How Digital Nudges Affect Consideration Set Size and Perceived Cognitive Effort in Idea Convergence of Open Innovation Contests

Open innovation initiatives are useful to acquire many ideas, but often face problems when it comes to selecting the best ideas. Idea convergence has been suggested as a first step in idea selection to filter those ideas that are worthy of further consideration. Digital nudges – digital interventions that aim at altering human behavior in a predictable way - could support convergence. However, their effects are largely unknown. This study explores how two digital nudges, selection strategy (inclusion/exclusion) and idea subset similarity (similar/random), affect the convergence outcomes consideration set size and perceived cognitive effort. We conducted a laboratory experiment with 88 students and found that guiding individuals towards an inclusion strategy results in smaller consideration sets and higher perceived cognitive effort. Moreover, presenting individuals with subsets of similar ideas resulted in smaller consideration sets. These insights are relevant for the design and use of digital nudges for convergence in open innovation environments

Comparison of artifacts between paste and collodion method of electrode application in pediatric EEG

Objectives: Children pose challenges to obtain quality EEG data due to excessive artifact. Collodion is used in EEG electrodes due to its water resistance and strong adhesive qualities. This study was done to evaluate differences in artifacts between the collodion and paste method. Methods: 115 subjects (children age \u3e 3 years) were randomized into paste and collodion groups and artifacts evaluated at baseline and every hour over 30s increments. Age, sleep state, and number of electrodes with artifact were also documented. T-test was performed to determine differences in the various parameters between the two groups. Results: 61 subjects were in the paste group and 54 in the collodion group. Mean of total seconds of artifact from 0 to 24h were 41.8s in paste group versus 30.3s in collodion group (P=0.02). Children \u3e 11 years old had less artifact than younger children from 0 to 24h (24.3 versus 41.2s, P=0.03), and from 24 to 48h (33.1 versus 43.1s, P=0.03). There was a significant effect of sleep vs. awake state recordings on artifact from 0 to 24h (30.3 versus 50.2s, P=0.01). Conclusion: Electrode problems are common with both collodion and paste in prolonged AEEG monitoring. However, for studies less than 24h, collodion may be a better alternative. Significance: Our study provides evidence that in some cases collodion may be a better alternative to paste in terms of decreased artifacts

Brief Communication

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67313/2/10.1177_088307389801300308.pd

Emulation of Large Scale Wireless Sensor Networks: From Real Neighbors to Imaginary Destination

International audienceThe ultimate test for many network layer protocols designed for wireless sensor networks would be to run on a large scale testbed. However, setting up a real-world large scale wireless sensor network (WSN) testbed requires access to a huge surface as well as extensive financial and human resources. Due to limited access to such infrastructures, the vast majority of existing theoretical and simulation studies in WSN are far from being validated in realistic environments. A more affordable approach is needed to provide preliminary insights on network protocol performances in large WSN. To replace large and expensive realistic testbeds, we introduce a novel approach to emulation. We propose a specifically designed experimental setup using a relatively small number of nodes forming a real one-hop neighborhood used to emulate any real WSN. The source node is a fixed sensor, and all other sensors are candidate forwarding neighbors towards a virtual destination. The source node achieves one forwarding step, then the virtual destination position and neighborhood are adjusted. The same source is used again to repeat the process. The main novelty is to spread available nodes regularly following a hexagonal pattern around the central node, used as the source, and selectively use subsets of the surrounding nodes at each step of the routing process to provide the desired density and achieve changes in configurations. Compared to real testbeds, our proposition has the advantages of emulating networks with any desired node distribution and densities, which may not be possible in a small scale implementation, and of unbounded scalability since we can emulate networks with an arbitrary number of nodes. Finally, our approach can emulate networks of various shapes, possibly with holes and obstacles. It can also emulate recovery mode in geographic routing, which appears impossible with any existing approach

Emulation of large scale WSN: from real neighbors to imaginary destination

International audienceSetting up large experimental testbeds for wireless sensor networks (WSN) requires access to a huge surface as well as extensive financial and human resources. Due to limited access to such infrastructures, the vast majority of existing theoretical and simulation studies on georouting are not evaluated in real environments. A more affordable approach is needed to provide preliminary insights on network protocols performance. To overcome the need for a large number of sensors required to perform realistic experiments we introduce a novel approach to emulation. We propose to emulate large scale experiment by using a smaller number of core sensor nodes. These nodes are the 1-hop neighborhood of node S, currently holding the packet to forward, and are potential next hops. The destination position is virtual, outside of this real neighborhood. Emulation is performed as follows: (i) S transmits the packet over a real wireless channel to the selected forwarding node B, (ii) re-map the node B to S and its neighborhood to core nodes, (iii) adjust the position of the virtual destination by translating it by BS. We repeat these steps until the virtual destination falls into the 1-hop neighborhood of the node currently holding the packet. Compared to real testbeds, our emulation allows testing networks of very high densities and provides unlimited scalability

Emulation of large scale wireless sensor networks: from real neighbors to imaginary destination

International audienc

Greedy geographic routing algorithms in a real environment

International audienceExisting theoretical and simulation studies on georouting appear detached from experimental studies in real environments. We set up our test environment by using WSN430 wireless sensor nodes. To overcome the need for significant number of wireless nodes required to perform a realistic experiment in high density network, we introduce a novel approach - emulation by using relatively small number of nodes in 1-hop experimental setup. Source node is a fixed sensor, all available sensors are candidate forwarding neighbors with virtual destination. Source node makes one forwarding step, destination position is adjusted, and the same source again searches for best forwarder. We compare three georouting algorithms. We introduce here Greedy geographical routing Algorithms in a REal environment (GARE) which builds a RNG by using ETX(uv) |uv| as edge weight (ETX(uv) counts all transmissions and possibly acknowledgments between two nodes until message is received), and selects RNG neighbor with greatest progress toward destination (if none of RNG neighbors has progress, all neighbors are considered). Our experiments show that GARE is significantly more efficient than existing XTC algorithm (applying RNG on ETX(uv)) in energy consumption. COP GARE selects neighbor with progress that minimizes ETX(uv) |uv| , and outperforms both algorithms

Emulation of large scale wireless sensor networks: from real neighbors to imaginary destination

International audienc