Recent research has indicated that lead in water of private wells is in the range of that which caused problems in Flint, Michigan. However, there is limited understanding of the mechanisms for water lead release in these systems. We evaluated water lead at the homes of two children with elevated blood lead in Macon County (North Carolina), which did not have identifiable lead paint or lead dust hazards, and examined water lead release patterns among 15 private wells in the county. Water lead release patterns differed among the 15 private wells. Problems with lead release were associated with (1) dissolution of lead from plumbing during periods of stagnation; (2) scouring of leaded scales and sediments during initial water use; and (3) mobilization of leaded scales during continued water use. Accurate quantification of water lead was highly dependent on sample collection methods, as flushing dramatically reduced detection of lead hazards. The incidence of high water lead in private wells may be present in other counties of North Carolina and elsewhere in the United States. The underestimation of water lead in wells may be masking cases of elevated blood lead levels attributed to this source and hindering opportunities to mitigate this exposure.
The most powerful scientific advances are propelled by creative ideas that cross disciplinary boundaries. Few fields exemplify this as thoroughly as nanoscience, which promises to benefit humankind by delivering radically new technologies-if scientists from different disciplines can work together creatively. Unfortunately, initiating interdisciplinary conversations can be a costly undertaking in the context of academia, where disciplines are separated by entrenched physical and social structures. We present a new method, called 'speedstorming,' designed to improve the process of teaching and initiating creative collaboration. Early results show great promise for accelerating the rate of collaboration formation in the field of nanoscience. We found that for teaching and forming creative collaboration, speedstorming is more efficient and more effective than group brainstorming. This article explores the rationale for using such a method in nanoscience research and education and details the steps to conducting speedstorming sessions to achieve several common aims in a variety of settings. Limitations and unanswered questions regarding the method are also explored.
Creative collaborations that cross disciplinary boundaries are essential to innovation. Individuals face challenges, however, in forming new collaborations. Empirical and anecdotal evidence suggests that the common formats of brainstorming and free-form networking are insufficient for enabling such collaborations to form. We present a potential solution called speedstorming, a pair-wise method of creative interaction similar to the round-robin 'speeddating' technique. Speedstorming combines an explicit purpose, time limits, and one-on-one encounters to create a setting where boundary-spanning opportunities can be recognized, ideas can be explored at a deep level of interdisciplinary expertise, and potential collaborators can be quickly assessed. A comparison of speedstorming and brainstorming suggests that ideas from speedstorming were more technically specialized and that speedstorming participants were more certain in their assessments of the collaborative potential of others. This paper concludes with a discussion of the method's application in a variety of settings.
This paper presents a novel computational approach to the study of creativity. In particular, it discusses a modeling framework that addresses the worth of ideas ascribed by agents embedded in a social world. The triple objective of this system is to improve our understanding of how ideas may emerge from a few individuals, how social interaction may result in the ascription of value to new ideas, and how culture may evolve through time, transforming or replacing dominant or consensual ideas. The proposed system encompasses commonalities in existing theories of creativity, and suggests future theoretical directions that can be explored via simulation. Experimentation, Theory COMPUTATIONAL STUDIES OF CREATIVITYA variety of computational approaches to the study of creativity has been developed in the last few decades. We can distinguish at least three modeling types: individualistic, interactionist and social simulations.The individualistic modeling approach results from aiming to build generative systems that in some way replicate or are inspired directly by cognitive research. Early examples aimed to model the discovery processes of scientific laws [19]. Others demonstrated the ability to generate interesting solutions that resemble divergent thinking puzzles [15].Systems of this type continue to draw attention today in number theory [8], painting [7], and literary composition [20,24]. This approach has been a prolific source of models, capturing the attention of scholars, and igniting long debates about their achievements, shortcomings and implications [3]. An underlying assumption of this type of models is that creativity is a type or a combination of generative processes carried out by an isolated creator (the computer), and that the resulting solutions carry the creativeness to be recognized by an external judge (the programmer or the audience). A common goal of these systems is to model the learning processes by which the creator explores and possibly transforms the search space, producing final solutions that experts either consider surprising or indistinguishable from human solutions.The interactionist modeling approach explicitly integrates the human into the system, guiding the generative processes by the evaluations of the human participant. This interaction aims to capture the needs, preferences or evaluation principles of experts. Human intervention often becomes a taxing demand, and constitutes a bottleneck in the process, although it does prevent the sprawl of unlimited random alternatives. Creative evolutionary systems are an example of this type of modeling [2], where human selection becomes part of the genetic operators in the system. A variant of this type includes systems that interact with their physical environment in order to constrain their search [4]. A key assumption of this type of models is that creativity -or more generally, learning-is a product of the interaction between computers and their context.A third type of modeling approach, social simulation, is gaining acceptance in recent y...
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