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New York City, NY, United States

Manhattan College is a private, independent, Roman Catholic, liberal arts college located in the Bronx, New York City, United States. After originally being established in 1853 by the Brothers of the Christian Schools as an academy for day students, Manhattan College was officially incorporated as an institution of higher education through a charter granted by the New York State Board of Regents. In 1922, the College moved from Manhattan to the Riverdale section of the Bronx, roughly 10 miles north of Midtown. Manhattan College offers undergraduate programs in the arts, business, education, health, engineering, and science. Graduate programs are offered for education, business, and engineering. U.S. News and World Report lists Manhattan as one of the top 20 colleges in the Regional Universities North category. In addition, Manhattan consistently ranks high in surveys that evaluate return on investment. In the 2014-2015 Payscale.com College Return on Investment survey, Manhattan placed 15th nationally Wikipedia.


Boliari N.,Manhattan College
IEEE Communications Magazine | Year: 2016

Broadband-technology-based critical communications networks are predicted and known to generate significant direct returns. Less attention is given to their potential to generate long term indirect returns in the form of various socioeconomic benefits. This article focuses on identifying such indirect returns, and suggests that they are included in financial and economic viability analyses for better accuracy in the cost-benefit analyses of broadband critical communications networks. Developing a net present value model that accounts for both direct and indirect benefits, the article suggests that broadband critical communications networks can be highly beneficial and desirable to implement, particularly from the public and social points of view. © 2016 IEEE. Source


Farley K.J.,Manhattan College | Meyer J.S.,Arcadis
Environmental Toxicology and Chemistry | Year: 2015

A comparison of 4 metal mixture toxicity models (that were based on the biotic ligand model [BLM] and the Windermere humic aqueous model using the toxicity function [WHAM-FTOX]) was presented in a previous paper. In the present study, a streamlined version of the 4 models was developed and applied to multiple data sets and test conditions to examine key assumptions and calibration strategies that are crucial in modeling metal mixture toxicity. Results show that 1) a single binding site on or in the organism was a useful and oftentimes sufficient framework for predicting metal toxicity; 2) a linear free energy relationship (LFER) for bidentate binding of metals and cations to the biotic ligand provided a good first estimate of binding coefficients; 3) although adjustments in metal binding coefficients or adjustments in chemical potency factors can both be used in model calibration for single-metal exposures, changing metal binding coefficients or chemical potency factors had different effects on model predictions for metal mixtures; and 4) selection of a mixture toxicity model (based on concentration addition or independent action) was important in predicting metal mixture toxicity. Moving forward, efforts should focus on reducing uncertainties in model calibration, including development of better methods to characterize metal binding to toxicologically active binding sites, conducting targeted exposure studies to advance the understanding of metal mixture toxicity, and further developing LFERs and other tools to help constrain the model calibration. © 2014 SETAC. Source


De A.,Manhattan College
Computers and Geotechnics | Year: 2012

Numerical modeling of the effects of explosions relies on suitable material models appropriate for large deformation problems. Available results of a wide range of static and dynamic tests on Nevada #120 sand, completed as part of an earlier project (VELACS), were utilized to calibrate a numerical model for sand, suitable for modeling surface explosions. A fully-coupled Euler-Lagrange Interaction was utilized to correctly model pressures created by the explosion simultaneously with the large deformations in the soil. The model was used to study two cases - the first with a 2-D axisymmetric case of crater formation; and the second with a 3-D case of surface explosion above an underground tunnel. The results of numerical analyses were found to closely match those from other analyses, field tests, and centrifuge model tests. © 2012 Elsevier Ltd. Source


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: Core R&D Programs | Award Amount: 719.11K | Year: 2015

