration frequencies, static stiffness, and joint rigidity are also considered. Energy absorption at the component level is used to study the effectiveness of carbon fiber reinforced polymer (CFRP) composite material with consideration of different failure criteria. A global-local design strategy is introduced and applied to multi-objective optimization of car body structures with CFRP components. Multiple example problems involving the analysis of full-vehicle crash and body-in-white models are used to examine the effect of material substitution and the choice of design criteria on weight reduction. The results of this study show that car body structures that are optimized for crashworthiness alone may not meet the vibration criterion. Moreover, optimized car body structures with CFRP components can be lighter with superior crashworthiness than the baseline and optimized metallic structures.

In the last years the steady development of autonomous driving technology has enabled the deployment of more mature autonomous vehicles. These vehicles have been applied in several pilot projects worldwide, most commonly in the form of small buses. At the same time, the amount of people traveling in especially urban areas is continuously growing, resulting in more trips in the transportation system. An efficient transportation system is therefore required to serve the growing passenger demand. Autonomous buses (AB) are assumed to have lower operational costs and with that public transport (PT) systems can potentially be designed more efficiently to facilitate the increased demand better. In this study, an AB specific simulation-based optimization framework is proposed which allows analyzing the impacts AB have on line-based PT systems. The thesis focuses on the transition from existing PT systems towards line-based PT systems operated partially or exclusively by AB. Existing work on PT service design is extended so that realistic AB systems can be investigated. This is achieved by (i) using AB specific operator cost formulations, (ii) integrating infrastructure costs required for AB operations, (iii) utilizing a dynamic, stochastic and schedule-based passenger assignment model for the simulation of PT networks and by (iv) formulating a multi-objective optimization problem allowing to investigate the stakeholder-specific impacts of AB. In Paper I the effects of AB, concerning service frequency and vehicle capacity, on fixed-line PT networks are investigated. Among other metrics, the changes are evaluated based on differences in level of service and passenger flow. Additionally, the sequential introduction of AB in existing PT systems is studied. The framework addresses a case study in Kista, Sweden. The study confirmed the initial hypothesis that the deployment of AB leads to an increase in service frequency and a marginal reduction in vehicle capacity. Furthermore, it could be seen that the deployment of AB increases the passenger load on AB lines and that passengers can shift from other PT modes towards the AB services. Paper II incorporates a multi-objective heuristic optimization algorithm in the simulation framework. The study investigates changes in transport network design based on the deployment of AB. The differences in user-focused and operator-focused network design are analyzed and the impact of AB on these is quantified. This study is applied to a case study in Barkarby, Sweden where a full-sized, line-based PT network is designed to exclusively operate AB. Among other findings, we show that the autonomous technology reduces the number of served bus stops and reduces the total PT network size. Additionally, average passenger waiting time can be reduced when deploying AB on user-focused PT networks, which in turn leads to a further reduction of user cost. / De senaste årens framsteg inom autonom körteknik har lett till mer mogna autonoma fordon. Dessa fordon har setts tillämpas i flera pilotprojekt över hela världen, oftast i form av små bussar. Samtidigt växer mängden människor som reser, särskilt i stadsområden, kontinuerligt vilket resulterar i fler resor i transportsystemet. Därför krävs ett effektivt transportsystem för att tillgodose det växande antalet passagerare. Autonoma bussar (AB) antas ha lägre driftskostnader och därmed kan system för kollektivtrafik (public transport, PT) potentiellt utformas mer effektivt för att underlätta den ökade efterfrågan bättre. I denna studie föreslås ett AB-specifikt simuleringsbaserat optimeringsramverk som gör det möjligt att analysera effekterna AB har på linjebaserade PT-system. Avhandlingen fokuserar på övergången från befintliga PT-system till linjebaserade PT-system som delvis eller uteslutande drivs av AB. Befintligt arbete med PT-tjänstdesign utvidgas så att realistiska AB-system kan undersökas. Detta uppnås genom att (i) använda AB-specifika operatörskostnadsformuleringar, (ii) integrera infrastrukturkostnader som krävs för AB-verksamhet, (iii) använda en dynamisk, stokastisk och schemabaserad modell för att tilldela passagerare vid simulering av PT-nät samt genom att (iv) formulera ett multifunktionellt optimeringsproblem som gör det möjligt att undersöka AB: s intressespecifika effekter. I artikel I undersöks effekterna av AB, med avseende på servicefrekvens och fordonskapacitet, på fasta linjer i PT-nät. Förändringar utvärderas bland annat utifrån skillnader i servicenivå och passagerarflöde. Dessutom studeras den sekventiella introduktionen av AB i befintliga PT-system. Det föreslagna ramverket tillämpas på en fallstudie i Kista, Sverige. Studien bekräftade den initiala hypotesen att utplaceringen av AB leder till en ökning av servicefrekvensen och en marginell minskning av fordonens kapacitet. Vidare kunde man se att utplaceringen av AB ökar passagerarbelastningen på AB-linjer och att passagerare kan skifta från andra PT-former mot AB-tjänsterna. Artikel II integrerar en multifunktionell heuristisk optimeringsalgoritm i ramverket för simuleringen. Studien undersöker förändringar i transportnätverkets design baserat på implementeringen av AB. Skillnaderna i användarfokuserad och operatörsfokuserad nätverksdesign analyseras och AB: s inverkan på dessa kvantifieras. Denna studie tillämpas på en fallstudie i Barkarby, Sverige, där ett fullstort linjebaserat PT-nät är utformat för att exklusivt driva AB. Vi visar bland annat att den autonoma tekniken reducerar antalet använda busshållplatser och reducerar den totala PT-nätstorleken. Dessutom kan implementeringen av AB på användarfokuserade PT-nät ytterligare förbättra servicenivån främst genom att minska den genomsnittliga väntetiden per passagerare.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The Air Force SEEK EAGLE Office (AFSEO) was chartered by the Secretary of the Air Force in December 1987. The mission of the AFSEO is to provide the United States Air Force increased combat capability through central management of the aircraft-stores certification process and provide in-house engineering and operations research capabilities. Additionally, the AFSEO is required to ensure the future viability of the aircraft-stores organic in-house capability with the insertion of evolving technologies. To accomplish this mission, the AFSEO employs all phases of the test process; from Digital Model and Simulation (DMS) to Open Air Range (OAR) flight tests. The AFSEO desires to prepare for the future DoD environment, and minimize the cost of developing its products that require advanced sensors and telemetry capability. For a number of years, a mainstay in the process has been instrumented aircraft. These aircraft were specially instrumented to support the mission of AFSEO. Similarly, stores were instrumented to obtain environmental data such as loads and vibration. With the rising cost of instrumentation and the national DoD trend to reduce the cost of development and maintenance of instrumentation, a new method will need to be found. Several advanced concepts in ground and airborne instrumentation at Eglin AFB are needed to support the mission of the AFSEO. These include a new generation of telemetry devices, sensors, and data acquisition components to provide rapid and cost effective instrumentation of test aircraft, stores, and suspension equipment. The new generation telemetry will provide integrated circuitry with “peel and stick” subminiature telemetry sensors. These telemetry sensors will provide flutter and structural loads data for aircraft-stores combinations. In conjunction with the telemetry sensors, advanced aircraft platform instrumentation will be needed to match precision flight mechanics to the spatial telemetry measurements for stress, strain, and dynamic activity of stores.

