Electron affinity of Li: A state-selective measurement

We have investigated the threshold of photodetachment of Li^- leading to the formation of the residual Li atom in the $2p ^2P$ state. The excited residual atom was selectively photoionized via an intermediate Rydberg state and the resulting Li^+ ion was detected. A collinear laser-ion beam geometry enabled both high resolution and sensitivity to be attained. We have demonstrated the potential of this state selective photodetachment spectroscopic method by improving the accuracy of Li electron affinity measurements an order of magnitude. From a fit to the Wigner law in the threshold region, we obtained a Li electron affinity of 0.618 049(20) eV.Comment: 5 pages,6 figures,22 reference

The effect of multiple internal representations on context rich instruction

This paper presents n-coding, a theoretical model of multiple internal mental representations. The n-coding construct is developed from a review of cognitive and imaging studies suggesting the independence of information processing along different modalities: verbal, visual, kinesthetic, social, etc. A study testing the effectiveness of the n-coding construct in an algebra-based mechanics course is presented. Four sections differing in the level of n-coding opportunities were compared. Besides a traditional instruction section used as a control group, each of the remaining three treatment sections were given context rich problems following the 'cooperative group problem solving' approach which differed by the level of n-coding opportunities designed into their laboratory environment. To measure the effectiveness of the construct, problem solving skills were assessed as was conceptual learning using the Force Concept Inventory. However, a number of new measures taking into account students' confidence in concepts were developed to complete the picture of student learning. Results suggest that using the developed n-coding construct to design context rich environments can generate learning gains in problem solving, conceptual knowledge and concept-confidence.Comment: Submitted to the American Journal of Physic

Design vocabulary for human-IoT systems communication

Digital devices and intelligent systems are becoming popular and ubiquitous all around us. However, they seldom provide sufficient feed-forwards and feedbacks to reassure users as to their current status and indicate what actions they are about to perform. In this study, we selected and analyzed nine concept videos on future IoT products/systems. Through systematic analysis of the interactions and communications of users with the machines and systems demonstrated in the films, we extracted 38 design vocabulary items and clustered them into 12 groups: Active, Request, Trigger functions, Approve, Reject, Notify, Recommend, Guide, Show problems, Express emotions, Exchange info, and Socialize. This framework can not only inspire designers to create selfexplanatory intelligence, but also support developers to provide a language structure at different levels of the periphery of human attention. Through the enhancement of situated awareness, human-IoT system interaction can become more seamless and graceful