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Union Springs, NY, United States

Ronan A.D.,Cooper Union
Storm Surge Barriers to Protect New York City: Against the Deluge | Year: 2013

Freshman engineering students were required to design a system of moveable storm surge barriers for the New York metropolitan area. They first selected barrier locations by evaluating flooding potential, land use, and topography. Student teams then developed conceptual designs of barriers for four sites, focusing on conformance to local site geometry: (1) Verrazano Narrows barrier designs consist of sets of huge sliding vertical panels that are stored in chambers near each shore. (2) One Arthur Kill barrier design is a set of vertical plates that are mounted across the channel and can be pivoted 90 to block off flow. Other Arthur Kill design concepts are a fabric barrier and a pivoting pedestrian bridge. (3) Upper East River proposed barriers incorporate designs similar to those used in England and the Netherlands. (4) Rockaway Inlet student teams opted to expand the protected zone with much longer barriers to protect the south shores of Queens and Brooklyn; proposed barrier designs include submerged gates similar to those under construction in Venice, pivoting boardwalks along the shore communities, and a new bridge that also functions as a barrier when radial gates are closed. This design exercise taught the students that engineering design cannot focus on just technical issues - environmental, economic, social and political issues must also be addressed. © 2013 American Society of Civil Engineering. Source

Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 107.88K | Year: 2011

This project engages mechanical engineering (ME) students by exposing them to relevant real-world problems by making use of a new state-of-the art energy-efficient Leadership in Energy and Environmental Design (LEED) certified academic building by: (1) incorporating learning opportunities that integrate energy consumption and sustainability, and (2) developing hands-on process control laboratory experiments that supplement traditional classroom learning. The new experiments and inductive learning platforms are incorporated into an ongoing redesign of the ME program that creates a more cooperative and student-centric learning environment. The project will provide assessments of student learning outcomes that result from the new case-based and experiential learning approaches, and disseminate new curricular materials that will be modular and adaptable to a wide range of control systems curricula. A key outcome of this effort is the examination of the role of first-hand experiences and curricular improvements in attracting and retaining diverse students traditionally underrepresented in mechanical engineering.

Agency: Department of Defense | Branch: Navy | Program: STTR | Phase: Phase II | Award Amount: 746.48K | Year: 2011

Each tactical radio uses a particular waveform that inhibits it from freely communicating with a radio on another network. There is a need for a flexible communication gateway that supports interoperability, and can automatically translate among a set of waveforms to transfer information across networks. In response to the need for a flexible gateway, MaXentric and The Cooper Union have partnered to propose an extensible software framework that can generate a communications gateway to translate information across different networks. Phase I resulted in two major contributions for further research and development. First, a system concept for a rule-based wireless communications gateway was created that translates information based on a certain set of semantics. Second, a proof-of-concept software engine was produced that can configure a gateway for translation at the physical and data link layers. In Phase II, the team plans to integrate the gateway engine with a sophisticated, wide band RF receiver and transmitter for a complete prototype. This will involve using the XG Sensor, a sophisticated, extensively tested, and inexpensive wideband receiver produced by Rockwell Collins.

Agency: NSF | Branch: Standard Grant | Program: | Phase: INFO INTEGRATION & INFORMATICS | Award Amount: 30.64K | Year: 2014

The United States spends over 26 billion dollars per annum on the delivery and care of the 12-13% of infants who are born preterm. As preterm birth (PTB) is a major public health problem with profound implications on society, there would be extreme value in being able to identify women at risk of preterm birth during the course of their pregnancy. Previous predictive approaches have been largely unsuccessful since they have focused on a limited number of well described risk factors known to be correlated with preterm birth (e.g. prior preterm birth, race, and infection) and less on combining multiple factors. The latter approach is necessary to understand the complex etiologies of preterm birth. While identifying individual PTB risk factors has brought insight into the problem and has led in some cases to successful treatments such as progesterone for women with a previous preterm birth, this has only a limited impact on the overall frequency since many at risk patients, such as first time mothers (nulliparous), go untreated. Today, there is still no widely tested prediction system that combines well-known PTB factors and is clinically useful. There is, however, a global awareness of the need to discover and integrate the complex etiologies of prematurity in order to predict women at risk. Significant efforts have been made in the last couple of decades to collect large curated datasets of pregnant women. Previous studies on these datasets used relatively straightforward biostatitistical methodologies such as relative risk assessments to measure associations between factors and PTB. However, risk factors are studied independently of each other, which does not account for the multifactorial complexity of PTB. This exploratory project aims to investigate the value of more advanced machine learning methods by simultaneously considering all the factors, to develop better predictive methods.

