Multi-stage programming is a valuable technique for improving the performance of computer programs through run-time optimization. Current implementations of multi-stage programming do not support run-time type introspection, which is a significant feature of modern object-oriented platforms such as Java and C#. This is unfortunate because many programs that use type introspection in these languages could be improved with multi-staging programming. The aim of this research is to investigate the interaction between multi-stage programming and object-oriented type introspection. This is done by the invention of a new programming language that is a multi-stage extension to C#. The language is capable of expressing traditional multi-stage programs as well as a new style of multi-stage programs that incorporate type introspection, most notably polytypic algorithms such as object serialization. A compiler for the language is implemented and freely available. The language is significant because it is the first object-oriented, multi-stage language; the first attempt to combine type introspection with multi-stage programming; and the first exploration of polytypic programming in a multi-stage context.

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Gameplay programming is vital for video game development and benefits from good tools and techniques. However, techniques are still used in the industry that involves describing how the computer operates. Reactive programming is a way to describe eventful and stateful computer programs declaratively, focusing on what the program should accomplish. This thesis uses the reactive library UniRx with the game engine Unity 5 to create an FPS with reactive techniques, and discusses the advantages and disadvantages of these. Object-oriented reactive programming is used to combine reactive objects with Unity’s component-based framework without using wrappers for non-reactive objects. The results include static methods for observable user input, patterns when defining game components with reactive content, and communication between game objects through interface components. It can be concluded that reactive programming for game programming enables the programmer to describe and understand game logic in a declarative manner. However, combining UniRx and Unity 5 led to complex patterns. Suggestion for future work is using reactive programming with game engines that allows to design game objects that are fully reactive. / Gameplay-programmering är avgörande för utveckling av videospel och har nytta av bra verktyg och tekniker. Dock används tekniker i spelbranschen som kräver att programmeraren beskriver hur datorn utför uppgifter. Reaktiv programmering är ett sätt att beskriva händelserika och föränderliga datorprogram med fokus på vad programmet ska utföra. Denna avhandling använder det reaktiva biblioteket UniRx med spelmotorn Unity 5 för att skapa en FPS med reaktiva tekniker, samt diskuterar fördelarna och nackdelarna med dessa. Objektorienterad reaktiv programmering används för att kombinera reaktiva objekt med Unity’s komponentbaserade ramverk utan att använda wrappers för icke-reaktiva objekt. Resultaten inkluderar statiska metoder för observerbar användarinput, programmeringsmönster för definition av spelkomponenter med reaktivt innehåll och kommunikation mellan spelobjekt via gränssnittskomponenter. Man kan dra slutsatsen att reaktiv programmering för spelprogrammering gör det möjligt för programmeraren att beskriva och förstå spellogik på ett deklarativt sätt. Kombinationen av UniRx och Unity 5 ledde emellertid till komplexa mönster. Förslag till framtida arbete är att använda reaktiv programmering med spelmotorer som gör det möjligt att designa spelobjekt som är helt reaktiva.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Call number: LD2668 .T4 1984 H92 / Master of Science

Commercial fresh vegetable production is one of the most rewarding and risky farming activities. The price and yield variations throughout the production year, the special characteristics of fresh vegetable produce (i.e. perishability), and the changing consumer demands are some of the factors contributing to the increased uncertainty faced by vegetable producers. This dissertation combined mathematical programming and econometric techniques to: 1) investigate the optimal production and marketing practices under different price distribution information scenarios, risk aversion levels and marketing outlets and 2) examine growers’ preferences as well the effect of risk aversion levels and growers’ risk perception on the choice of marketing contracts. Specifically, the following three modeling approaches were adopted in order to achieve the dissertation objectives: 1) quadratic programming under a mean-variance framework, 2) discrete choice experiments and 3) a combination of quadratic and integer programming embodied in a meanvariance framework. The findings indicate that optimal production practices and the resulting net returns are substantially influenced not only by the choice of marketing channel but also by growers’ risk aversion levels as well as price knowledge. Furthermore, regarding the choice of marketing contracts, the results highlight the existence of heterogeneity in preferences and illustrate the importance of certification cost, in line with the previous literature. Lastly, the findings indicate that risk aversion and risk preferences do not play a significant role in the choice of contractual agreements by farmers.

The aim of this dissertation is to present an algorithm for mixed integer programs which when started with a good heuristic solution can find improved solutions and reduce the error estimate as quickly as possible. This is achieved by using two ideas: a fixed order branch-and-bound method with selective expansion of subproblems and the sieve strategy which uses stronger than optimal bounds. The fixed order branch-and-bound method with selective expansion of subproblems is effective in reducing the error estimate quickly whereas the sieve strategy is effective in reducing the error estimate as well as finding improved solutions quickly. Computational experience is reported.

A stochastic dynamic programming model (called P.B.S.D.P.) based on the consideration of peak discharge and time between peaks as two stochastic variables has been used to model and to solve a reservoir operation problem. This conceptualization of the physical reality allows to solve, in this order, the tactical and strategic operation of surface reservoirs. This P.B.S.D.P. model has been applied to the Sau reservoir in the Northeastern corner of Spain. The results showed a significant improvement over the currently used operation procedure, yielding values of yearly average electricity production that are somewhat under 6% of what could have been the maximum electricity production.

Differently from other regions in the Planet, since 2010, in Latin America textual programming language (Rhinoscripting) is being replaced by its visual equivalent (Grasshopper). This is a consequence of our preference for an interactive platform, and because our design problems are not as complex, so we aim to control geometrical problems or aspects belonging to an product scale instead of an architectural one. Problems emerging when creating code could be improved by modifying and reusing existing solutions as a starting point, since learning would not be centered in the object but in the process of creating it, using a suitable instrument.

Well-publicized lost opportunities for U.S. and coalition air forces to strike enemy leadership targets in Afghanistan and Iraq demonstrate the importance of Time Sensitive Targeting. How do we "pair" the weapon and weapons delivery platform with their target? The available platforms (aircraft, manned or unmanned) may be on the ground in an alert status, loitering airborne, or on their way to attack other targets. The problem is compounded by the facts that we actually wish to (a) create multiple strike packages simultaneously, (b) recompose existing strike packages that are disrupted by the new plans, (c) minimize such disruptions, (d) satisfy minimum kill probabilities, and (e) avoid the attrition of tasked assets. This thesis develops an automated, optimizing, heuristic decision aid, "RAPT-OR", that rapidly revises a current Air Taking Order (ATO) to meet the requirements above. Using a set-packing model, RAPT-OR, an ATO near optimally, on a desktop PC, in less than two seconds, for a typical scenario with 40 aircraft, four new targets and hundreds of potential strike packages. RAPT-OR allows decision makers the ability of adjusting risk acceptance in the formulation of possible courses of action by manipulating friendly attrition importance in formulating a solution.