Adjustable mount for electro-optic transducers in an evacuated cryogenic system

The invention is an adjustable mount for positioning an electro-optic transducer in an evacuated cryogenic environment. Electro-optic transducers are used in this manner as high sensitivity detectors of gas emission lines of spectroscopic analysis. The mount is made up of an adjusting mechanism and a transducer mount. The adjusting mechanism provided five degrees of freedom, linear adjustments and angular adjustments. The mount allows the use of an internal lens to focus energy on the transducer element thereby improving the efficiency of the detection device. Further, the transducer mount, although attached to the adjusting mechanism, is isolated thermally such that a cryogenic environment can be maintained at the transducer while the adjusting mechanism remains at room temperature. Radiation shields also are incorporated to further reduce heat flow to the transducer location

Student Construction of a Working Solar Collector

Student constructed projects may serve as the basis for studies of the advantages and limitations of solar energy. An eighth grade science class at Miami University\u27s McGuffey Laboratory School completed a study of solar energy with the construction of a window unit solar collector. The collector was installed in a south window of the science room in time for use and testing during the winter of 1980. The class activity developed after numerous individual projects allowed students to test variables that would be important in the design, construction and use of such a unit. Students performed a variety of individual investigations to test construction materials, methods of insulation, heat storage, angle of collecting surface, and heat absorption and transmission of different materials. The results of individual investigations were important considerations as the project was being planned. One student then built a small scale model as a science fair project. Tests with the model were positive enough to encourage other students to cooperate in the construction of a full-scale working unit

Distinguishing artefacts:evaluating the saturation point of convolutional neural networks

Prior work has shown Convolutional Neural Networks (CNNs) trained on surrogate Computer Aided Design (CAD) models are able to detect and classify real-world artefacts from photographs. The applications of which support twinning of digital and physical assets in design, including rapid extraction of part geometry from model repositories, information search \& retrieval and identifying components in the field for maintenance, repair, and recording. The performance of CNNs in classification tasks have been shown dependent on training data set size and number of classes. Where prior works have used relatively small surrogate model data sets ($<100$ models), the question remains as to the ability of a CNN to differentiate between models in increasingly large model repositories. This paper presents a method for generating synthetic image data sets from online CAD model repositories, and further investigates the capacity of an off-the-shelf CNN architecture trained on synthetic data to classify models as class size increases. 1,000 CAD models were curated and processed to generate large scale surrogate data sets, featuring model coverage at steps of 10$^{\circ}$, 30$^{\circ}$, 60$^{\circ}$, and 120$^{\circ}$ degrees. The findings demonstrate the capability of computer vision algorithms to classify artefacts in model repositories of up to 200, beyond this point the CNN's performance is observed to deteriorate significantly, limiting its present ability for automated twinning of physical to digital artefacts. Although, a match is more often found in the top-5 results showing potential for information search and retrieval on large repositories of surrogate models.Comment: 6 Pages, 5 Figures, 2 Tables, Conference, Design Engineering, CNN, Digital Twi

August 1972 solar-terrestrial events: Observations of interplanetary shocks at 2.2 AU

Pioneer 10 magnetic field measurements, supplemented by previously published plasma data, have been used to identify shocks at 2.2 AU associated with the large solar flares of early August 1972. The first three flares, which gave rise to three forward shocks at Pioneer 9 and at earth, led to only a single forward shock at Pioneer 10. The plasma driver accompanying the shock has been tentatively identified. A local shock velocity at Pioneer 10 of 717 km/s has been estimated by assuming that the shock was propagating radially across the interplanetary magnetic field. This velocity and the rise time of ≃2 s imply a shock thickness of ∼1400 km, which appears to be large in comparison with the characteristic plasma lengths customarily used to account for the thickness of the earth's bow shock. This Pioneer 10 shock is identified with the second forward shock observed at Pioneer 9, which was then at 0.8 AU and radially aligned with Pioneer 10, since it was apparently the only Pioneer 9 shock that was also driven. The local velocity of the Pioneer 9 shock of 670 km/s, previously inferred by other authors, compares reasonably well with the local velocity at Pioneer 10, but both values are significantly smaller than the average value computed from the time interval required for the shock to propagate from the sun to Pioneer 9 (2220 km/s). The velocity implied by the time required to propagate from Pioneer 9 to Pioneer 10 (770 km/s) is in reasonable agreement with the local velocities. The fourth solar flare also gave rise to a forward shock at Pioneer 10 as well as at Pioneer 9. The local velocity at Pioneer 10, estimated on the basis of quasi-perpendicularity, is 660 km/s, a value which again agrees well with previously derived velocities for the Pioneer 9 shock of 670 km/s. The local velocities for this shock and the velocity between Pioneer 9 and Pioneer 10 (635 km/s) are also significantly less than the average velocity of propagation from the sun to Pioneer 9 (830 km/s). The general finding that the local velocities of both shocks are approximately equal at 0.8 and 2.2 AU but significantly slower than the average speeds nearer the sun is interpreted as evidence of a major deceleration of the shocks as they propagate outward from the sun that is essentially completed when the shocks reach 0.8 AU, there being little, if any, subsequent deceleration. This conclusion is qualitatively inconsistent with previous inferences of a deceleration of the shocks as they propagate from 0.8 to 2.2 AU. A third, reverse shock is also identified in the Pioneer 10 data which was not seen either at Pioneer 9 or at earth. The estimated speed of this shock is 530 km/s, and its estimated thickness is ≲500 km, which compares well with an anticipated proton inertial length of 500 km

