Effects of Overmolding Process Parameters on Bondzone Quality

Urban air mobility (UAM) vehicles refer to small sized systems that transport people or cargo by air. In recent years, the interest in UAM vehicles has risen to combat traffic congestion, provide faster delivery times, or provide easier access to remote places. To meet potential future demand, traditional aircraft manufacturers must introduce manufacturing technologies that can cope with high production rates. One manufacturing technology aircraft manufacturers are investigating is overmolding, a manufacturing technology common to the automotive industry. Overmolding combines the manufacturing and assembly cycles into one by injection molding a component on top of a substrate. The quality of the bond between the injection molded component and substrate is of great importance and will impact the strength of the part. Overmolding can increase the production rate significantly, but, it has seen little use for aerospace load-bearing applications with one of the concerns being the limited understanding of the bond characteristics. This study aims to characterize the effects of overmolding process parameters such as, injection pressure and temperature, packing temperature and pressure, and mold temperature, as well as material properties (like, melt temperature and viscosity) on bond strength using Moldex3D and ABAQUS. Moldex3D is used to determine the effects of the process and material parameters on the temperature distribution at the bond zone, while ABAQUS is used to predict the failure load of a T-stiffener overmolded on a substrate

Systematic review and case series: flexible sigmoidoscopy identifies most cases of checkpoint inhibitor‐induced colitis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149542/1/apt15263_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149542/2/apt15263.pd

Prevalence and correlates of repeat testing during pregnancy and postpartum in rural Kenya

Thesis (Master's)--University of Washington, 2018University of Washington Abstract Prevalence and correlates of repeat testing during pregnancy and postpartum in rural Kenya Monalisa Penumetsa Chair of the Supervisory Committee: Alison Drake Department of Global Health Background: Repeat HIV testing during pregnancy and the postpartum period is crucial for early detection and treatment of incident maternal HIV infection, and to achieve elimination of mother-to-child HIV transmission (MTCT). World Health Organization (WHO) guidelines recommend repeat testing of peripartum HIV negative women but data on implementation are lacking. The objective of this study is to determine the uptake and correlates of repeat HIV testing during pregnancy, delivery, and postpartum. Methods: HIV seronegative women seeking care during the 3rd trimester, delivery, or at 6 weeks, 6 months, or 9 months postpartum were enrolled in a cross-sectional study in rural Kenya. Prior HIV testing history was abstracted from maternal child health (MCH) booklets to estimate prevalence of repeat testing at different timepoints. Poisson generalized linear models were used to determine correlates of repeat testing. We externally validated a risk score tool to predict maternal HIV infections using area under the curve (AUC) and Brier score. Results: Among 1558 women enrolled, the median age was 23 years, 60% of women were married and the median number of times tested for HIV in the most recent pregnancy was 1 (interquartile range [IQR]: 1-2). Prevalence of new HIV infection detected by the study was 0.4% with no difference between pregnant and postpartum women (Odds ratio: 0.8, 95% Confidence Interval [CI]: 0.2-3.7; p=0.8). Prevalence of programmatic repeat HIV testing at 6 weeks (51%) postpartum was significantly higher than in the 3rd trimester (22%), during delivery (5%) and 6 months postpartum (32%) (p= <0.001). In multivariate analysis, women with more lifetime number of sexual partners (Prevalence Ratio [PR]: 0.97 per 1 unit increase, CI: 0.95-0.99; p=.007), with history of sexually transmitted infection (STI) (PR: 1.18, CI: 1.10-1.27; p=<.001) and women who were 21-30 years were less likely than women <21 years (PR: 0.87, CI: 0.81-0.93; p=<.001) to receive ≥1 programmatic repeat tests. External validation of a risk score tool yielded an AUC of 0.82 (95%CI: 0.68-0.93) and Brier score 0.21. Conclusion: Prevalence of repeat testing was higher during the early postpartum period than in late pregnancy. Developing strategies that address barriers and increase antenatal care (ANC) attendance, could improve uptake of repeat testing during pregnancy