A two level hierarchical simulation and decision modeling framework is proposed for electric power networks involving PV based solar generators, various storage, and grid connection. The high level model, from a utility company perspective, concerns operational decision making and defining regulations for customers for a reduced cost and enhanced reliability. The lower level model concerns changes in power quality and changes in demand behavior caused by customers' response to operational decisions and regulations made by the utility company at the high level. The higher level simulation is based on system dynamics and agent-based modeling while the lower level simulation is based on agent-based modeling and circuit-level continuous time modeling. The proposed two level model incorporates a simulation based optimization engine that is a combination of three meta-heuristics including Scatter Search, Tabu Search, and Neural Networks for finding optimum operational decision making. In addition, a reinforcement learning algorithm that uses Markov decision process tools is also used to generate decision policies. An integration and coordination framework is developed, which details the sequence, frequency, and types of interactions between two models. The proposed framework is demonstrated with several case studies with real-time or historical for solar insolation, storage units, demand profiles, and price of electricity of grid (i.e., avoided cost). Challenges that are addressed in case studies and applications include 1) finding a best policy, optimum price and regulation for a utility company while keeping the customers electricity quality within the accepted range, 2) capacity planning of electricity systems with PV generators, storage systems, and grid, and 3) finding the optimum threshold price that is used to decide how much energy should be bought from sold to grid to minimize the cost. Mathematical formulations, and simulation and decision modeling methodologies are presented. A grid-storage analysis is performed for arbitrage, to explore if in future it is going to be beneficial to use storage systems along with grid, with future technological improvement in storage and increasing cost of electrical energy. An information model is discussed that facilitates interoperability of different applications in the proposed hierarchical simulation and decision environment for energy systems.

