Affordances, constraints and information flows as ‘leverage points’ in design for sustainable behaviour

Copyright @ 2012 Social Science Electronic PublishingTwo of Donella Meadows' 'leverage points' for intervening in systems (1999) seem particularly pertinent to design for sustainable behaviour, in the sense that designers may have the scope to implement them in (re-)designing everyday products and services. The 'rules of the system' -- interpreted here to refer to affordances and constraints -- and the structure of information flows both offer a range of opportunities for design interventions to in fluence behaviour change, and in this paper, some of the implications and possibilities are discussed with reference to parallel concepts from within design, HCI and relevant areas of psychology

Committed to Safety: Ten Case Studies on Reducing Harm to Patients

Presents case studies of healthcare organizations, clinical teams, and learning collaborations to illustrate successful innovations for improving patient safety nationwide. Includes actions taken, results achieved, lessons learned, and recommendations

Hampton-Seabrook Estuary Habitat Restoration Compendium

The goal of this report is to identify restoration opportunities within the watershed derived from data on habitat change. Many other factors exist that are important in the identification and selection of restoration projects, including water quality and non-point source pollution, water withdrawal, harbor maintenance, recreational impacts, human history, and socioeconomic factors, among others. Although information regarding these factors is not explicitly included in this analysis, these factors must be considered and addressed as they may limit the potential for success in specific restoration effort

Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study

Developing robot agnostic software frameworks involves synthesizing the disparate fields of robotic theory and software engineering while simultaneously accounting for a large variability in hardware designs and control paradigms. As the capabilities of robotic software frameworks increase, the setup difficulty and learning curve for new users also increase. If the entry barriers for configuring and using the software on robots is too high, even the most powerful of frameworks are useless. A growing need exists in robotic software engineering to aid users in getting started with, and customizing, the software framework as necessary for particular robotic applications. In this paper a case study is presented for the best practices found for lowering the barrier of entry in the MoveIt! framework, an open-source tool for mobile manipulation in ROS, that allows users to 1) quickly get basic motion planning functionality with minimal initial setup, 2) automate its configuration and optimization, and 3) easily customize its components. A graphical interface that assists the user in configuring MoveIt! is the cornerstone of our approach, coupled with the use of an existing standardized robot model for input, automatically generated robot-specific configuration files, and a plugin-based architecture for extensibility. These best practices are summarized into a set of barrier to entry design principles applicable to other robotic software. The approaches for lowering the entry barrier are evaluated by usage statistics, a user survey, and compared against our design objectives for their effectiveness to users

Accessing Antecedents and Outcomes of RFID Implementation in Health Care

This research first conceptualizes, develops, and validates four constructs for studying RFID in health care, including Drivers (Internal and External), Implementation Level (Clinical Focus and Administrative Focus), Barriers (Cost Issues, Lack of Understanding, Technical Issues, and Privacy and Security Concerns), and Benefits (Patient Care, Productivity, Security and Safety, Asset Management, and Communication). Data for the study were collected from 88 health care organizations and the measurement scales were validated using structural equation modeling. Second, a framework is developed to discuss the causal relationships among the above mentioned constructs. It is found that Internal Drivers are positively related to Implementation Level, which in turn is positively related to Benefits and Performance. In addition, Barriers are found to be positively related to Implementation Level, which is in contrast to the originally proposed negative relationship. The research also compares perception differences regarding RFID implementation among the non-implementers, future implementers, and current implementers of RFID. It is found that both future implementers and current implementers consider RFID barriers to be lower and benefits to be higher compared to the non-implementers. This paper ends with our research implications, limitations and future research

Hampton-Seabrook Estuary Restoration Compendium

The Hampton-Seabrook Estuary Habitat Restoration Compendium (HSEHRC) is a compilation of information on the historic and current distributions of salt marsh and sand dune habitats and diadromous fishes within the Hampton-Seabrook Estuary watershed. These habitats and species groups were selected due to the important ecological role they play within the watershed and with effective restoration and conservation efforts, will continue to play. Other ecologically important habitats and species, such as avifauna, shellfish and eelgrass beds, currently are or historically were present within the watershed. Shellfish and seagrass are recognized as important habitats within the Estuary, but were not included in the current report because a different analytical approach may be required for such dynamic and/or short-lived species. A recent report by the New Hampshire (NH) Audubon Society details modern bird use of the Hampton-Seabrook Estuary (McKinley and Hunt 2008). Restoration opportunities have been identified within the watershed by evaluating habitat loss and changes in land use over time. Restoration opportunities are not prioritized in order to allow the goals and objectives of each restoration practitioner to govern project selection. However, in accordance with an ecosystem-based approach to restoration, areas containing multi-habitat restoration opportunities are considered to be of the highest priority. Furthermore, restoration efforts should ensure processes critical for the support of restored components are maintained or reestablished. The goal of this report is to identify restoration opportunities within the watershed derived from data on habitat change. Many other factors exist that are important in the identification and selection of restoration projects, including water quality and non-point source pollution, water withdrawal, harbor maintenance, recreational impacts, human history, and socioeconomic factors, among others. Although information regarding these factors is not explicitly included in this analysis, these factors must be considered and addressed as they may limit the potential for success in specific restoration efforts We present a series of maps detailing changes in the extent of sand dune and salt marsh habitats over time, the current and historic distribution of seven diadromous fish species, and restoration opportunities within the Hampton-Seabrook Estuary and watershed. A narrative describes the methods used, the results of analyses and examples of prominent restoration projects. Each major section concludes with references used in the narrative and maps. The maps are available for viewing as portable document format (.pdf) files. For those with GIS capabilities, the ArcMap 9.2 project files, associated data files and metadata are included on the compact disc as well. The underlying concept and methods for the HSEHRC stem from a previous project conducted within the Great Bay Estuary, the Great Bay Estuary Restoration Compendium (Odell et al. 2006)

Understanding Model-Based Reinforcement Learning and its Application in Safe Reinforcement Learning

Model-based reinforcement learning algorithms have been shown to achieve successful results on various continuous control benchmarks, but the understanding of model-based methods is limited. We try to interpret how model-based method works through novel experiments on state-of-the-art algorithms with an emphasis on the model learning part. We evaluate the role of the model learning in policy optimization and propose methods to learn a more accurate model. With a better understanding of model-based reinforcement learning, we then apply model-based methods to solve safe reinforcement learning (RL) problems with near-zero violation of hard constraints throughout training. Drawing an analogy with how humans and animals learn to perform safe actions, we break down the safe RL problem into three stages. First, we train agents in a constraint-free environment to learn a performant policy for reaching high rewards, and simultaneously learn a model of the dynamics. Second, we use model-based methods to plan safe actions and train a safeguarding policy from these actions through imitation. Finally, we propose a factored framework to train an overall policy that mixes the performant policy and the safeguarding policy. This three-step curriculum ensures near-zero violation of safety constraints at all times. As an advantage of model-based method, the sample complexity required at the second and third steps of the process is significantly lower than model-free methods and can enable online safe learning. We demonstrate the effectiveness of our methods in various continuous control problems and analyze the advantages over state-of-the-art approaches