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Institution

Boise State University

EducationBoise, Idaho, United States
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.


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Journal ArticleDOI
TL;DR: In this paper, a modified Bridgman method was used to grow Ni-Mn-Ga single crystals from an oriented seed using a modified version of the BIM method.

58 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated persistence factors that contributed to students' successful completion from one of the largest and most successful online programs in the United States of America and found that both personal and program attributes contributed to student's successful completion of a fully online program.
Abstract: Despite their rapid growth, online programs routinely face student attrition. How to retain students and help them successfully complete is usually a top priority for online programs. This study investigated persistence factors that contributed to students’ successful completion from one of the largest and most successful online programs in the United States of America. Results show that both personal and program attributes contributed to students’ successful completion of a fully online program. Main individual attributes include interest in or career goals related to technology, time and effort invested, and perceived utility of learning. Main program attributes include relevancy of courses to individual or professional needs, satisfaction with courses and program, and ties between coursework and job promotion. Results of this study have implications in terms of prioritizing different attributes and strategizing resources to improve completion and graduation rates for fully online programs.

58 citations

Journal ArticleDOI
TL;DR: Comparisons between road andoff-road cycling events indicate that off-road cyclists sustain more fractures, dislocations and concussions than their road-event counterparts.
Abstract: Off-road bicycles, commonly called ‘mountain bikes’, have become increasingly popular worldwide since their introduction in the western US in the late 1970s. This popularity is partly because these vehicles can be ridden on a wide variety of terrain which is not accessible to other types of bicycle. Although early versions were rather crude, off-road bicycles today typically include high strength, lightweight frames with a wide array of available suspension and braking systems. Virtually all aspects of the technology continue to evolve, including components and protective equipment. As the popularity of off-road cycling has increased, so too has the interest and level of participation in the competitive aspects of the sport. Currently, 2 organisations — the National Off-Road Bicycle Association (NORBA) and the Union Cycliste Internationale (UCI) — sponsor the major events within the US and around the world. To date, the majority of studies have been descriptive in nature, with data collected via self-report, questionnaire formats. Only 1 prospective study has been reported thus far, which surveyed a major international competition held in the US in 1994. Injury rates calculated on the basis of injuries per ride or event in competitive venues have been reported, ranging from 0.2 to 0.39% compared with 0.30% for recreational participants. Retrospective data collected from recreational and competitive riders indicate that from 20 to 88% of those surveyed reported having sustained an injury during the previous year of participation. The majority of injuries appear to be acute, traumatic episodes involving the extremities, with contusions and abrasions being the most common. In general, the incidence of more severe injuries such as dislocations, fractures and concussions is low. Comparisons between road and off-road cycling events indicate that off-road cyclists sustain more fractures, dislocations and concussions than their road-event counterparts. Future research should incorporate epidemiological methods of data collection to determine the relationships between vehicle design, terrain and safety equipment and riding-related accidents. Further, those engaged in such research should attempt to set a standard definition for injury.

58 citations

Journal ArticleDOI
TL;DR: It is shown how the VOI provides a measure of model resolution and how insight gained from VOI analysis cannot be gained through similar examination of the average sensitivity distributions, as well as a comparative measure of survey performance.
Abstract: Solution appraisal is difficult for large 3D, nonlinear inverse problems such as electrical resistivity tomography (ERT). We construct the volume of investigation index (VOI) as the sensitivity of the inversion result to a variable-reference model. This limited exploration of the model space provides an efficient and pragmatic method of appraisal for a particular data set and a 3D model domain. We present a synthetic example to demonstrate the applicability of the VOI as a tool for characterizing model reliability for 3D ERT and as a method of survey design. We show how the VOI provides a measure of model resolution and how insight gained from VOI analysis cannot be gained through similar examination of the average sensitivity distributions. In the context of ERT monitoring of an injection/withdrawal experiment, we utilize the VOI for judging the degree of reliability of hydrogeological interpretations that stem from features observed in the estimated electrical-conductivity models. We employ the VOI for the experimental data as a comparative measure of survey performance. For this experiment, the VOI shows that a larger, more artifact-free region of reliability is achieved using a circulating vertical dipole-dipole survey geometry, as opposed to a horizontal dipole-dipole survey geometry. The experimental VOI distributions exhibit dependence on the borehole infrastructure and the actual earth model.

58 citations

Journal ArticleDOI
TL;DR: Performance limits of classification of sparse as well as not necessarily sparse signals based on compressive measurements are provided and it is shown that Kullback-Leibler and Chernoff distances between two probability density functions under any two hypotheses are preserved up to a factor of M/N.
Abstract: Most of the recent compressive sensing (CS) literature has focused on sparse signal recovery based on compressive measurements. However, exact signal recovery may not be required in certain signal processing applications such as in inference problems. In this paper, we provide performance limits of classification of sparse as well as not necessarily sparse signals based on compressive measurements. When signals are not necessarily sparse, we show that Kullback-Leibler and Chernoff distances between two probability density functions under any two hypotheses are preserved up to a factor of M/N with M(<;N)-length compressive measurements compared to that with N-length original measurements when the pdfs of the original-length observation vectors exhibit certain properties. These results are used to quantify the performance limits in terms of upper and lower bounds on the probability of error in signal classification with M-length compressive measurements. When the signals of interest are sparse in the standard canonical basis, performance limits are derived in terms of lower bounds on the probability of error in classifying sparse signals with any classification rule.

58 citations


Authors

Showing all 3902 results

NameH-indexPapersCitations
Jeffrey G. Andrews11056263334
Zhu Han109140748725
Brian R. Flay8932526390
Jeffrey W. Elam8343524543
Pramod K. Varshney7989430834
Scott Fendorf7924421035
Gregory F. Ball7634221193
Yan Wang72125330710
David C. Dunand7252719212
Juan Carlos Diaz-Velez6433414252
Michael K. Lindell6218619865
Matthew J. Kohn6216413741
Maged Elkashlan6129414736
Bernard Yurke5824217897
Miguel Ferrer5847811560
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202370
2022210
2021763
2020695
2019620
2018637