Materials & mechanics problem-based learning project for undergraduates

Materials & Mechanics is part of the core courses of the Madison Engineering curriculum that is an integrated subject course taken in the junior year at James Madison University. The emphasis of the course is to provide a working foundation for exploring the governing principles of materials science and the mechanics of materials. The course fuses the theory and application of the mechanics of materials within science-led and design-led approaches to develop a plan for materials selection. A semester long team-based project that attempts to underscore the interconnectedness of structure, properties, processing, and performance of materials in products has been developed. Each team is assigned a material family to select a material candidate for use as part of a shelf system for the home market. The focus of the discussion will be the semester long, team-based, experiential, open-ended, and nondirected project that was motivated by providing Madison Engineering students with an “industrial new hire” type of experience. There are modules that focus on aspects of the project but the entire project encompasses the translation of customer desires into functional attributes for the purpose of selecting materials that will yield a valued and sustainable final product. Students must span the design space to understand how materials have been used in the past for this particular application to creating draft drawings and calculations depicting the load that will be experienced by each component of the system to physical material testing and analysis that will allow them to produce a scaled version of a conceptual prototype

Effective dose estimation for U.S. Army soldiers undergoing multiple computed tomography scans

Diagnosing the severity of blunt trauma injuries is difficult and involves the use of diagnostic radiological scanning. The primary diagnostic radiology modality used for assessing these injuries is computed tomography (CT). CT delivers more radiation dose than other diagnostic scanning modalities. Trauma patients are at an increased risk of radiation induced cancer because of the cumulative dose effects from multiple scanning procedures. Current methods for estimating effective dose, the quantity used to describe the whole body health detriment from radiation, involves the use of published conversion coefficients and procedure specific machine parameters such as dose-length-product based on computed tomography dose index and scan length. Other methods include the use of Monte Carlo simulations based upon the specific machine geometry and radiation source. Unless the requisite machine information is known, the only means of estimating the effective dose is through the use of generic estimates that are published by scientific radiation committees and have a wide range of values. This research addressed a knowledge gap in assigning effective doses from computed tomography when machine parameters knowledge is either unknown or incomplete. The research involved the development of a new method of estimating the effective dose from CT through the use of regression models incorporating the use of patient parameters as opposed to machine specific parameters. This new method was experimentally verified using two adult anthropomorphic phantoms and optically stimulated luminescent dosimeters. The new method was then compared against a real patient population undergoing similar computed tomography scanning procedures. Utilizing statistical procedures, the new method was tested for repeatability and bias against the current conversion coefficient method. The analysis of the new method verifies that the estimation ability is similar to recent research indicating that the older conversion coefficient methods can underestimate the effective dose to the patient by up to 40%. The new method can be used as a retrospective tool for effective dose estimation from CT trauma protocols for a patient population with physical characteristics similar to the U.S. Army Soldier population

Estimates of indirect land use change from biofuels based on historical data

ILUC emissions from biofuels are commonly estimated with sophisticated economic models of world agriculture. Because these are often complex, the JRC in collaboration with Overmars and PBL has evaluated and developed an alternative approach base on “historical” data. This approach gives simple and transparent estimates of ILUC emissions in recent years, even if the method is less rigorous in principle than estimates based on sophisticated economic models. ILUC emissions calculated by a methodology using historical data are generally in line with those of economic models, showing a lower impact of cereals and sugar crops compared to vegetable oils.JRC.F.8-Sustainable Transpor

Evidence for Innate and Adaptive Immune Responses in a Cohort of Intractable Pediatric Epilepsy Surgery Patients.

Brain-infiltrating lymphocytes (BILs) were isolated from resected brain tissue from 10 pediatric epilepsy patients who had undergone surgery for Hemimegalencephaly (HME) (n = 1), Tuberous sclerosis complex (TSC) (n = 2), Focal cortical dysplasia (FCD) (n = 4), and Rasmussen encephalitis (RE) (n = 3). Peripheral blood mononuclear cells (PBMCs) were also isolated from blood collected at the time of the surgery. Cells were immunostained with a panel of 20 antibody markers, and analyzed by mass cytometry. To identify and quantify the immune cell types in the samples, an unbiased clustering method was applied to the entire data set. More than 85 percent of the CD45+ cells isolated from resected RE brain tissue comprised T cells; by contrast NK cells and myeloid cells constituted 80-95 percent of the CD45+ cells isolated from the TSC and the FCD brain specimens. Three populations of myeloid cells made up >50 percent of all of the myeloid cells in all of the samples of which a population of HLA-DR+ CD11b+ CD4- cells comprised the vast majority of myeloid cells in the BIL fractions from the FCD and TSC cases. CD45RA+ HLA-DR- CD11b+ CD16+ NK cells constituted the major population of NK cells in the blood from all of the cases. This subset also comprised the majority of NK cells in BILs from the resected RE and HME brain tissue, whereas NK cells defined as CD45RA- HLA-DR+ CD11b- CD16- cells comprised 86-96 percent of the NK cells isolated from the FCD and TSC brain tissue. Thirteen different subsets of CD4 and CD8 αβ T cells and γδ T cells accounted for over 80% of the CD3+ T cells in all of the BIL and PBMC samples. At least 90 percent of the T cells in the RE BILs, 80 percent of the T cells in the HME BILs and 40-66 percent in the TSC and FCD BILs comprised activated antigen-experienced (CD45RO+ HLA-DR+ CD69+) T cells. We conclude that even in cases where there is no evidence for an infection or an immune disorder, activated peripheral immune cells may be present in epileptogenic areas of the brain, possibly in response to seizure-driven brain inflammation

