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Institution

Oklahoma State University–Stillwater

EducationStillwater, Oklahoma, United States
About: Oklahoma State University–Stillwater is a education organization based out in Stillwater, Oklahoma, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 18267 authors who have published 36743 publications receiving 1107500 citations. The organization is also known as: Oklahoma State University & OKState.


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Journal ArticleDOI
TL;DR: A reliable and efficient multi-robot coordination algorithm to accomplish an area exploration task given that the communication range of each robot is limited is proposed, based on a distributed bidding model to coordinate the movement of multiple robots.

233 citations

Journal ArticleDOI
TL;DR: An innovative UAV-enabled MEC system involving the interactions among IoT devices, UAV, and edge clouds (ECs) and an efficient algorithm based on the successive convex approximation to obtain suboptimal solutions is proposed.
Abstract: Mobile edge computing (MEC) is an emerging technology to support resource-intensive yet delay-sensitive applications using small cloud-computing platforms deployed at the mobile network edges. However, the existing MEC techniques are not applicable to the situation where the number of mobile users increases explosively or the network facilities are sparely distributed. In view of this insufficiency, unmanned aerial vehicles (UAVs) have been employed to improve the connectivity of ground Internet of Things (IoT) devices due to their high altitude. This article proposes an innovative UAV-enabled MEC system involving the interactions among IoT devices, UAV, and edge clouds (ECs). The system deploys and operates a UAV properly to facilitate the MEC service provisioning to a set of IoT devices in regions where the existing ECs cannot be accessible to IoT devices due to terrestrial signal blockage or shadowing. The UAV and ECs in the system collaboratively provide MEC services to the IoT devices. For optimal service provisioning in this system, we formulate an optimization problem aiming at minimizing the weighted sum of the service delay of all IoT devices and UAV energy consumption by jointly optimizing UAV position, communication and computing resource allocation, and task splitting decisions. However, the resulting optimization problem is highly nonconvex and thus, difficult to solve optimally. To tackle this problem, we develop an efficient algorithm based on the successive convex approximation to obtain suboptimal solutions. Numerical experiments demonstrate that our proposed collaborative UAV-EC offloading scheme largely outperforms baseline schemes that solely rely on UAV or ECs for MEC in IoT.

232 citations

Journal ArticleDOI
TL;DR: Findings indicate that working memory deficits persist into adulthood and suggest that methodological variability may explicate why WM deficits have not been uniformly detected in previous experimental studies.
Abstract: Objective Within the last decade, working memory (WM) has garnered increased interest as a potential core deficit or endophenotype of attention-deficit/hyperactivity disorder (ADHD). The current study is the first meta-analytic review to examine several subject and task moderator variables' (e.g., percent female, diagnostic selection procedure, trials per set size, response demands, type of dependent variable, and central executive [CE] demands) effect on between-group phonological (PH) and visuospatial (VS) WM in adults with ADHD, relative to healthy controls. Method Literature searches were conducted using the PsycINFO, Web of Science, and PubMed databases, and yielded 38 studies of WM in adults with ADHD. Results Results revealed moderate-magnitude between-group effect sizes (ESs) across both WM domains. In addition, several task-moderating variables explained significant ES variability among PH and VS studies. Conclusions Collectively, these findings indicate that WM deficits persist into adulthood and suggest that methodological variability may explicate why WM deficits have not been uniformly detected in previous experimental studies.

231 citations

Journal ArticleDOI
TL;DR: This study identifies QTLs directly and indirectly affecting grain yield expression in 132 F12 recombinant inbred lines derived by single-seed descent from a cross between the Chinese facultative wheat Ning7840 and the US soft red winter wheat Clark.
Abstract: Grain yield and associated agronomic traits are important factors in wheat (Triticum aestivum L.) improvement. Knowledge regarding the number, genomic location, and effect of quantitative trait loci (QTL) would facilitate marker-assisted selection and the development of cultivars with desirable characteristics. Our objectives were to identify QTLs directly and indirectly affecting grain yield expression. A population of 132 F12 recombinant inbred lines (RILs) was derived by single-seed descent from a cross between the Chinese facultative wheat Ning7840 and the US soft red winter wheat Clark. Phenotypic data were collected for 15 yield and other agronomic traits in the RILs and parental lines from three locations in Oklahoma from 2001 to 2003. Twenty-nine linkage groups, consisting of 363 AFLP and 47 SSR markers, were identified. Using composite interval mapping (CIM) analysis, 10, 16, 30, and 14 QTLs were detected for yield, yield components, plant adaptation (shattering and lodging resistance, heading date, and plant height), and spike morphology traits, respectively. The QTL effects ranged from 7 to 23%. Marker alleles from Clark were associated with a positive effect for the majority of QTLs for yield and yield components, but gene dispersion was the rule rather than the exception for this RIL population. Often, QTLs were detected in proximal positions for different traits. Consistent, co-localized QTLs were identified in linkage groups 1AL, 1B, 4B, 5A, 6A, and 7A, and less consistent but unique QTLs were identified on 2BL, 2BS, 2DL, and 6B. Results of this study provide a benchmark for future efforts on QTL identification for yield traits.

231 citations

Journal ArticleDOI
Maria Dornelas1, Laura H. Antão1, Laura H. Antão2, Faye Moyes1  +283 moreInstitutions (130)
TL;DR: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time to enable users to calculate temporal trends in biodiversity within and amongst assemblage using a broad range of metrics.
Abstract: Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km(2) (158 cm(2)) to 100 km(2) (1,000,000,000,000 cm(2)).Time period and grainBio: TIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.

231 citations


Authors

Showing all 18403 results

NameH-indexPapersCitations
Gerald I. Shulman164579109520
James M. Tiedje150688102287
Robert J. Sternberg149106689193
Josh Moss139101989255
Brad Abbott137156698604
Itsuo Nakano135153997905
Luis M. Liz-Marzán13261661684
Flera Rizatdinova130124289525
Bernd Stelzer129120981931
Alexander Khanov129121987089
Dugan O'Neil128100080700
Michel Vetterli12890176064
Josu Cantero12684673616
Nicholas A. Kotov12357455210
Wei Chen122194689460
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202336
2022254
20211,902
20201,780
20191,633
20181,529