An Investigation of Bimodal Cellular Distributions via Supercritical Fluid Assisted (SCF) Foam Injection Molding

The Corporate Average Fuel Economy (CAFÉ) standards for 2025 are set to introduce a fleet-wide average of 54.5 MPG for cars and thereby, prevent emissions of 6 billion metric tons of CO2 [1]. This has propelled the automotive industry to renew their focus on lightweighting cars, particularly through the use of crude oil-based structural foams. While these foams offer a unique combination of ultra-lightweighting with adequate strength, they are practically non-renewable, non-biodegradable and contribute to the growing anthropogenic carbon footprint. An alternative paradigm to such foams is the use of biosourced polymers as they offer immense advantages due to their renewable, sustainable and biodegradable nature. Currently, polylactic acid (PLA) remains the most abundant commercially consumed biopolymer, but it suffers from two major drawbacks: its inherent brittle nature and poor melt processability. Blending PLA with an inherently toughened counterpart provides an effective mechanism to overcome both these drawbacks [2]. Additionally, foaming of PLA-based blends can provide a replacement for synthetic structural foams. However, processing of such blended foams is inhibited by challenges associated with structural foam molding with regard to controlling foam microstructure – specifically, cell size and cell density. Additionally, controlled processing of bimodal cell structure has remained elusive with currently used molding parameters and chemical blowing agents. Bimodal cellular distributions are preferred for their superior properties – enhanced toughness and compressive strength, weight reduction, and insulating properties –compared to their unimodal counterparts. This study investigates the effect of material properties and processing parameters on unique cellular distributions of polylactic acid (PLA), polybutylene succinate adipate (PBSA) and their blends processed via supercritical fluid-assisted injection molding. Cell morphology, size and density were determined via scanning electron microscopy, while their influence on mechanical properties was studied using tensile testing. Thermal stability of the blends was studied via differential scanning calorimetry and thermo-gravimetric analyzer. Effect of melt rheology and viscoelastic behavior was studied in an effort to explain the bimodal cellular structure obtained

Modeling and Optimization of Dynamical Systems in Epidemiology using Sparse Grid Interpolation

Infectious diseases pose a perpetual threat across the globe, devastating communities, and straining public health resources to their limit. The ease and speed of modern communications and transportation networks means policy makers are often playing catch-up to nascent epidemics, formulating critical, yet hasty, responses with insufficient, possibly inaccurate, information. In light of these difficulties, it is crucial to first understand the causes of a disease, then to predict its course, and finally to develop ways of controlling it. Mathematical modeling provides a methodical, in silico solution to all of these challenges, as we explore in this work. We accomplish these tasks with the aid of a surrogate modeling technique known as sparse grid interpolation, which approximates dynamical systems using a compact polynomial representation. Our contributions to the disease modeling community are encapsulated in the following endeavors. We first explore transmission and recovery mechanisms for disease eradication, identifying a relationship between the reproductive potential of a disease and the maximum allowable disease burden. We then conduct a comparative computational study to improve simulation fits to existing case data by exploiting the approximation properties of sparse grid interpolants both on the global and local levels. Finally, we solve a joint optimization problem of periodically selecting field sensors and deploying public health interventions to progressively enhance the understanding of a metapopulation-based infectious disease system using a robust model predictive control scheme

Finite Element Analysis Prediction of Stresses in H.L. Hunley Submarine by Global-to-Local Model Coordination

H.L Hunley was a submarine of the Confederate States of America that participated in the American Civil War. On February 17, 1864, H.L.Hunley created history by becoming the first submarine to sink a enemy ship after its attack on USS Houstanic. After Hunley never returned to the shore and the details of its wreck were unknown. On August 8, 2000, H. L Hunley was brought to the surface after 136 years of its wreckage. The submarine is currently at the Warren Lasch Conservation Center located in Charleston. This study focuses on the structural analysis of the H.L Hunley submarine to predict stresses and potential structural failure. Modeling the structure is challenging because of (1) the lack of symmetry due to its current position, (2) non-uniformity due to high corrosion, and (3) the riveted connections with more than 4000 rivets. Although connections between plates in ships are generally considered stronger and stiffer than the rest of the structure, this assumption is assumed to be invalid in the case of the Hunley because of the high and non-uniform corrosion. Since modeling the entire submarine and its 4000 rivet is impossible, the purpose of this study is to create a coordination procedure between the global model of the submarine with simplified connections and the local model of a riveted connection to affectively predict the stresses. The Global model is the whole submarine modeled using shell elements to decrease complexity. The local model consists of one of the riveted connections in the submarine. The validation of the procedure is discussed

XY Traverse Stage for Automated Microscopy using Compliant Mechanism

This abstract provides an overview of research in precision engineering, with a focus on compliant mechanisms for XY stage positioning. The study explores existing methods, such as piezoelectric actuators and compliant mechanisms, as well as the work of renowned researchers in the field. It introduces two distinctive approaches for displacement reduction: topology optimization and flexure transmission mechanisms. The research aims to improve precision and reliability in automated microscopy and suggests a novel actuation procedure for the XY traversal stage using displacement reduction compliant mechanisms. Compliant mechanisms are essential in this application due to their unique attributes, including high precision, reduced mechanical complexity. These mechanisms play a crucial role in XY stage positioning for automated microscopy, where accuracy and reliability are of paramount importance. The study suggests potential future directions for validation, considering thermal effects, scalability, and decoupling challenges in XY stage applications, with the ultimate goal of advancing automated microscopy technology

