Reading, writing and drawing in relief : the IPO relief-drawing set

Describes an improved relief·drawing set that allows durable, tangible, and vlsible embossed Images to be made wlth a ball-point pen

Reading, writing and drawing in relief : the IPO relief-drawing set

Describes an improved relief·drawing set that allows durable, tangible, and vlsible embossed Images to be made wlth a ball-point pen

Expectation-based user interaction

Multimedia and multimodal interfaces reflect the growing technological possibilities of computer-based systems for interaction with the user. The ongoing increase in communication bandwidth and the growing variety of communication channels enable further improvement in the user interface. However, how this increased communication capacity can optimally be exploited is as yet unknown. Since the functionality of these computer-based systems also continues to grow, the increased complexity of interaction procedures and the difficulty of mastering them are prime issues in the design of "easy to use" multimodal user interfaces. In order to appreciate more fully what is involved in self-evident and at the same time efficient interaction between user and system, we will first briefly describe the layered-protocol model of computer-human dialogue as proposed by Taylor (1988a). This conceptual framework emphasizes the relevance of layered feedback for the efficiency of communication. As indicated by Engel & Haakma (1993), in particular early feedback about the system's interpretation of the message part already received (I-feedback) as well as on machine expectations about message elements still to be received (E-feedback) are of relevance for the system's ease of use. Thereafter, as an interesting example of improved human-computer interaction through layered multimodal I- and E-feedback, an experimental trackball device will be described. It provides the user, in addition to the standard visual I-feedback about the current cursor position, with tactile E-feedback about the expected cursor target position. Lastly, our running experimental exploration of the possibilities for automatic cursor-endpoint prediction will be described, this research being of relevance for the further improvement of interaction with the mentioned trackball device with expectation-based force-feedback

Gut microbiota composition is associated with environmental landscape in honey bees.

There is growing recognition that the gut microbial community regulates a wide variety of important functions in its animal hosts, including host health. However, the complex interactions between gut microbes and environment are still unclear. Honey bees are ecologically and economically important pollinators that host a core gut microbial community that is thought to be constant across populations. Here, we examined whether the composition of the gut microbial community of honey bees is affected by the environmental landscape the bees are exposed to. We placed honey bee colonies reared under identical conditions in two main landscape types for 6 weeks: either oilseed rape farmland or agricultural farmland distant to fields of flowering oilseed rape. The gut bacterial communities of adult bees from the colonies were then characterized and compared based on amplicon sequencing of the 16S rRNA gene. While previous studies have delineated a characteristic core set of bacteria inhabiting the honey bee gut, our results suggest that the broad environment that bees are exposed to has some influence on the relative abundance of some members of that microbial community. This includes known dominant taxa thought to have functions in nutrition and health. Our results provide evidence for an influence of landscape exposure on honey bee microbial community and highlight the potential effect of exposure to different environmental parameters, such as forage type and neonicotinoid pesticides, on key honey bee gut bacteria. This work emphasizes the complexity of the relationship between the host, its gut bacteria, and the environment and identifies target microbial taxa for functional analyses