Growing volumes of wood are being used in construction, interior architecture, and product design, resulting in increasing amounts of wood waste. Using this waste is challenging, because it is too labor-intensive to process large volumes of uneven wood pieces that vary in geometry, quality, and origin. The project “Circular Wood for the Neighborhood” researches how advanced computational design and robotic production approaches can be used to create meaningful applications from waste wood. shifting the perception of circular wood as a simply harvested stream, towards a material with unique aesthetics of its own right. The complexity of the material is suggested to be tackled by switching from the object-oriented design towards designing soft systems. The system developed uses a bottom-up approach where each piece of wood aggregates according to certain parameters and the designed medium is mainly rule-sets and connections. The system is able to produce many options and bring the end-user for a meaningful co-design instead of choosing from the pre-designed options. Material-driven design algorithms were developed, which can be used by designers and end-users to design bespoke products from waste wood. In the first of three case studies, a small furniture item (“coffee table”) was designed from an old door, harvested from a renovation project. For its production, two principle approaches were developed: with or without preprocessing the wood. The principles were tested with an industrial robotic arm and available waste wood. A first prototype was made using the generated aggregation from the system, parametric production processes and robotic fabrication.
Advanced technology is a primary solution for the shortage of care professionals and increasing demand for care, and thus acceptance of such technology is paramount. This study investigates factors that increase use of advanced technology during elderly care, focusing on current use of advanced technology, factors that influence its use, and care professionals’ experiences with the use. This study uses a mixed-method design. Logfiles were used (longitudinal design) to determine current use of advanced technology, questionnaires assessed which factors increase such use, and in-depth interviews were administered to retrieve care professionals’ experiences. Findings suggest that 73% of care professionals use advanced technology, such as camera monitoring, and consult clients’ records electronically. Six of nine hypotheses tested in this study were supported, with correlations strongest between performance expectancy and attitudes toward use, attitudes toward use and satisfaction, and effort expectancy and performance expectancy. Suggested improvements for advanced technology include expanding client information, adding report functionality, solving log-in problems, and increasing speed. Moreover, the quickest way to increase acceptance is by improving performance expectancy. Care professionals scored performance expectancy of advanced technology lowest, though it had the strongest effect on attitudes toward the technology.
Game Mechanics is aimed at game design students and industry professionals who want to improve their understanding of how to design, build, and test the mechanics of a game. Game Mechanics will show you how to design, test, and tune the core mechanics of a game—any game, from a huge role-playing game to a casual mobile phone game to a board game. Along the way, we’ll use many examples from real games that you may know: Pac-Man, Monopoly, Civilization, StarCraft II, and others. The authors provide two features. One is a tool called Machinations that can be used to visualize and simulate game mechanics on your own computer, without writing any code or using a spreadsheet. The other is a design pattern library, including the deep structures of game economies that generate challenge and many kinds of feedback loops.
