Heating Augmentation for Short Hypersonic Protuberances

Computational aeroheating analyses of the Space Shuttle Orbiter plug repair models are validated against data collected in the Calspan University of Buffalo Research Center (CUBRC) 48 inch shock tunnel. The comparison shows that the average difference between computed heat transfer results and the data is about 9:5%. Using CFD and Wind Tunnel (WT) data, an empirical correlation for estimating heating augmentation on short hyper- sonic protuberances (k/delta < 0.33) is proposed. This proposed correlation is compared with several computed flight simulation cases and good agreement is achieved. Accordingly, this correlation is proposed for further investigation on other short hypersonic protuberances for estimating heating augmentation

LAURA Users Manual: 5.6

This users manual provides in-depth information concerning installation and execution of Laura, version 5. Laura is a structured, multiblock, computational aerothermodynamic simulation code. Version 5 represents a major refactoring of the original Fortran 77 Laura code toward a modular structure afforded by Fortran 95. The refactoring improved usability and maintainability by eliminating the requirement for problem-dependent recompilations, providing more intuitive distribution of functionality, and simplifying inter- faces required for multi-physics coupling. As a result, Laura now shares gas-physics modules, MPI modules, and other low-level modules with the Fun3D unstructured-grid code. In addition to internal refactoring, several new features and capabilities have been added, e.g., a GNU-standard installation process, parallel load balancing, automatic trajectory point sequencing, free-energy minimization, and coupled ablation and flow field radiation

Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods

Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow

Magnetic resonance spectroscopic imaging in gliomas: clinical diagnosis and radiotherapy planning

The reprogramming of cellular metabolism is a hallmark of cancer diagnosis and prognosis. Proton magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic technique for investigating brain metabolism to establish cancer diagnosis and IDH gene mutation diagnosis as well as facilitate pre-operative planning and treatment response monitoring. By allowing tissue metabolism to be quantified, MRSI provides added value to conventional MRI. MRSI can generate metabolite maps from a single volume or multiple volume elements within the whole brain. Metabolites such as NAA, Cho and Cr, as well as their ratios Cho:NAA ratio and Cho:Cr ratio, have been used to provide tumor diagnosis and aid in radiation therapy planning as well as treatment assessment. In addition to these common metabolites, 2-hydroxygluterate (2HG) has also been quantified using MRSI following the recent discovery of IDH mutations in gliomas. This has opened up targeted drug development to inhibit the mutant IDH pathway. This review provides guidance on MRSI in brain gliomas, including its acquisition, analysis methods, and evolving clinical applications

The relationship between video display terminals (VDTs) usage and dermatologic manifestations : a cross sectional study

BACKGROUND: Recently, it has been observed that Video Display Terminals (VDTs) usage for long periods can cause some dermatological manifestations on the face. An analytical cross-sectional study was designed in order to determine this relationship. METHODS: In this study, 600 office workers were chosen randomly from an organization in Tehran (Iran). The subjects were then divided into two groups based on their exposure to VDTs. 306 workers were considered exposure negative (non VDT user) who worked less than 7 hours a week with VDTs. The remainders 294 were exposure-positive, who worked 7 hours or more with VDTs. The frequency of dermatologic manifestations was compared in these two groups. RESULTS: In the exposure-positive and exposure-negative groups, the frequency of these dermatologic manifestations were 27 and 5 respectively. After statistical analysis, a P.value of < 0.05 was obtained indicating a statistically significant difference between these two groups for dermatological manifestations. CONCLUSION: According to our study, there is a relationship between dermatologic manifestations on the face and exposure to VDTs

A 30 ps Timing Resolution for Single Photons with Multi-pixel Burle MCP-PMT

We have achieved {approx}30 psec single-photoelectron and {approx}12ps for multi-photoelectron timing resolution with a new 64 pixel Burle MCP-PMT with 10 micron microchannel holes. We have also demonstrated that this detector works in a magnetic field of 15kG, and achieved a single-photoelectron timing resolution of better than 60 psec. The study is relevant for a new focusing DIRC RICH detector for particle identification at future Colliders such as the super B-factory or ILC, and for future TOF techniques. This study shows that a highly pixilated MCP-PMT can deliver excellent timing resolution

A combinatorial framework to quantify peak/pit asymmetries in complex dynamics

LL’s acknowledges funding from an EPSRC Early Career Fellowship EP/P01660X/1