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.

A hybrid twin screw extrusion/electrospinning method to process nanoparticle-incorporated electrospun nanofibres

Abstract: A new hybrid methodology that fully integrates the processing capabilities of the twin screw extrusion process (conveying solids, melting, dispersive and distributive mixing, pressurization, temperature profiling, devolatilization) with electrospinning is described. The hybrid process is especially suited to the dispersion of nanoparticles into polymeric binders and the generation of nanoparticle-incorporated fibres and nanofibres. The new technology base is demonstrated with the dispersion of β-tricalcium phosphate (β-TCP) nanoparticles into poly(e-caprolactone) (PCL) to generate biodegradable non-woven meshes that can be targeted as scaffolds for tissue engineering applications. The new hybrid method yielded fibre diameters in the range of 200-2000 nm for both PCL and β-TCP/PCL (35% by weight) composite scaffolds. The degree of crystallinity of polycaprolactone meshes could be manipulated in the 35.1-41% range, using the voltage strength as a parameter. The electrospinning process, integrated with dispersive kneading disc elements, facilitated the decrease of the cluster sizes and allowed the continuous compounding of the nanoparticles into the biodegradable polymer prior to electrospinning. Thermogravimetric analysis (TGA) of the non-woven meshes validated the continuous incorporation of 35 ± 1.5% (by weight) β-TCP nanoparticles for a targeted concentration of 35%. Uniaxial tensile testing of the meshes with and without the nanoparticles indicated that the ultimate tensile strength at break of the meshes increased from 0.47 ± 0.04 to 0.79 ± 0.08 MPa upon the incorporation of the β-TCP nanoparticles. This demonstration study suggests that the new technology base is particularly suitable for the concomitant dispersion and electrospinning of nanoparticles in the generation of myriad types of functional nanofibres.

Empirical Study of Value-at-Risk and Expected Shortfall Modelswith Heavy Tails

Abstract: Expected Shortfall (ES) has been proposed as an alternative, almost coherent, risk measure to Value-at-Risk (VaR), as it considers expected loss beyond VaR. In this paper, we compare the performance of different models in estimating VaR and ES using historical data. Daily returns of popular indices (S&P500, DAX, CAC, Nikkei, TSE, and FTSE) and currencies (US dollar vs. Euro, Yen, Pound, and Canadian dollar) for over ten years are modeled with Empirical (or Historical), Gaussian, Generalized Pareto (Peak Over Threshold (POT) technique of Extreme Value Theory (EVT)) and Stable Paretian distribution (both symmetric and non-symmetric). Our backtesting results support the assumption of fat-tailed distributions of asset returns. Several computational issues and effects of factors, i.e. rolling window size and confidence level, are also addressed.

Percolation in magnetic composites

Abstract: Electric and magnetic properties of composite materials consisting of low density polyethylene filled with powdered ferromagnetic materials were investigated. The volume fractions of the fillers were varied from 10% up to the theoretical maximum packing fractions, i.e. between 0.70 and 0.77, so that the percolation phenomenon could be investigated. The ferromagnetic fillers used were HyMu 800 (a nickel-iron-molybdenum alloy), MnZn ferrite and NiZn ferrite. The particle sizes and size distributions of the fillers were well characterized by image analysis techniques. Based on the particle size distribution the maximum loading levels of fillers as permitted by geometric considerations were calculated. The properties of the composites characterized included: volume and surface resistivities, dielectric constants, electrical loss factors and magnetic permeabilities.

Detecting and parsing architecture at city scale from range data

Abstract: We present a method for detecting and parsing buildings from unorganized 3D point clouds into a compact, hierarchical representation that is useful for high-level tasks. The input is a set of range measurements that cover large-scale urban environment. The desired output is a set of parse trees, such that each tree represents a semantic decomposition of a building – the nodes are roof surfaces as well as volumetric parts inferred from the observable surfaces. We model the above problem using a simple and generic grammar and use an efficient dependency parsing algorithm to generate the desired semantic description. We show how to learn the parameters of this simple grammar in order to produce correct parses of complex structures. We are able to apply our model on large point clouds and parse an entire city.

Localized sources of knowledge and the effect of knowledge spillovers: an empirical study of inventors in the telecommunications industry

Abstract: Knowledge spillovers have been considered a major driver for the increased rate of innovation in technological clusters. In this study, we respond to some recent calls for a deeper understanding of the mechanisms of localized knowledge exchange, by focusing on the effect of local sources of knowledge on inventors in telecommunication clusters. We differentiate between the different types of knowledge in our model for knowledge accessibility, and present a framework for knowledge exchange within clusters. We empirically test this model on inventors across the USA, where we compare the responses of inventors in clusters to inventors who are not in clusters. The results highlight the significance of the ‘local buzz’, and the dynamics of knowledge spillovers are explored.