Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency, by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network (BN) modelling is explored as an alternative to traditional risk calculation. BNs can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a BN has been developed and parameterised for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterisation using azoxystrobin as an example. We also demonstrate seasonal risk calculation for the three pesticides.
We present a new technology-based paradigm to support embodied mathematics educational games, using wearable devices in the form of SmartPhones and SmartWatches for math learning, for full classes of students in formal in-school education settings. The Wearable Learning Games Engine is web based infrastructure that enables students to carry one mobile device per child, as they embark on math team-based activities that require physical engagement with the environment. These Wearable Tutors serve as guides and assistants while students manipulate, measure, estimate, discern, discard and find mathematical objects that satisfy specified constraints. Multiplayer math games that use this infrastructure have yielded both cognitive and affective benefits. Beyond math game play, the Wearable Games Engine Authoring Tool enables students to create games themselves for other students to play; in this process, students engage in computational thinking and learn about finite-state machines. We present the infrastructure, games, and results for a series of experiments on both game play and game creation.
Insights into conducting research and the writing of scientific papers are given by Prof. Whitesides in this short essay. The manuscript and its guidelines has been circulated within the Whitesides' research group since 1989.
Quantitative ethnographers across a range of domains study complex collaborative thinking (CCT): the processes by which members of a group or team develop shared understanding by making cognitive connections from the statements and actions of the group. CCT is difficult to model because the actions of group members are interdependent—the activity of any individual is influenced by the actions of other members of the group. Moreover, the actions of group members engaged in some collaborative tasks may need to follow a particular order. However, current techniques can account for either interdependence or order, but not both. In this paper, we present directed epistemic network analysis (dENA), an extension of epistemic network analysis (ENA), as a method that can simultaneously account for the interdependent and ordered aspects of CCT. To illustrate the method, we compare a qualitative analysis of two U.S. Navy commanders working in a simulation to ENA and dENA analyses of their performance. We find that by accounting for interdependence but not order, ENA was not able to model differences between the commanders seen in the qualitative analysis, but by accounting for both interdependence and order, dENA was able to do so.
Objective: In the United States, there are numerous ongoing efforts to remedy the Word Gap: massive differences in heardvocabulary for poor versus advantaged children during the first 5 years of life. One potentially important resource for vocabulary exposure is children's book reading sessions, which are more lexically diverse than standard caregiver-child conversations and have demonstrated significant correlational and causal influences on children's vocabulary development. Yet, nationally representative data suggest that around 25% of caregivers never read with their children. Method: This study uses data from 60 commonly read children's books to estimate the number of words that children are exposed to during book reading sessions. We estimated the total cumulative word exposure for children who are read to at varying frequencies corresponding to nationally representative benchmarks across the first 5 years of life. Results: Parents who read 1 picture book with their children every day provide their children with exposure to an estimated 78,000 words each a year. Cumulatively, over the 5 years before kindergarten entry, we estimate that children from literacy-rich homes hear a cumulative 1.4 million more words during storybook reading than children who are never read to. Conclusion: Home-based shared book reading represents an important resource for closing the Word Gap.
Women are underrepresented in the top ranks of the scientific career, including the biomedical disciplines. This is not generally the result of explicit and easily recognizable gender biases but the outcome of decisions with many components of unconscious nature that are difficult to assess. Evidence suggests that implicit gender stereotypes influence perceptions as well as decisions. To explore these potential reasons of women’s underrepresentation in life sciences we analyzed the outcome of gender-science and gender-career Implicit Association Tests (IAT) taken by 2,589 scientists working in high profile biomedical research centers. We found that male-science association is less pronounced among researchers than in the general population (34% below the level of the general population). However, this difference is mostly explained by the low level of the IAT score among female researchers. Despite the highly meritocratic view of the academic career, male scientists have a high level of male-science association (261% the level among women scientists), similar to the general population.
Learning has been defined functionally as changes in behavior that result fromexperience or mechanistically as changes in the organism that result from experience. Bothtypes of definitions are problematic. We define learning as ontogenetic adaptation, that is, aschanges in the behavior of an organism that result from regularities in the environment of theorganism. This functional definition not only solves the problems of other definitions but alsohas important advantages for cognitive learning research.