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Institution

Stevens Institute of Technology

EducationHoboken, New Jersey, United States
About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Cognitive radio & Wireless network. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.


Papers
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Journal ArticleDOI
TL;DR: A survey of current routing solutions is presented, where routing protocols for opportunistic networks are classified based on the network graph employed and the need to capture performance trade-offs from a multi-objective perspective is highlighted.
Abstract: This article examines the evolution of routing protocols for intermittently connected ad hoc networks and discusses the trend toward socialbased routing protocols. A survey of current routing solutions is presented, where routing protocols for opportunistic networks are classified based on the network graph employed. The need to capture performance trade-offs from a multi-objective perspective is highlighted.

80 citations

Journal ArticleDOI
TL;DR: The authors compare the predictive validity of these commonly-used wordlists to a wordlist developed specifically for the context of financial disclosure, using a sample of over 15,000 earnings press releases, and find that the context-specific wordlist is more powerful than the general wordlists used in past research.
Abstract: A growing stream of research in accounting and finance tests the extent to which the tone of financial disclosure narrative affects security prices, over and above the disclosed financial performance. These studies measure tone by counting the relative frequency of positive versus negative words in a given disclosure (e.g., earnings press releases). Critical to the analysis is the list of words deemed to be positive or negative. Most studies use general wordlists (GI or Diction) rather than wordlists that are specific to the domain of financial disclosure. General wordlists likely omit words that would be considered positive or negative in the context of financial disclosure and include words that would not. Application of general wordlists to financial disclosure also gives rise to problems with polysemy. For example, the word 'division' is considered a negative word in the GI wordlist, but that word is commonly used in financial disclosure to describe a segment of a company and is thus neither negative nor positive in a domain-specific context. In this study, we compare the predictive validity of these commonly-used wordlists to a wordlist developed specifically for the context of financial disclosure. Using a sample of over 15,000 earnings press releases, we find that the context-specific wordlist developed by Henry (2006, 2008) is more powerful than the general wordlists used in past research. Our findings suggest that capital markets researchers will benefit by using the domain-specific wordlist in the context of financial disclosure. These results will help to establish a firm foundation for research on qualitative information in financial disclosure.

80 citations

Journal ArticleDOI
TL;DR: In this article, the authors have incorporated nanofibers onto spiral-structured 3D scaffolds made of poly (epsilon-caprolactone) (PCL).
Abstract: Polymeric nanofiber matrices have already been widely used in tissue engineering. However, the fabrication of nanofibers into complex three-dimensional (3D) structures is restricted due to current manufacturing techniques. To overcome this limitation, we have incorporated nanofibers onto spiral-structured 3D scaffolds made of poly (epsilon-caprolactone) (PCL). The spiral structure with open geometries, large surface areas, and porosity will be helpful for improving nutrient transport and cell penetration into the scaffolds, which are otherwise limited in conventional tissue-engineered scaffolds for large bone defects repair. To investigate the effect of structure and fiber coating on the performance of the scaffolds, three groups of scaffolds including cylindrical PCL scaffolds, spiral PCL scaffolds (without fiber coating), and spiral-structured fibrous PCL scaffolds (with fiber coating) have been prepared. The morphology, porosity, and mechanical properties of the scaffolds have been characterized. Furthermore, human osteoblast cells are seeded on these scaffolds, and the cell attachment, proliferation, differentiation, and mineralized matrix deposition on the scaffolds are evaluated. The results indicated that the spiral scaffolds possess porosities within the range of human trabecular bone and an appropriate pore structure for cell growth, and significantly lower compressive modulus and strength than cylindrical scaffolds. When compared with the cylindrical scaffolds, the spiral-structured scaffolds demonstrated enhanced cell proliferation, differentiation, and mineralization and allowed better cellular growth and penetration. The incorporation of nanofibers onto spiral scaffolds further enhanced cell attachment, proliferation, and differentiation. These studies suggest that spiral-structured nanofibrous scaffolds may serve as promising alternatives for bone tissue engineering applications.

80 citations

Journal ArticleDOI
TL;DR: Results suggest that a weighted flow capacity rate, which accounts for both the contribution of an edge to maximum network flow and the extent to which the edge is a bottleneck in the network, shows most promise across four instances of varying network sizes and densities.

80 citations

Journal ArticleDOI
TL;DR: This paper provides a comprehensive survey on resource constrained routing and scheduling that unveils the problem characteristics with respect to resource qualifications, service requirements and problem objectives and identifies the most effective exact and heuristic algorithms for this class of problems.

80 citations


Authors

Showing all 5536 results

NameH-indexPapersCitations
Paul M. Thompson1832271146736
Roger Jones138998114061
Georgios B. Giannakis137132173517
Li-Jun Wan11363952128
Joel L. Lebowitz10175439713
David Smith10099442271
Derong Liu7760819399
Robert R. Clancy7729318882
Karl H. Schoenbach7549419923
Robert M. Gray7537139221
Jin Yu7448032123
Sheng Chen7168827847
Hui Wu7134719666
Amir H. Gandomi6737522192
Haibo He6648222370
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202342
2022139
2021765
2020820
2019799
2018563