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Maker activities help students connect to STEAM content through hands-on activities that emphasize the roles of mentors, peers, and in-person interaction with physical artifacts. Despite the positive affordances of these activities, they do not translate well to online settings. Without immediate in-person feedback mechanisms, unstructured making activities may lead to frustration and decreased engagement. How do communities help students develop identities as future engineers if local help and mentorship is not available? The proposed study aims to address challenges of scaffolding collaboration during remote maker sessions through investigation of a novel projection device that allows users to talk & share gestures around a common physical artifact while in separate locations.

Classical novae are shock-powered multiwavelength transients triggered by a thermonuclear runaway on an accreting white dwarf. V1674 Her is the fastest nova ever recorded (time to declined by two magnitudes is t2 = 1.1 d) that challenges our understanding of shock formation in novae. We investigate the physical mechanisms behind nova emission from GeV γ-rays to cm-band radio using coordinated Fermi-LAT, NuSTAR, Swift, and VLA observations supported by optical photometry. Fermi-LAT detected short-lived (18 h) 0.1–100 GeV emission from V1674 Her that appeared 6 h after the eruption began; this was at a level of (1.6 ± 0.4) × 10−6 photons cm−2 s−1. Eleven days later, simultaneous NuSTAR and Swift X-ray observations revealed optically thin thermal plasma shock-heated to kTshock = 4 keV. The lack of a detectable 6.7 keV Fe Kα emission suggests super-solar CNO abundances. The radio emission from V1674 Her was consistent with thermal emission at early times and synchrotron at late times. The radio spectrum steeply rising with frequency may be a result of either free-free absorption of synchrotron and thermal emission by unshocked outer regions of the nova shell or the Razin–Tsytovich effect attenuating synchrotron emission in dense plasma. The development of the shock inside the ejecta is unaffected by the extraordinarily rapid evolution and the intermediate polar host of this nova.

Abstract The Van Allen Probes Electric Fields and Waves (EFW) instrument provided measurements of electric fields and spacecraft floating potentials over a wide dynamic range from DC to 6.5 kHz near the equatorial plane of the inner magnetosphere between 600 km altitude and 5.8 Re geocentric distance from October 2012 to November 2019. The two identical instruments provided data to investigate the quasi-static and low frequency fields that drive large-scale convection, waves induced by interplanetary shock impacts that result in rapid relativistic particle energization, ultra-low frequency (ULF) MHD waves which can drive radial diffusion, and higher frequency wave fields and time domain structures that provide particle pitch angle scattering and energization. In addition, measurements of the spacecraft potential provided a density estimate in cold plasmas ( $<20~\text{eV}$ < 20 eV ) from 10 to $3000~\text{cm}^{-3}$ 3000 cm − 3 . The EFW instrument provided analog electric field signals to EMFISIS for wave analysis, and it received 3d analog signals from the EMFISIS search coil sensors for inclusion in high time resolution waveform data. The electric fields and potentials were measured by current-biased spherical sensors deployed at the end of four 50 m booms in the spacecraft spin plane (spin period $\sim11~\text{sec}$ ∼ 11 secmore ») and a pair of stacer booms with a total tip-tip separation of 15 m along the spin axis. Survey waveform measurements at 16 and/or 32 S/sec (with a nominal uncertainty of 0.3 mV/m over the prime mission) were available continuously while burst waveform captures at up to 16,384 S/sec provided high frequency waveforms. This post-mission paper provides the reader with information useful for accessing, understanding and using EFW data. Selected science results are discussed and used to highlight instrument capabilities. Science quantities, data quality and error sources, and analysis routines are documented.« less

Abstract Damage mechanism identification has scientific and practical ramifications for the structural health monitoring, design, and application of composite systems. Recent advances in machine learning uncover pathways to identify the waveform-damage mechanism relationship in higher-dimensional spaces for a comprehensive understanding of damage evolution. This review evaluates the state of the field, beginning with a physics-based understanding of acoustic emission waveform feature extraction, followed by a detailed overview of waveform clustering, labeling, and error analysis strategies. Fundamental requirements for damage mechanism identification in any machine learning framework, including those currently in use, under development, and yet to be explored, are discussed.