The PTB data acquired in the context of this project brings together Electronic Health Records (EHRs) for mothers and their babies along with well-curated NIH data. The data is rich with structured clinical data and unstructured free text that require manual feature extraction. This project, largely motivated by the PTB problem, has two main goals:
(1) Improving the quality and aggregation the annotations for heterogeneous data. The researchers aim to capture socioeconomic, psychological and behavioral risk factors documented in the text of clinical notes via studying the process of manual feature extraction by human annotators. State-of-the-art methods either rely on the expertise of the annotator and/or the difficulty of the instance but ignore the variability in the quality of labeling over time due to fatigue, boredom, or knowledge. To improve the annotations, the project will develop a novel Bayesian framework for human labeling of unstructured data. The Bayesian model will embed a complete set of parameters including the prevalence of each class, difficulty of the instance and variability in the quality of annotation during the process. If the model construction is successful, then the developed framework will replace ad-hoc heuristics into a well-designed process for producing high quality annotations. This framework would allow extracting reliable features from the clinical text for subsequent analyses in devising PTB prediction models.
(2) Developing predictive models for multiple data spaces. To leverage all of the existing data, the project will investigate the value of using Vapniks paradigm of Learning Using Privileged Information (LUPI) in the context of preterm birth. Privileged information is a data that is available for training models but is not available for test examples. Data in this project come with two potential privileged information spaces namely the clinical notes and the space of future events. NICU data is an example of future event privileged information, which is only available for a subset of the examples (only premature babies requiring intensive care stay in the NICU). It has been shown that LUPI not only induces a better decision rule, it also increases the rate of convergence of the algorithm, hence requiring fewer training examples. This is a compelling property in the case of PTB prediction because of the rate of PTB. The project will extend LUPI into a powerful and applicable framework to handle the two spaces of privileged information, while developing spline-generating kernels, to manage LUPIs high computational cost. If successful, this proof-of-concept is expected to yield efficient and widely applicable LUPI algorithms in domains where privileged information is available, such as the financial domain and many other medical applications.
The developed software, publications and datasets resulting from this project will be made publicly available to the research community through the project website (http://www1.ccls.columbia.edu/~ansaf/CING/PTB/).

Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 15.00K | Year: 2013

PI: Lepek

This proposal will help subsidize travel-related expenses to enable U.S. researchers, particularly early-career researchers such as the PI, to attend and present their research at the Fluidization XIV conference (Fluidization XIV: From Fundamentals to Products) in Noordwijkerhout, The Netherlands (May 25-30, 2013). This proposal will support researchers from U.S. institutions that would normally not have the financial resources necessary to attend this international conference. In addition, this proposal will help support the travel costs of early-career researchers and researchers from traditionally underrepresented groups in the STEM fields that will be recruited to participate in this conference.

Fluidization is a very important field of both fundamental research and broad industrial applications. Currently, knowledge of complex fluid-particle interactions, particularly when coupled with multiphase transport phenomena and chemical reactions is still incomplete. This is primarily due to the challenges associated with coupling the solid properties of particles and their hydrodynamic behavior within a moving fluid medium. The addition of heat and mass transfer operations and also chemical reacting systems makes multiphase systems such as fluidized bed reactors a challenge to successfully design, scale-up, and operate. Although fluidized bed reactors are fairly common within the petrochemical industry, they can be found in other industries for a variety of applications such as coating and the design of new materials. Therefore, advances in fluidization research are necessary to help develop and ultimately commercialize new technologies such as CO2 capture (chemical looping), renewable energy systems, nanostructured materials, and the continuous processing of novel coated drug delivery systems. This conference will focus on advances in the field of fluidization research which impact the research and development, as well as the large-scale manufacturing of advanced materials that are common across a wide range of chemical process, life science, and food science industries.

This conference will bring together a wide range of scientists and engineers across multiple continents that are focused on fluidization research, which can have a major impact on multiple industries. By bringing together researchers who share a mutual interest in fluidization, it is expected that new international partnerships and collaborations will be established. This will be especially beneficial to the early-career researchers supported by this proposal that will have the unique opportunity to present their recent unpublished research findings in front of a congregation of established international researchers.

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