Experimental studies on a two-step fast pyrolysis-catalytic hydrotreatment process for hydrocarbons from microalgae (Nannochloropsis gaditana and Scenedesmus almeriensis)

Two microalgae species (marine Nannochloropsis gaditana, and freshwater Scenedesmus almeriensis) were subjected to pyrolysis followed by a catalytic hydrotreatment of the liquid products with the objective to obtain liquid products enriched in hydrocarbons. Pre-dried microalgae were pyrolyzed in a mechanically stirred fluidized bed reactor (380 and 480 degrees C) with fractional condensation. The heavy phase pyrolysis oils were hydrotreated (350 degrees C and 15 MPa of H-2 pressure for 4 h) using a NiMo on alumina catalyst. The pyrolysis liquids after pyrolysis and those after catalytic hydrotreatment were analyzed in detail using GC-MS, GC x GC-MS, and 2D HSQC NMR. The liquid products are enriched in aromatics and aliphatic hydrocarbons and, as such have a considerably lower oxygen content (1.6-4.2% w/w) compared to the microalgae feeds (25-30% w/w). The overall carbon yield for the liquid products was between 15.6 and 19.1% w/w based on the initial carbon content of the algae feedstock

Modeling Megacity Medical System Response to a CBRNE Event

The collaborative effectiveness of the public health system (PHS) and the Army Medical Department (AMEDD) is limited in the case of a 10-kiloton (kt) nuclear event on a megacity due to an overall lack of knowledge and understanding among agencies. This study details an exhaustive analysis of the current medical response system using New York City as a case study. Through the problem definition phase of the Systems Decision Process (SDP), this report identifies operational gaps existing at different levels within the system. Identified operational gaps existed at the local, state, and federal levels in the areas of resources, communication, and planning within the following agencies: Sloan Kettering Memorial Hospital, the Office of Emergency Management (OEM), the Federal Emergency Management Agency (FEMA), Health and Human Services (HHS), and the United States Department of Veteran Affairs (VA). Evaluation of the operational gaps illustrated the areas which were most vulnerable. The current analysis suggests that the system in place requires adjustments of the identified gaps so that maximum efficiency can be achieved

Food Security Network Modeling

Food security creates a complex issue for American interests. Within a constantly expanding operational environment, food security remains a vital lifeline both domestically and abroad.  Current methods of mapping an area’s food system rely on ad-hoc assessments that produce skewed results and minimal metric analysis. Previous assessments methodologies failed to incorporate components of a food system that influences the overall stability of an area. The research conducted utilized the Systems Decision Process (SDP) to create a value hierarchy and model that provide an assessment for an areas food system. The findings from the research showcase that a food system relies on several variables such as infrastructure, dietary needs, and the national stability of a region. A more enhanced assessment model was developed that placed an overarching value to a food network that allows ground commanders to gain a holistic overview of the condition of an areas food system

How to Translate Time: The Temporal Aspects of Rodent and Human Pathobiological Processes in Traumatic Brain Injury.

Traumatic brain injury (TBI) triggers multiple pathobiological responses with differing onsets, magnitudes, and durations. Identifying the therapeutic window of individual pathologies is critical for successful pharmacological treatment. Dozens of experimental pharmacotherapies have been successfully tested in rodent models, yet all of them (to date) have failed in clinical trials. The differing time scales of rodent and human biological and pathological processes may have contributed to these failures. We compared rodent versus human time scales of TBI-induced changes in cerebral glucose metabolism, inflammatory processes, axonal integrity, and water homeostasis based on published data. We found that the trajectories of these pathologies run on different timescales in the two species, and it appears that there is no universal "conversion rate" between rodent and human pathophysiological processes. For example, the inflammatory process appears to have an abbreviated time scale in rodents versus humans relative to cerebral glucose metabolism or axonal pathologies. Limitations toward determining conversion rates for various pathobiological processes include the use of differing outcome measures in experimental and clinical TBI studies and the rarity of longitudinal studies. In order to better translate time and close the translational gap, we suggest 1) using clinically relevant outcome measures, primarily in vivo imaging and blood-based proteomics, in experimental TBI studies and 2) collecting data at multiple post-injury time points with a frequency exceeding the expected information content by two or three times. Combined with a big data approach, we believe these measures will facilitate the translation of promising experimental treatments into clinical use

Validation of vessel size imaging (VSI) in high-grade human gliomas using magnetic resonance imaging, image-guided biopsies, and quantitative immunohistochemistry.

To evaluate the association between a vessel size index (VSIMRI) derived from dynamic susceptibility contrast (DSC) perfusion imaging using a custom spin-and-gradient echo echoplanar imaging (SAGE-EPI) sequence and quantitative estimates of vessel morphometry based on immunohistochemistry from image-guided biopsy samples. The current study evaluated both relative cerebral blood volume (rCBV) and VSIMRI in eleven patients with high-grade glioma (7 WHO grade III and 4 WHO grade IV). Following 26 MRI-guided glioma biopsies in these 11 patients, we evaluated tissue morphometry, including vessel density and average radius, using an automated procedure based on the endothelial cell marker CD31 to highlight tumor vasculature. Measures of rCBV and VSIMRI were then compared to histological measures. We demonstrate good agreement between VSI measured by MRI and histology; VSIMRI = 13.67 μm and VSIHistology = 12.60 μm, with slight overestimation of VSIMRI in grade III patients compared to histology. rCBV showed a moderate but significant correlation with vessel density (r = 0.42, p = 0.03), and a correlation was also observed between VSIMRI and VSIHistology (r = 0.49, p = 0.01). The current study supports the hypothesis that vessel size measures using MRI accurately reflect vessel caliber within high-grade gliomas, while traditional measures of rCBV are correlated with vessel density and not vessel caliber