The EHR Core Research Program funds proposals that will help synthesize, build and/or expand research foundations in the following areas of STEM (Science, Technology, Engineering, and Mathematics) Education: STEM learning, STEM learning environments, STEM workforce development, and broadening participation in STEM. The STEM education pipeline narrows significantly in college. Community colleges serve some of the most diverse audiences, and are increasingly using online learning as a cheaper way to provide STEM instruction; additionally Massive Open Online Courses (commonly known as MOOCs) are being proposed as alternatives to credit-bearing instruction. Prior research shows that online learning environments impact different kinds of students differently. This research project based at a community college asks questions such as the following: Is this move towards STEM learning online at the community college level likely to impact underrepresented groups more than others, and will it have positive or negative impact? Can we identify which students are best served by online vs. face-to-face instruction or conduct interventions for students at-risk in the online environment? This project aims to answer these questions by using two important datasets: one is a dataset to be assembled from six schools in the CUNY (City University of New York) system, which serves one of the most diverse student bodies in the country, and in which over 50,000 students have taken STEM courses online. The second is a large-scale national dataset from the National Center for Education Statistics which contains demographic, academic, personal, and financial variables.

Only a small proportion of the research conducted on online learning has controlled for student self-selection into online courses in a rigorous way. This study will explore the extent to which students with particular characteristics fare better or more poorly not only in online STEM courses, but in college afterwards, with a matched comparison to students who take comparable face-to-face STEM courses. The project uses mixed methods. Quantitative analysis will include principal component factor analysis, logistic regression, linear regression, analysis of variance and covariance, generalized linear mixed models, propensity score matching, and sensitivity analysis to examine course and college outcomes including course retention (attendance through the end of the tenth week of classes) and successful course completion (earning a C- or better in the course), whether students re-enrolled in the semester immediately following the course, and persistence at one, two, three, and six years. Overall grade point average, the number of credits accumulated, and transfer and graduation rates at these intervals will also be used. Independent variables and covariates to be modeled include online vs. hybrid vs. offline STEM course format, and a variety of demographic variables including effort capital, social capital, cultural capital, financial capital, human capital, and habitus. Qualitative interviews and in-depth surveys will be used to explore the trends found in the large scale datasets, and a survey will be conducted specifically with online instructors in the CUNY system. Data will be explored to model what variables contribute to differential risk online. The intellectual merit of the project rests on advancing our understanding of how online options differentially help or hinder different kinds of postsecondary STEM students. For broader impacts, the results of the model could be used as the basis for implementation of targeted interventions, either by providing at-risk students with additional mentoring, tutoring, technical support, advisement, or training in skills and behaviors necessary to succeed in an online course; or by advising them to enroll in a comparable face-to-face course instead. These policy implications will be discussed at a culminating one-day conference on elearning hosted by the project.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: ROBERT NOYCE SCHOLARSHIP PGM | Award Amount: 297.52K | Year: 2015

This Robert Noyce Teacher Scholarship capacity building project will investigate ways to promote and support engineering education for three groups of learners: i) engineers and engineering students, ii) students in middle and high schools - especially from underrepresented groups, and iii) current math and science majors pursuing teacher certification, who desire to be prepared to teach engineering principles to students in grades 6-12. Currently, there is little engineering in many teacher certification programs for individuals interested in becoming a STEM teacher; but the need has changed with the roll-out of the Next Generation Science Standards. Teachers are most often certified to teach math, science, or technology, but rarely have sufficient background to adequately teach engineering principles. Engineers, on the other hand, have expertise in engineering principles, but lack the knowledge and skills to effectively teach students.

To address the increasing demand for teachers qualified to teach the engineering concepts included in the Next Generation Science Standards, this project will produce skilled STEM educators through three newly developed programs; first a minor in education for students studying engineering. Second, a certificate in engineering education for STEM majors who are pursuing teacher certification, and third, a post-baccalaureate certificate in engineering education for engineering graduate students. The project will also provide professional development opportunities for current STEM educators. Finally, the project will select groups of university students to be trained to present hands-on workshops in local schools serving underrepresented groups with the intent of enticing these students to consider future studies in STEM related fields. The project will contribute to a transformative change in STEM educator preparation, while providing outreach services to high need schools and attracting these students to STEM fields.

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