Institution
Mississippi State University
Education•Starkville, Mississippi, United States•
About: Mississippi State University is a education organization based out in Starkville, Mississippi, United States. It is known for research contribution in the topics: Population & Catfish. The organization has 14115 authors who have published 28594 publications receiving 700030 citations. The organization is also known as: The Mississippi State University of Agriculture and Applied Science & Mississippi State University of Agriculture and Applied Science.
Papers published on a yearly basis
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TL;DR: A new method is introduced for synthesizing kinematic relationships for a general class of continuous backbone, or continuum, robots that enable real-time task and shape control by relating workspace (Cartesian) coordinates to actuator inputs, such as tendon lengths or pneumatic pressures, via robot shape coordinates.
Abstract: We introduce a new method for synthesizing kinematic relationships for a general class of continuous backbone, or continuum , robots. The resulting kinematics enable real-time task and shape control by relating workspace (Cartesian) coordinates to actuator inputs, such as tendon lengths or pneumatic pressures, via robot shape coordinates. This novel approach, which carefully considers physical manipulator constraints, avoids artifacts of simplifying assumptions associated with previous approaches, such as the need to fit the resulting solutions to the physical robot. It is applicable to a wide class of existing continuum robots and models extension, as well as bending, of individual sections. In addition, this approach produces correct results for orientation, in contrast to some previously published approaches. Results of real-time implementations on two types of spatial multisection continuum manipulators are reported.
780 citations
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TL;DR: This paper reviewed the development of the family firm context and identified its dimensions and explored the nomological relationships of the construct based on a social capital theory perspective and offer a theory of familiness.
Abstract: In the search for ways in which the family firm context is unique to organizational science, the construct of “familiness” has been identified and defined as resources and capabilities that are unique to the family’s involvement and interactions in the business. While identification and isolation of a construct unique to family firms is both groundbreaking and important for family firm research, it is also important that the development of the construct continues to be examined from complementing theoretical viewpoints. As such, we set out to review the development of the familiness construct and identify its dimensions. We also explore the nomological relationships of the construct based on a social capital theory perspective and offer a theory of familiness.
764 citations
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University of Saskatchewan1, Natural History Museum2, Centre for Environment, Fisheries and Aquaculture Science3, University of Rhode Island4, Sewanee: The University of the South5, Academy of Sciences of the Czech Republic6, National Institutes of Health7, Saint Petersburg State University8, University of Salzburg9, Centre national de la recherche scientifique10, Mississippi State University11, Science for Life Laboratory12, Uppsala University13, Charles University in Prague14, Spanish National Research Council15, Kaiserslautern University of Technology16, University of Duisburg-Essen17, University of Oslo18, Dalhousie University19, Pierre-and-Marie-Curie University20, American Museum of Natural History21, University of Michigan22, University of Warsaw23, University of São Paulo24, University of Paris25, University of British Columbia26, University of Guelph27, Royal Botanic Garden Edinburgh28, Kyungpook National University29, University of Geneva30, University of Alabama31, Pompeu Fabra University32, Edinburgh Napier University33, University of Arkansas34, Hosei University35, Oklahoma State University–Stillwater36, Chinese Academy of Sciences37
TL;DR: It is confirmed that eukaryotes form at least two domains, the loss of monophyly in the Excavata, robust support for the Haptista and Cryptista, and suggested primer sets for DNA sequences from environmental samples that are effective for each clade are provided.
Abstract: This revision of the classification of eukaryotes follows that of Adl et al., 2012 [J. Euk. Microbiol. 59(5)] and retains an emphasis on protists. Changes since have improved the resolution of many ...
750 citations
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TL;DR: The discovery of the GW150914 with the Advanced LIGO detectors provides the first observational evidence for the existence of binary black-hole systems that inspiral and merge within the age of the Universe as mentioned in this paper.
Abstract: The discovery of the gravitational-wave source GW150914 with the Advanced LIGO detectors provides the first observational evidence for the existence of binary black-hole systems that inspiral and merge within the age of the Universe. Such black-hole mergers have been predicted in two main types of formation models, involving isolated binaries in galactic fields or dynamical interactions in young and old dense stellar environments. The measured masses robustly demonstrate that relatively "heavy" black holes (≳25M⊙) can form in nature. This discovery implies relatively weak massive-star winds and thus the formation of GW150914 in an environment with metallicity lower than ∼1/2 of the solar value. The rate of binary black-hole mergers inferred from the observation of GW150914 is consistent with the higher end of rate predictions (≳1Gpc−3yr−1) from both types of formation models. The low measured redshift (z∼0.1) of GW150914 and the low inferred metallicity of the stellar progenitor imply either binary black-hole formation in a low-mass galaxy in the local Universe and a prompt merger, or formation at high redshift with a time delay between formation and merger of several Gyr. This discovery motivates further studies of binary-black-hole formation astrophysics. It also has implications for future detections and studies by Advanced LIGO and Advanced Virgo, and gravitational-wave detectors in space.
742 citations
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TL;DR: In this paper, the authors provide an overview of the mechanical characteristics and behavior of metallic parts fabricated via direct laser deposition (DLD), while also discussing methods to optimize and control the DLD process.
Abstract: The mechanical behavior, and thus ‘trustworthiness’/durability, of engineering components fabricated via laser-based additive manufacturing (LBAM) is still not well understood. This is adversely affecting the continual adoption of LBAM for part fabrication/repair within the global industry at large. Hence, it is important to determine the mechanical properties of parts fabricated via LBAM as to predict their performance while in service. This article is part of two-part series that provides an overview of Direct Laser Deposition (DLD) for additive manufacturing (AM) of functional parts. The first part (Part I) provides a general overview of the thermo-fluid physics inherent to the DLD process. The objective of this current article (Part II) is to provide an overview of the mechanical characteristics and behavior of metallic parts fabricated via DLD, while also discussing methods to optimize and control the DLD process. Topics to be discussed include part microstructure, tensile properties, fatigue behavior and residual stress – specifically with their relation to DLD and post-DLD process parameters (e.g. heat treatment, machining). Methods for controlling/optimizing the DLD process for targeted part design will be discussed – with an emphasis on monitored part temperature and/or melt pool morphology. Some future challenges for advancing the knowledge in AM-part adoption are discussed. Despite various research efforts into DLD characteristics and process optimization, it is clear that there are still many areas that require further investigation.
737 citations
Authors
Showing all 14277 results
Name | H-index | Papers | Citations |
---|---|---|---|
Naomi J. Halas | 140 | 435 | 82040 |
Bin Liu | 138 | 2181 | 87085 |
Shuai Liu | 129 | 1095 | 80823 |
Vijay P. Singh | 106 | 1699 | 55831 |
Liangpei Zhang | 97 | 839 | 35163 |
K. L. Dooley | 95 | 320 | 63579 |
Feng Chen | 95 | 2138 | 53881 |
Marco Cavaglia | 93 | 372 | 60157 |
Tuan Vo-Dinh | 86 | 698 | 24690 |
Nicholas H. Barton | 84 | 267 | 32707 |
S. Kandhasamy | 81 | 235 | 50363 |
Michael S. Sacks | 80 | 386 | 20510 |
Dinesh Mohan | 79 | 283 | 35775 |
James Mallet | 78 | 209 | 21349 |
George D. Kuh | 77 | 248 | 30346 |