Brain activityisahighlynonlinearphenomenon,andassuch,dynamicalrelation- ships betweenneuronsandexpressedbehaviorshavebeendescribedonlowdi- mensional attractormanifoldsmostlythroughdimensionalityreductiontechniques. These rangefromsimpleprincipalcomponentanalysistosophisticatedsequen- tial recurrentvariationalautoencoderssuchasLFADS.Theselowdimensional representations i.e.theprincipalcomponentsofPCAorfactorsofavariational autoencoder,areadimensionallyreducedrepresentationoftheoriginalhighdimen- sional activityofthousandsofobservedneurons.Althoughtheselowdimensional manifolds offerunifyingviewsofglobalbrainactivitytheprincipalcomponentsof PCA reducedorfactorsofvariationalautoencodersarenotexperimentallytractable. ForexamplewithPCA,itisnotpossibletoexperimentallymanipulatethefactional amount oftheactivityofaparticularneuron(e.g.0.7ofneuron1,0.9ofneuron2, 0.3 ofneuron3etc...)thatcorrespondstoitsprojectiononaprincipalcomponent. Thus althoughtheselowdimensionalmanifoldsprovideappealingexplanatory power,itisultimatelydifficultifnotimpossibletoaddresstheseexperimentally.To solvethisproblem,here,weproposeanalgorithmgroundedindynamicalsystems theory thatgeneralizesmanifoldlearningfromaglobalstaterepresentation,toa networkoflocalinteractingmanifolds–termedaGenerativeManifoldNetwork (GMN). Manifoldsarediscoveredusingtheconvergentcrossmapping(CCM) causal inferencealgorithmwhicharethencompressedintoareducedredundancy network.Therepresentationisanetworkofmanifoldsembeddedfromobserva- tional datawhereeachorthogonalaxisofalocalmanifoldisanembeddingofa individuallyidentifiableneuronorbrainareathathasexactcorrespondenceinthe real world.Assuchthesecanbeexperimentallymanipulatedtotesthypotheses derivedfromtheoryanddataanalysis.Herewedemonstratethatthisrepresentation preservestheessentialfeaturesofthebrainofafly.Inadditiontoaccuratenear- term prediction,theGMNmodelcanbeusedtosynthesizerealistictimeseriesof whole brainneuronalactivityandlocomotionviewedoverthelongterm.Thus, as afinalvalidationofhowwellGMNcapturesessentialdynamicinformation, we showthattheartificiallygeneratedtimeseriescanbeusedasatrainingset to predictout-of-sampleobservedflylocomotion,aswellasbrainactivityinout of samplewithhelddatanotusedinmodelbuilding.Remarkably,theartificially generated timeseriesshowrealisticnovelbehaviorsthat do not existinthetraining data, butthat do existintheout-of-sampleobservationaldata.Thissuggeststhat GMN capturesinherentlyemergentpropertiesofthenetwork.Wesuggestour approach maybeagenericrecipeformappingtimeseriesobservationsofany complexnonlinearnetworkintoamodelthatisabletogeneratenaturalisticsystem behaviorsthatidentifiesvariablesthathaverealworldcorrespondenceandcanbe experimentallymanipulated.

In this work, we demonstrate that damage mechanism identification from acoustic emission (AE) signals generated in minicomposites with elastically similar constituents is possible. AE waveforms were generated by SiC/SiC ceramic matrix minicomposites (CMCs) loaded under uniaxial tension and recorded by four sensors (two models with each model placed at two ends). Signals were encoded with a modified partial power scheme and subsequently partitioned through spectral clustering. Matrix cracking and fiber failure were identified based on the frequency information contained in the AE event they produced, despite the similar constituent elastic properties of the matrix and fiber. Importantly, the resultant identification of AE events closely followed CMC damage chronology, wherein early matrix cracking is later followed by fiber breaks, even though the approach is fully domain-knowledge agnostic. Additionally, the partitions were highly precise across both the model and location of the sensors, and the partitioning was repeatable. The presented approach is promising for CMCs and other composite systems with elastically similar constituents.

Abstract

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Paleoceanography. The data include parameters of paleoceanography with a geographic location of Southern Ocean. The time period coverage is from 59000000 to 5000000 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Abstract We present panchromatic observations and modeling of calcium-strong supernovae (SNe) 2021gno in the star-forming host-galaxy NGC 4165 and 2021inl in the outskirts of elliptical galaxy NGC 4923, both monitored through the Young Supernova Experiment transient survey. The light curves of both, SNe show two peaks, the former peak being derived from shock cooling emission (SCE) and/or shock interaction with circumstellar material (CSM). The primary peak in SN 2021gno is coincident with luminous, rapidly decaying X-ray emission ( L x = 5 × 10 41 erg s −1 ) detected by Swift-XRT at δ t = 1 day after explosion, this observation being the second-ever detection of X-rays from a calcium-strong transient. We interpret the X-ray emission in the context of shock interaction with CSM that extends to r < 3 × 10 14 cm. Based on X-ray modeling, we calculate a CSM mass M CSM = (0.3−1.6) × 10 −3 M ⊙ and density n = (1−4) × 10 10 cm −3 . Radio nondetections indicate a low-density environment at larger radii ( r > 10 16 cm) and mass-loss rate of M ̇ < 10 − 4 M ⊙ yr −1 . SCE modeling of both primary light-curvemore »peaks indicates an extended-progenitor envelope mass M e = 0.02−0.05 M ⊙ and radius R e = 30−230 R ⊙ . The explosion properties suggest progenitor systems containing either a low-mass massive star or a white dwarf (WD), the former being unlikely given the lack of local star formation. Furthermore, the environments of both SNe are consistent with low-mass hybrid He/C/O WD + C/O WD mergers.« less