Influenza specific T- and B-cell responses in immunosuppressed patients

Influenza, known as the ‘flu’, is a recurrent acute viral infection that might cause severe inflammation, particularly in vulnerable individuals, i.e. young children, the elderly, and immune-suppressed patients, such as stem cell transplant recipients. Prevention strategies, primarily vaccination, and possibly the use of anti-viral drugs, are recommended with the aim to reduce mortality and morbidity. Influenza vaccination responses are often sub-optimal in immune-compromised patients. There is therefore a need to evaluate other vaccination systems and schedules to improve vaccine efficacy. We mapped the humoral and cellular anti-flu directed immune responses and studied in a first set of experiments the immune responses in immune competent individuals prior to, and following a natural pandemic influenza infection, as well as after adjuvanted Pandemrix® influenza vaccination. This was performed prospectively during the H1N1 pandemic influenza of 2009. ‘High content’ influenza proteome peptide arrays were used to gauge serum IgG epitope signatures prior to and after Pandemrix® vaccination/ or H1N1 pandemic infection described in paper I. A novel epitope residing in the sialic acid receptor-binding domain of VEPGDKITFEATGNL (251-265) of the pandemic flu hemagglutinin was identified. This epitope was found to be exclusively recognized in serum from previously vaccinated individuals and never in serum from individuals with H1N1 infection. The natural H1N1 infection induced a different footprint of IgG epitope recognition patterns as compared to the Pandemrix® H1N1 vaccination. Pre-transplant influenza vaccination of the donor or allogeneic hematopoietic stem cell (HSCT) candidate was evaluated in a randomized study of 122 HSCT patients reported in paper II. The antibody titers against H1 (p=0.028) and H3 (p<0.001) were highest in the pretransplant recipient vaccination group until d.180 after transplantation. A significant difference was found concerning the specific Ig levels against pandemic H1N1 at 6 months after HSCT (p=0.02). The mean IgG levels against pandemic H1N1, generic H1N1 and H3N2 were highest in the pre-transplant recipient vaccination group. Pre-transplant influenza vaccination of the donor or the HSCT candidate was found to be beneficial in eliciting seroprotective titers. The immunogenicity after a single dose of adjuvanted trivalent virosomal vaccination was evaluated in a cohort of 21 HSCT recipients and compared to a control cohort of 30 HSCT recipients who received a single dose of non-adjuvanted seasonal trivalent subunit vaccination, reported in Paper III. The delta change of IFNγ production in response to pandemic influenza H1N1 (p=0.005) and influenza B antigens (p=0.01) were significantly increased in blood from individuals who received the virosomal, as compared to the nonadjuvanted vaccine. Virosomal vaccination was found to be beneficial in eliciting robust cellular immune responses to influenza pandemic H1N1. Pandemic influenza hemagglutinin MHC class 1 peptide restricted CD8 T-cells were enumerated over the course of a natural pandemic influenza infection and Pandemrix® vaccination in a prospective study reported in Paper IV. PBMCs from vaccinated control individuals exhibited a significantly increased percentage of (p=0.003) hemagglutinin specific CD8 T-cells that resided in the terminally differentiated effector memory compartment, as compared to PBMCs from individuals that contracted H1N1 infection. The cellular immune signatures were found to be different elicited by a natural flu infection as compared to vaccination concerning the phenotype/maturation of antigen-specific CD8 Tcells

Data Mining in Biodata Analysis

For finding interesting patterns in large databases has lot of development in recent years.. Data mining is used in many fields like medicine, securing the data etc. Whereas bio data means the data regarding the biology, medical science, DNA technology and Bioinformatics in-depth analysis. Bio Informatics is the science which can perform managing, finding data, integrating, interrupting information from biological data, genomic, and metadata. Even additional knowledge and complexness can lead to the integration among genes. This paper is all about joining these two fields, the data regarding biology us ing data mining and gives the details of future developments in biodata analysis

Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement using Data-Driven Approach

Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li2CO3 content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders\u27 compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of CAC at different ages. The XGBoost model is then used to predict the compressive strength, porosity, and hydration products of CAC based on the mixture design and age. The XGBoost model is also used to evaluate the influence of input parameters on the compressive strength and porosity of CAC. Based on the results of this analysis, a closed-form analytical model is developed to predict the compressive strength and porosity of CAC accurately. Overall, the study demonstrates that ML can be effectively used to predict the properties of CAC binders, providing a valuable tool for researchers and practitioners in the field of cement science

Thermoplastics Foams: An Automotive Perspective

The automotive industry has witnessed a massive shift in terms of materials used, ranging from being a metallic heavyweight in the 1950s to employing a hybrid sandwich of multiple material systems. This apparent shift can be attributed to achieving improvements in performance, safety and fuel efficiency, along with responding to the various environmental regulations imposed by different governments. The recent advocacy of Corporate Average Fuel Economy (CAFE) standard of 54.5 MPG by 2025 by the US Environmental Protection Agency (EPA) to reduce greenhouse gas (GHG) emissions [1] has spurred the sector at large towards the use of lightweight materials

AI for Search and Rescue - Locating a Missing Person

Building on the work done initially as a SURP 2021 project and continued through 2021-23, the focus for this summer project will be on the use of computer technology for locating a missing person. Over the last year, we developed the digital equivalents of about 30 paper-based S&R forms and the infrastructure to collect the respective information. In their current use, these paper forms are filled out by search teams, collected in a command post, and reviewed by search coordinators. This process is time-consuming, prone to errors and loss of information, and relies heavily on the experience, skills, and mental acuity of the search coordinators. At the core of this process is the Lost Person Questionnaire, a lengthy and complex form that collects relevant information about the subject of the S&R mission. For this SURP effort, we will explore the use of Artificial Intelligence and Machine Learning to combine information about the ongoing search effort, past missions with similar profiles, and general knowledge such as terrain and travel routes to identify areas of high priority for the search