Muffler characterization with implementation of the finite element method and experimental techniques

Determining how an exhaust system will perform acoustically before a prototype muffler is built can save the designer both a substantial amount of time and resources. In order to effectively use the simulation tools available it is important to understand what is the most effective tool for the intended purpose of analysis as well as how typical elements in an exhaust system affect muffler performance. An in-depth look at the available tools and their most beneficial uses are presented in this thesis. A full parametric study was conducted using the FEM method for typical muffler elements which was also correlated to experimental results. This thesis lays out the overall ground work on how to accurately predict sound pressure levels in the free field for an exhaust system with the engine properties included. The accuracy of the model is heavily dependent on the correct temperature profile of the model in addition to the accuracy of the source properties. These factors will be discussed in detail and methods for determining them will be presented. The secondary effects of mean flow, which affects both the acoustical wave propagation and the flow noise generation, will be discussed. Effective ways for predicting these secondary effects will be described. Experimental models will be tested on a flow rig that showcases these phenomena

Modeling reactivity to biological macromolecules with a deep multitask network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural networkthe XenoSite reactivity modelusing literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity

The impact of an iPad-supported annotation and sharing technology on university students' learning

© 2018 Elsevier Ltd iPads, or more generally tablet computers, have received rapid and widespread uptake across higher education. Despite this, there is limited evidence of how their use affects student learning within this context. This study focuses on the use of a tablet by the instructor to support the annotation and in-class sharing of students' work to create a collaborative learning environment within a first year undergraduate subject. This paper reports the results of an empirical study looking at the effect this tablet technology has on student performance using a sample of 741 first-year accounting students. The study uses data from enrolment and attendance records, end of semester examination results and student perceptions from a survey. Results indicate that class sharing of the instructor's and students' annotation of homework through the use of a tablet is associated with an improvement in student performance on procedural or equation-based questions as well as increased student engagement. However, contrary to expectations, the introduction of in class annotations was associated with a decline in student performance on theoretical, extended response questions. The authors argue that affordances of the tablet, when used in a student-centred way, can introduce a bias towards some kinds of interactions over others. This large-scale study of in-class tablet use suggests that though the tablets may be positively associated with student engagement and satisfaction, caution must be exercised in how the use by the instructor affects the classroom environment and what students learn. These findings have particular relevance to university learning contexts with equation-centric subjects such as those in Business and STEM

Vitellogenesis as a biomarker for estrogenic contamination of the aquatic environment

A rapidly increasing number of chemicals, or their degradation products, are being recognized as possessing estrogenic activity, albeit usually weak. We have found that effluent from sewage treatment works contains a chemical, or mixture of chemicals, that induces vitellogenin synthesis in male fish maintained in the effluent, thus indicating that the effluent is estrogenic. The effect was extremely pronounced and occurred at all sewage treatment works tested. The nature of the chemical or chemicals causing the effect is presently not known. However, we have tested a number of chemicals known to be estrogenic to mammals and have shown that they are also estrogenic to fish; that is, no species specificity was apparent. Many of these weakly estrogenic chemicals are known to be present in effluents. Further, a mixture of different estrogenic chemicals was considerably more potent than each of the chemicals when tested individually, suggesting that enhanced effects could occur when fish are exposed simultaneously to various estrogenic chemicals (as is likely to occur in rivers receiving effluent). Subsequent work should determine whether exposure to these chemicals at the concentrations present in the environment leads to any deleterious physiological effects

Integrating human and environmental health in antibiotic risk assessment: A critical analysis of protection goals, species sensitivity and antimicrobial resistance

This is the author accepted manuscriptAntibiotics are vital in the treatment of bacterial infectious diseases but when released into the environment they may impact non-target organisms that perform vital ecosystem services and enhance antimicrobial resistance development with significant consequences for human health. We evaluate whether the current environmental risk assessment regulatory guidance is protective of antibiotic impacts on the environment, protective of antimicrobial resistance, and propose science-based protection goals for antibiotic manufacturing discharges. A review and meta-analysis was conducted of aquatic ecotoxicity data for antibiotics and for minimum selective concentration data derived from clinically relevant bacteria. Relative species sensitivity was investigated applying general linear models, and predicted no effect concentrations were generated for toxicity to aquatic organisms and compared with predicted no effect concentrations for resistance development. Prokaryotes were most sensitive to antibiotics but the range of sensitivities spanned up to several orders of magnitude. We show reliance on one species of (cyano)bacteria and the ‘activated sludge respiration inhibition test’ is not sufficient to set protection levels for the environment. Individually, neither traditional aquatic predicted no effect concentrations nor predicted no effect concentrations suggested to safeguard for antimicrobial resistance, protect against environmental or human health effects (via antimicrobial resistance development). Including data from clinically relevant bacteria and also more species of environmentally relevant bacteria in the regulatory framework would help in defining safe discharge concentrations for antibiotics for patient use and manufacturing that would protect environmental and human health. It would also support ending unnecessary testing on metazoan species.AstraZeneca Global SHE Research Programm

Increasing Student Engagement and Performance in Introductory Accounting through Student-Generated Screencasts

The paper reports the findings of a trial of student generated screencasts in an introductory accounting subject. This paper examines the effect of this screencast project on student engagement and performance. The effect on student engagement is examined using data from a pre and post screencast project student survey and performance effects examined by analysing the performance of students completing and not completing the project. The results of the study suggest the screencast project facilitated higher student engagement and performance. These findings have important implications for integrating technologies such as screencasting to facilitate enhanced learning outcomes in introductory accounting subjects

Characterizing groundwater flow and heat transport in fractured rock using Fiber-Optic Distributed Temperature Sensing

International audienceWe show how fully distributed space-time measurements with Fiber-Optic Distributed Temperature Sensing (FO-DTS) can be used to investigate groundwater flow and heat transport in fractured media. Heat injection experiments are combined with temperature measurements along fiber-optic cables installed in boreholes. Thermal dilution tests are shown to enable detection of cross-flowing fractures and quantification of the cross flow rate. A cross borehole thermal tracer test is then analyzed to identify fracture zones that are in hydraulic connection between boreholes and to estimate spatially distributed temperature breakthrough in each fracture zone. This provides a significant improvement compared to classical tracer tests, for which concentration data are usually integrated over the whole abstraction borehole. However, despite providing some complementary results, we find that the main contributive fracture for heat transport is different to that for a solute tracer