This thesis studies the Second-order Least Squares (SLS) estimation method in regression models with and without measurement error. Applications of the methodology in general quasi-likelihood and variance function models, censored models, and linear and generalized linear models are examined and strong consistency and asymptotic normality are established. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is used and its asymptotic properties are studied. Finite sample performances of the estimators in all of the studied models are investigated through simulation studies. / February 2009

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Maximum likelihood is an ubiquitous method used in the estimation of generalized linear mixed model (GLMM). However, the method entails computational difficulties and relies on the normality assumption for random effects. We propose a second-order least squares (SLS) estimator based on the first two marginal moments of the response variables. The proposed estimator is computationally feasible and requires less distributional assumptions than the maximum likelihood estimator. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is proposed. We show that the SLS estimators are consistent and asymptotically normally distributed under fairly general conditions in the framework of GLMM. Missing data is almost inevitable in longitudinal studies. Problems arise if the missing data mechanism is related to the response process. This thesis develops the proposed estimators to deal with response data missing at random by either adapting the inverse probability weight method or applying the multiple imputation approach. In practice, some of the covariates are not directly observed but are measured with error. It is well-known that simply substituting a proxy variable for the unobserved covariate in the model will generally lead to biased and inconsistent estimates. We propose the instrumental variable method for the consistent estimation of GLMM with covariate measurement error. The proposed approach does not need any parametric assumption on the distribution of the unknown covariates. This makes the method less restrictive than other methods that rely on either a parametric distribution of the covariates, or to estimate the distribution using some extra information. In the presence of data outliers, it is a concern that the SLS estimators may be vulnerable due to the second-order moments. We investigated the robustness property of the SLS estimators using their influence functions. We showed that the proposed estimators have a bounded influence function and a redescending property so they are robust to outliers. The finite sample performance and property of the SLS estimators are studied and compared with other popular estimators in the literature through simulation studies and real world data examples.

Embodied conversational agents put a “human” touch on intelligent tutoring systems by using conversation to support learning. When considering instruction in interpersonal domains, such as intercultural negotiation, the development of an interpersonal relationship with one’s pedagogical agent may play a significant role in learning. However, there is conflicting evidence in the literature both regarding the ability of agents to cultivate social relationships with humans, and their effect on learning. In this dissertation, I present a model of social dialog designed to affect learners’ interpersonal relations with virtual agents, a development process for creating social dialog, and empirical studies showing that this dialog has significant effects on learners’ perceptions of the agents and negotiation performance. In early work, I explicitly prompted learners to have social goals for the interaction. I found that while students who reported social goals for interacting with the agents had significantly higher learning gains, explicit prompting was not effective at inducing these goals. I thus focused on implicit influence of learner goals, developing a model of social instructional dialog (SID) that integrates conversational strategies that are theorized to produce interpersonal effects on relationships. In two subsequent studies, an agent with the SID model engendered greater feelings of entitativity, shared perspective, and trust, suggesting that the model improved learner social relationships with the agent. Importantly, these effects transferred to other agents encountered later in the environment. The social dialog condition also made fewer errors and achieved more negotiation objectives in a subsequent negotiation than a control group, evidence that the improved social relationship lead to better negotiation performance. These findings regarding interpersonal relationships with agents contribute to the literature on learner-agent interactions, and can guide the future development of agents in social environments.

This thesis studies the Second-order Least Squares (SLS) estimation method in regression models with and without measurement error. Applications of the methodology in general quasi-likelihood and variance function models, censored models, and linear and generalized linear models are examined and strong consistency and asymptotic normality are established. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is used and its asymptotic properties are studied. Finite sample performances of the estimators in all of the studied models are investigated through simulation studies.