A comparison of energy efficient adaptation algorithms in cloud data centers

Context: In recent years, Cloud computing has gained a wide range of attention in both industry and academics as Cloud services offer pay-per-use model, due to increase in need of factors like reliability and computing results with immense growth in Cloud-based companies along with a continuous expansion of their scale. However, the rise in Cloud computing users can cause a negative impact on energy consumption in the Cloud data centers as they consume huge amount of overall energy. In order to minimize the energy consumption in virtual datacenters, researchers proposed various energy efficient resources management strategies. Virtual Machine dynamic Consolidation is one of the prominent technique and an active research area in recent time, used to improve resource utilization and minimize the electric power consumption of a data center. This technique monitors the data centers utilization, identify overloaded, and underloaded hosts then migrate few/all Virtual Machines (VMs) to other suitable hosts using Virtual Machine selection and Virtual Machine placement, and switch underloaded hosts to sleep mode.   Objectives: Objective of this study is to define and implement new energy-aware heuristic algorithms to save energy consumption in Cloud data centers and show the best-resulted algorithm then compare performances of proposed heuristic algorithms with old heuristics.   Methods: Initially, a literature review is conducted to identify and obtain knowledge about the adaptive heuristic algorithms proposed previously for energy-aware VM Consolidation, and find the metrics to measure the performance of heuristic algorithms. Based on this knowledge, for our thesis we have proposed 32 combinations of novel adaptive heuristics for host overload detection (8) and VM selection algorithms (4), one host underload detection and two adaptive heuristic for VM placement algorithms which helps in minimizing both energy consumption and reducing overall Service Level Agreement (SLA) violation of Cloud data center. Further, an experiment is conducted to measure the performances of all proposed heuristic algorithms. We have used the CloudSim simulation toolkit for the modeling, simulation, and implementation of proposed heuristics. We have evaluated the proposed algorithms using PlanetLab VMs real workload traces.   Results: The results were measured using metrics energy consumption of data center (power model), Performance Degradation due to Migration (PDM), Service Level Agreement violation Time per Active Host (SLATAH), Service Level Agreement Violation (SLAV = PDM . SLATAH) and, Energy consumption and Service level agreement Violation (ESV).  Here for all four categories of VM Consolidation, we have compared the performances of proposed heuristics with each other and presented the best heuristic algorithm proposed in each category. We have also compared the performances of proposed heuristic algorithms with existing heuristics which are identified in the literature and presented the number of newly proposed algorithms work efficiently than existing algorithms. This comparative analysis is done using T-test and Cohen's d effect size.   From the comparison results of all proposed algorithms, we have concluded that Mean absolute Deviation around median (MADmedain) host overload detection algorithm equipped with Maximum requested RAM VM selection (MaxR) using Modified First Fit Decreasing VM placement (MFFD), and Standard Deviation (STD) host overload detection algorithm equipped with Maximum requested RAM VM selection (MaxR) using Modified Last Fit decreasing VM placement (MLFD) respectively performed better than other 31 combinations of proposed overload detection and VM selection heuristic algorithms, with regards to Energy consumption and Service level agreement Violation (ESV). However, from the comparative study between existing and proposed algorithms, 23 and 21 combinations of proposed host overload detection and VM selection algorithms using MFFD and MLFD VM placements respectively performed efficiently compared to existing (baseline) heuristic algorithms considered for this study.   Conclusions: This thesis presents novel proposed heuristic algorithms that are useful for minimization of both energy consumption and Service Level Agreement Violation in virtual datacenters. It presents new 23 combinations of proposed host overloading detection and VM selection algorithms using MFFD VM placement and 21 combinations of proposed host overloading detection and VM selection algorithms using MLFD VM placement, which consumes the minimum amount of energy with minimal SLA violation compared to the existing algorithms. It gives scope for future researchers related to improving resource utilization and minimizing the electric power consumption of a data center. This study can be extended in further by implementing the work on other Cloud software platforms and developing much more efficient algorithms for all four categories of VM consolidation

Reversible myocardial dysfunction following intraocular bevacizumab administration

Heart failure has been reported as a rare side effect of bevacizumab, a chemotherapeutic agent, used in the treatment of breast cancer. However, reversible left ventricular systolic dysfunction with a pattern similar to stress-induced cardiomyopathy has not been reported. The etiopathogenesis of stress-induced cardiomyopathy is poorly understood. Given this uncertainty, we should always look out for other potential offenders causing similar presentation, rather than label all of them as stress-induced

Staphylococcus aureus Endocarditis with Multivalvular Involvement Secondary to an Atrial Septal Defect

Infective endocarditis is usually diagnosed using modified Duke’s criteria. Our patient had a subacute presentation and a low suspicion for endocarditis during admission, unfortunately leading to her death. Despite advances in diagnostic and therapeutic measures including antibiotic therapy and surgical techniques, morbidity and mortality with staphylococcal infective endocarditis remain high. Hence, we stress the significance of having a low threshold for TEE in patients with multisystem involvement due to Staphylococcus aureus that have evidence of persistent infection despite antibiotic treatment, even if the suspicion for endocarditis is low based on Duke’s criteria. TEE substantially improves the sensitivity of diagnosis but may not be readily available in many medical centers. Presence of an ASD has been noted to have increased the risk of left sided endocarditis even with conditions that predispose to right sided endocarditis, particularly in patients with hemodialysis and diabetes as morbid risk factors