Boise State University

About: Boise State University is a education organization based out in Boise, Idaho, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 3698 authors who have published 8664 publications receiving 210163 citations. The organization is also known as: BSU & Boise State.

Silver chalcogenide based memristor devices

Abstract: We have fabricated two-terminal chalcogenide-based devices containing Ge 2 Se 3 and Ag that function as memristors. These devices have been electrically characterized at room temperature using quasi-static DC methods, AC sinusoidal methods, and AC pulse testing methods. In all cases, the devices exhibit memristive behavior.

Strategic use of bundling for reducing consumers' perceived risk associated with the purchase of new high-tech products

Abstract: Consumers’ perceived risk is a significant barrier to the adoption of new high-tech products. Based on a review of the marketing, consumer behavior and psychology literatures, a theoretical framework is developed here that suggests how bundling a new high-tech product with an existing technology could help reduce consumers’ perceived risk associated with the purchase of the new high-tech product. This manuscript also explores what factors influence consumers’ risk perception when a product bundle is available. Managers might use this framework to develop bundling strategies for high-tech products, and to understand what factors make such bundles more attractive.

Rapid prototyping technology for surgeries of the pediatric spine and pelvis: benefits analysis.

Abstract: Background:The benefits provided by rapid prototyping (RP) of patient anomalies in complex surgeries of the pediatric spine and pelvis were elucidated and characterized.Methods:Surgeons involved in the study were solicited from the Texas Scottish Rite Hospital for Children and the network of Shriner

Revealing Online Learning Behaviors and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching

Abstract: This study was conducted with data mining (DM) techniques to analyze various patterns of online learning behaviors, and to make predictions on learning outcomes. Statistical models and machine learning DM techniques were conducted to analyze 17,934 server logs to investigate 98 undergraduate students’ learning behaviors in an online business course in Taiwan. The study scientifically identified students’ behavioral patterns and preferences in the online learning processes, differentiated active and passive learners, and found important parameters for performance prediction. The results also demonstrated how data mining techniques might be utilized to help improve online teaching and learning with suggestions for online instructors, instructional designers and courseware developers.