Measured Sonic Boom Signatures Above and Below the XB-70 Airplane Flying at Mach 1.5 and 37,000 Feet

During the 1966-67 Edwards Air Force Base (EAFB) National Sonic Boom Evaluation Program, a series of in-flight flow-field measurements were made above and below the USAF XB-70 using an instrumented NASA F-104 aircraft with a specially designed nose probe. These were accomplished in the three XB-70 flights at about Mach 1.5 at about 37,000 ft. and gross weights of about 350,000 lbs. Six supersonic passes with the F-104 probe aircraft were made through the XB-70 shock flow-field; one above and five below the XB-70. Separation distances ranged from about 3000 ft. above and 7000 ft. to the side of the XB-70 and about 2000 ft. and 5000 ft. below the XB-70. Complex near-field "sawtooth-type" signatures were observed in all cases. At ground level, the XB-70 shock waves had not coalesced into the two-shock classical sonic boom N-wave signature, but contained three shocks. Included in this report is a description of the generating and probe airplanes, the in-flight and ground pressure measuring instrumentation, the flight test procedure and aircraft positioning, surface and upper air weather observations, and the six in-flight pressure signatures from the three flights

The expression of PD-L1 in salivary gland carcinomas

Objective was to analyze the role of PD-L1 and its relation to demographic, patho-clinical and outcome parameters in salivary gland carcinoma (SGC) patients. Patients treated for salivary gland carcinomas between 1994 and 2010 were included. A retrospective chart review for baseline characteristics, pathohistological, clinical and outcome data was performed. Immunohistochemistry for PD-L1 was performed using tissue microarrays. PD-L1 expression was assessed in tumor cells and tumor-infiltrating immune cells (TIIC) and statistical analysis with regard to baseline and outcome data was performed. Expression of PD-L1 (by means ≥1% of the cells with PD-L1 positivity) was present in the salivary gland carcinoma cells of 17%, in the TIIC of 20% and in both tumor cells and TIIC of 10% the patients. PD-L1 expression in tumor cells and both tumor cells and TIIC was related to tumor grading (p = 0.035 and p = 0.031, respectively). A trend towards higher grading was also seen for PD-L1 expression in TIICs (p = 0.058). Patients with salivary duct carcinomas and PD-L1 expressing TIICs showed a significantly worse DFS and OS (p = 0.022 and p = 0.003, respectively), those with both tumor cells and TIIC expressing PD-L1 a significantly worse DFS (p = 0.030). PD-L1 expression is present in 17% and 20% of salivary gland carcinoma cells and TIIC. Ten percent of the patient showed a PD-L1 positivity in both tumor cells and TIIC. This is related to high tumor grading and therefore might be a negative prognostic factor

Equivalent Longitudinal Area Distributions of the B-58 and XB-70-1 Airplanes for Use in Wave Drag and Sonic Boom Calculations

A detailed geometric description, in wave drag format, has been developed for the Convair B-58 and North American XB-70-1 delta wing airplanes. These descriptions have been placed on electronic files, the contents of which are described in this paper They are intended for use in wave drag and sonic boom calculations. Included in the electronic file and in the present paper are photographs and 3-view drawings of the two airplanes, tabulated geometric descriptions of each vehicle and its components, and comparisons of the electronic file outputs with existing data. The comparisons include a pictorial of the two airplanes based on the present geometric descriptions, and cross-sectional area distributions for both the normal Mach cuts and oblique Mach cuts above and below the vehicles. Good correlation exists between the area distributions generated in the late 1950s and 1960s and the present files. The availability of these electronic files facilitates further validation of sonic boom prediction codes through the use of two existing data bases on these airplanes, which were acquired in the 1960s and have not been fully exploited

