The Economic Impact Of Broadband: Estimates From A Regional Input-Output Model

Like good roads, schools, and hospitals, cutting-edge broadband infrastructure is crucial to economic development and to the quality of life of local communities. Second-generation broadband (SGB), capable of supporting video, voice and data services simultaneously over a fiber-optic infrastructure, can provide users not merely faster internet connectivity, but a whole array of applications and communication services. This study provides an approach to quantifying the economic effects of first and second generation broadband availability in Hamilton County (TN) using an IMPLAN model. We find that household broadband expenditures over the period 2001-2005 supported 548 jobs and contributed $109.8 million in income and taxes to Hamilton County. Further, we estimate that while a new fiber-to-the-home project would cost$195.5 million over ten years, the economic impact of such a project would result in income and taxes exceeding $352 million while creating over 2,600 new jobs. We conclude that Hamilton County would benefit from the adoption of this technology

A new set of relativistic screening constants for the screened hydrogenic model

AnewRelativisticScreenedHydrogenicModel has been developed to calculate atomic data needed to compute the optical and thermodynamic properties of high energy density plasmas. The model is based on anewset of universal screeningconstants, including nlj-splitting that has been obtained by fitting to a large database of ionization potentials and excitation energies. This database was built with energies compiled from the National Institute of Standards and Technology (NIST) database of experimental atomic energy levels, and energies calculated with the Flexible Atomic Code (FAC). The screeningconstants have been computed up to the 5p3/2 subshell using a Genetic Algorithm technique with an objective function designed to minimize both the relative error and the maximum error. To select the best set of screeningconstants some additional physical criteria has been applied, which are based on the reproduction of the filling order of the shells and on obtaining the best ground state configuration. A statistical error analysis has been performed to test the model, which indicated that approximately 88% of the data lie within a ±10% error interval. We validate the model by comparing the results with ionization energies, transition energies, and wave functions computed using sophisticated self-consistent codes and experimental data

Object-Oriented Programming : An Evolutionary Approach

X, 270 tr.; 23 cm

Hidden Bridges of Collaborative Growth in Retention within STEM Disciplines

In 2014, the University of North Georgia committed to increasing opportunity for student / faculty participation by investing in the High Impact Practice of Undergraduate Research. Simultaneously, faculty development, undergraduate research, academic engagement, grant development and nationally competitive scholarships were consolidated into the Office of Research and Engagement. The synergy formed by bridging the touch-points between students, faculty, administration, and external opportunity within ORE led to new opportunities for student engagement. Ignited by a $1.3M five-year UNG Presidential Incentive Awards program, UNG increased nationally competitive scholarship awards, student faculty research opportunities, and competitive external awards from$1M / yr to $6 M in FY’18. Join us as we discuss the hidden bridges that can be tapped within our institutions to bridge opportunities for STEM students

Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision

Port State Control (PSC) inspections have been implemented as an administrative measure to detect and detain substandard ships and thus to ensure maritime safety. Advanced risk models were developed to investigate the impact of factors influencing ship detention. Although showing much attractiveness, current studies still reveal a key challenge on how such analysis can improve the ship performance in PSC inspections and aid PSC detention risk control decision. By incorporating a data-driven Bayesian network (BN) into the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, this paper proposes a new ship detention risk control methodology, in which the decision criteria are generated from the root risk variables, and the alternatives refer to the established strategies adopted by ship-owners in their practical ship detention risk control. Along with the new methodology, the main technical novelty of this paper lies in the quantitative measurement of the effectiveness of each strategy in terms of the reduction of detention rate in a dynamic manner. Its practical contributions are seen, from both ship owner and port authority perspectives, through the provisions of useful insights on dynamic evaluation of rational control strategies to reduce ship detention risk under various PSC inspection scenarios