Stevens Institute of Technology

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.

The spanning subgraphs of eulerian graphs

Abstract: It is shown that a connected graph G spans an eulerian graph if and only if G is not spanned by an odd complete bigraph K(2m + 1, 2n + 1). A disconnected graph spans an eulerian graph if and only if it is not the union of the trivial graph with a complete graph of odd order. Exact formulas are obtained for the number of lines which must be added to such graphs in order to get eulerian graphs.

On the existence of uniformly optimally reliable networks

Abstract: A well-known model for network reliability studies consists of an undirected graph with perfectly reliable nodes and equal and independent edge failure probabilities. The measure of reliability is then defined to be the probability that the graph is connected. A well-defined synthesis problem is to find the graph that minimizes the failure probability given the number of nodes n, the number of edges e, and the edge failure rate ρ. In this work, we consider the possibility of the existence of a fixed graph that is optimal for all possible ρ. It is simple to verify that such graphs exist when e = n − 1, or n. Herein, we show that they also exist when e = n + 1 and n + 2.

Horizontal Allocation of Decision Rights for On-Premise Applications and Software-as-a-Service

Abstract: This study addresses a major gap in our knowledge about the allocation of information technology (IT) decision rights between business and IT units at the application level, including the governance of applications delivered on-premise versus those delivered with a software-as-a-service (SaaS) model. Building on the findings from a multicase qualitative study of organizations that had adopted the same SaaS application, we draw on three theoretical lenses (agency theory, transaction cost economics, and knowledge-based view) to develop a theoretically grounded model with three organization-level factors, three application-level factors, and application-level IT governance. Hypotheses derived from the model, as well as a set of differential hypotheses about factor influences due to on-premise versus SaaS delivery, are tested with survey responses from 207 firms in which application-level governance is operationalized with two dimensions: decision control rights (decision authority) and decision management ri...

Protein Surface Patterning Using Nanoscale PEG Hydrogels

Abstract: We have used focused electron-beam cross-linking to create nanosized hydrogels and thus present a new method with which to bring the attractive biocompatibility associated with macroscopic hydrogels into the submicron length-scale regime. Using amine-terminated poly(ethylene glycol) thin films on silicon substrates, we generate nanohydrogels with lateral dimensions of order 200 nm which can swell by a factor of at least five, depending on the radiative dose. With the focused electron beam, high-density arrays of such nanohydrogels can be flexibly patterned onto silicon surfaces. Significantly, the amine groups remain functional after e-beam exposure, and we show that they can be used to covalently bind proteins and other molecules. We use bovine serum albumin to amplify the number of amine groups, and we further demonstrate that different proteins can be covalently bound to different hydrogel pads on the same substrate to create multifunctional surfaces useful in emerging bio/proteomic and sensor technologies.

A survey of big data research

Abstract: Big data create values for business and research, but pose significant challenges in terms of networking, storage, management, analytics, and ethics. Multidisciplinary collaborations from engineers, computer scientists, statisticians, and social scientists are needed to tackle, discover, and understand big data. This survey presents an overview of big data initiatives, technologies, and research in industries and academia, and discusses challenges and potential solutions.