Acoustic classification of guitar tunings with deep learning

A guitar tuning is the allocation of pitches to the open strings of the guitar. A wide variety of guitar tunings are featured in genres such as blues, classical, folk, and rock. Standard tuning provides a convenient placing of intervals and a manageable selection of fingerings. However, numerous other tunings are frequently used as they offer different harmonic possibilities and playing methods. A robust method for the acoustic classification of guitar tunings would provide the following benefits for digital libraries for musicology: (i) guitar tuning tags could be assigned to music recordings; these tags could be used to better organise, retrieve, and analyse music in digital libraries, (ii) tuning classification could be integrated into an automatic music transcription system, thus facilitating the production of more accurate and fine-grained symbolic representations of guitar recordings, (iii) insights acquired through guitar tunings research, would be helpful when designing systems for indexing, analysing, and transcribing other string instruments. Neural networks offer a promising approach for the automated identification of guitar tunings as they can learn useful features for complex discriminative tasks. Furthermore, they can learn directly from unstructured data, thereby reducing the need for elaborate feature extraction techniques. Thus, we evaluate the potential of neural networks for the acoustic classification of guitar tunings. A dataset of authentic song recordings, which featured polyphonic acoustic guitar performances in various tunings, was compiled and annotated. Additionally, a dataset of synthetic polyphonic guitar audio in 5 different tunings was generated with sample-based audio software and tablatures. Using audio converted into log mel spectrograms and chromagrams as input, convolutional neural networks were trained to classify guitar tunings. The resulting models were tested using unseen data from disparate recording conditions. The best performing systems attained a classification accuracy of 97.5% (2 tuning classes) and 73.9% (5 tuning classes). This research provides evidence that neural networks can classify guitar tunings from music audio recordings; produces novel annotated datasets that contain authentic and synthetic guitar audio, which can serve as a benchmark for future guitar tuning research; proposes new methods for the collection, annotation, processing, and synthetic generation of guitar data

Anthropogenic ecosystem disturbance and the recovery debt

Ecosystem recovery from anthropogenic disturbances, either without human intervention or assisted by ecological restoration, is increasingly occurring worldwide. As ecosystems progress through recovery, it is important to estimate any resulting deficit in biodiversity and functions. Here we use data from 3,035 sampling plots worldwide, to quantify the interim reduction of biodiversity and functions occurring during the recovery process (that is, the 'recovery debt'). Compared with reference levels, recovering ecosystems run annual deficits of 46-51% for organism abundance, 27-33% for species diversity, 32-42% for carbon cycling and 31-41% for nitrogen cycling. Our results are consistent across biomes but not across degrading factors. Our results suggest that recovering and restored ecosystems have less abundance, diversity and cycling of carbon and nitrogen than 'undisturbed' ecosystems, and that even if complete recovery is reached, an interim recovery debt will accumulate. Under such circumstances, increasing the quantity of less-functional ecosystems through ecological restoration and offsetting are inadequate alternatives to ecosystem protection

Exploring human-guided strategies for reaction network exploration:Interactive molecular dynamics in virtual reality as a tool for citizen scientists

The emerging fields of citizen science and gamification reformulate scientific problems as games or puzzles to be solved. Through engaging the wider non-scientific community, significant breakthroughs may be made by analyzing citizen-gathered data. In parallel, recent advances in virtual reality (VR) technology are increasingly being used within a scientific context and the burgeoning field of interactive molecular dynamics in VR (iMD-VR) allows users to interact with dynamical chemistry simulations in real time. Here, we demonstrate the utility of iMD-VR as a medium for gamification of chemistry research tasks. An iMD-VR "game" was designed to encourage users to explore the reactivity of a particular chemical system, and a cohort of 18 participants was recruited to playtest this game as part of a user study. The reaction game encouraged users to experiment with making chemical reactions between a propyne molecule and an OH radical, and "molecular snapshots" from each game session were then compiled and used to map out reaction pathways. The reaction network generated by users was compared to existing literature networks demonstrating that users in VR capture almost all the important reaction pathways. Further comparisons between humans and an algorithmic method for guiding molecular dynamics show that through using citizen science to explore these kinds of chemical problems, new approaches and strategies start to emerge.</p