Review of Student-Built Spectroscopy Instrumentation Projects

Copyright © 2020 American Chemical Society and Division of Chemical Education, Inc. One challenge of teaching chemical analysis is the proliferation of sophisticated, but often impenetrable, instrumentation in the modern laboratory. Complex instruments, and the software that runs them, distance students from the physical and chemical processes that generate the analytical signal. A solution to this challenge is the introduction of a student-driven instrument-building project. Visible absorbance spectroscopy is well-suited to such a project due to its relative simplicity and the ubiquity of absorbance measurements. This Article reviews simple instructor- A nd student-built instruments for spectroscopy, providing an overview of common designs, components, and applications. This comprehensive summary includes options that are suitable for in-person or remote learning with K-12 students and undergraduates in general chemistry, analytical chemistry, instrumental analysis, and electronics courses

Rapid cryogenic characterisation of 1024 integrated silicon quantum dots

Quantum computers are nearing the thousand qubit mark, with the current focus on scaling to improve computational performance. As quantum processors grow in complexity, new challenges arise such as the management of device variability and the interface with supporting electronics. Spin qubits in silicon quantum dots are poised to address these challenges with their proven control fidelities and potential for compatibility with large-scale integration. Here, we demonstrate the integration of 1024 silicon quantum dots with on-chip digital and analogue electronics, all operating below 1 K. A high-frequency analogue multiplexer provides fast access to all devices with minimal electrical connections, enabling characteristic data across the quantum dot array to be acquired in just 5 minutes. We achieve this by leveraging radio-frequency reflectometry with state-of-the-art signal integrity, reaching a minimum integration time of 160 ps. Key quantum dot parameters are extracted by fast automated machine learning routines to assess quantum dot yield and understand the impact of device design. We find correlations between quantum dot parameters and room temperature transistor behaviour that may be used as a proxy for in-line process monitoring. Our results show how rapid large-scale studies of silicon quantum devices can be performed at lower temperatures and measurement rates orders of magnitude faster than current probing techniques, and form a platform for the future on-chip addressing of large scale qubit arrays.Comment: Main text: 14 pages, 8 figures, 1 table Supplementary: 8 pages, 6 figure

The Effects of Biting and Pulling on the Forces Generated during Feeding in the Komodo Dragon (Varanus komodoensis)

In addition to biting, it has been speculated that the forces resulting from pulling on food items may also contribute to feeding success in carnivorous vertebrates. We present an in vivo analysis of both bite and pulling forces in Varanus komodoensis, the Komodo dragon, to determine how they contribute to feeding behavior. Observations of cranial modeling and behavior suggest that V. komodoensis feeds using bite force supplemented by pulling in the caudal/ventrocaudal direction. We tested these observations using force gauges/transducers to measure biting and pulling forces. Maximum bite force correlates with both body mass and total body length, likely due to increased muscle mass. Individuals showed consistent behaviors when biting, including the typical medial-caudal head rotation. Pull force correlates best with total body length, longer limbs and larger postcranial motions. None of these forces correlated well with head dimensions. When pulling, V. komodoensis use neck and limb movements that are associated with increased caudal and ventral oriented force. Measured bite force in Varanus komodoensis is similar to several previous estimations based on 3D models, but is low for its body mass relative to other vertebrates. Pull force, especially in the ventrocaudal direction, would allow individuals to hunt and deflesh with high success without the need of strong jaw adductors. In future studies, pull forces need to be considered for a complete understanding of vertebrate carnivore feeding dynamics

The IASLC Early Lung Imaging Confederation (ELIC) Open-Source Deep Learning and Quantitative Measurement Initiative.

BackgroundWith global adoption of CT lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open source, cloud-based, globally distributed, screening CT imaging dataset and computational environment that are compliant with the most stringent international privacy regulations that also protects the intellectual properties of researchers, the International Association of the Study of Lung Cancer (IASLC) sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be utilized for clinically relevant AI research.MethodsIn this second Phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans.ResultsA total of 1,394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness ≥ 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high quality CT scans.ConclusionThese initial experiments demonstrated that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based datasets