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Project GOALS (Greater Opportunities to Advance Lifelong Success), an NSF Advanced Technological Education targeted research project, brought together researchers and community college educators from 2020 through 2024 to co-develop, test, package, and distribute resources for developing technicians' professional skills. Through this work, the team discovered the barriers that hinder both instructors and students from connecting around professional skills development. To address these barriers, the Project GOALS team developed an instructional framework grounded in research that integrates focused low-stakes activities into classes as students work toward their technical certificates. Based on our research, we believe Project GOALS provides ways for students to students' career readiness. In this research brief, the team describes how the Project GOALS co-development collaboration revealed the supports that technical instructors need to build students' professional awareness and reflection habits. Through qualitative analysis, Project GOALS researchers discovered the challenges that prevent many instructors from sharing their honest assessments of students' professional skills. The brief describes findings and recommends ways that community colleges can provide the assistance and resources that instructors need to develop students' professional skills. 

Experiments today can compress solids near isentropically to pressures approaching 100 × 106 atmospheres; however, determining the temperature of such matter remains a major challenge. Extended x-ray absorption fine-structure (EXAFS) spectroscopy is one of the few techniques sensitive to the bulk temperature of highly compressed solid matter, and the validity of this temperature measurement relies on constraining the local ion structure from the EXAFS spectrum. At high-energy-density (HED) conditions, the local ion structure often becomes distorted, which must be accounted for during the EXAFS analysis. Described here is a technique, using a parametrized ion-distribution model to directly analyze EXAFS spectra that provides a better constraint on the local structure than traditional second- or third-order cumulant expansion techniques at HED conditions. The parametrized ion-distribution model is benchmarked by analyzing EXAFS spectra from nickel molecular-dynamics simulations at ∼100 GPa and shown to provide a 10%–20% improvement in constraining the cumulants of the true ion distribution. 

Single-molecule fluorescence approaches have revolutionized biological and materials microscopy. However, many questions can only be addressed by multicolor imaging of multiple targets, a capability that is limited by the small subset of available, well-performing, and spectrally-distinct fluorescent probes. We recently introduced an alternative single-molecule multiplexing approach termed blinking-based multiplexing (BBM), wherein individual molecules are classified on the basis of their intrinsic blinking dynamics. We demonstrate accurate (>93.5%) binary classification of spectrally-overlapped rhodamine and quantum dot emitters using BBM, even when substantial blinking heterogeneity is observed. Classification can be accomplished using change point detection (CPD) analysis of blinking dynamics or a deep learning (DL) algorithm, the latter of which provides up to 96.6% accuracy. Here, we use CPD and DL algorithms to probe the excitation power, environmental, and molecular dependence of BBM. In addition to providing new opportunities in single-molecule spectroscopy and imaging, BBM represents a new take on single-molecule research, where blinking dynamics can be harnessed for more than just traditional localization or nanoreporting. 

Abstract Because of the extreme purity, lack of disorder, and complex order parameter, the first-order superfluid 3 He A–B transition is the leading model system for first order transitions in the early universe. Here we report on the path dependence of the supercooling of the A phase over a wide range of pressures below 29.3 bar at nearly zero magnetic field. The A phase can be cooled significantly below the thermodynamic A–B transition temperature. While the extent of supercooling is highly reproducible, it depends strongly upon the cooling trajectory: The metastability of the A phase is enhanced by transiting through regions where the A phase is more stable. We provide evidence that some of the additional supercooling is due to the elimination of B phase nucleation precursors formed upon passage through the superfluid transition. A greater understanding of the physics is essential before 3 He can be exploited to model transitions in the early universe. 

Ants have remarkably diverse diets and extraordinary species richness, making them an excellent model system to study the evolution of taste. In this entirely eusocial clade, food choice and the mechanisms that regulate feeding have both individual and social dimensions. How taste receptors and sensory processing drive food preferences to generate dietary breadth in ants is poorly understood. It is additionally unclear how elements of colony organization such as division of labor and social food flow impact the mechanistic basis and evolution of taste. Previous work on dipteran, lepidopteran, and hymenopteran gustatory systems, while foundational, provide limited insights into ant dietary specialization. Here we synthesize and analyze research on ant gustation to identify mechanisms, sociobiological correlates, and phylogenetic patterns. We discuss the current state of genomic analyses of taste and future research. We propose that strikingly polymorphic species of Pheidole , Cephalotes , Camponotus , and leafcutter ants ( Atta and Acromyrmex ) offer compelling social systems to explore adaptive variation in gustation because of their pronounced division of labor in which morphologically, behaviorally, and neurally differentiated workers vary in feeding behavior. Research on ant gustation within and among species will advance our understanding of sensory systems and provide insight into the impact of taste on the evolution of species diversity and how social organization influences gustation. 

We explore the statistical radio frequency interference (RFI) mitigation technique spectral kurtosis (SK) in the context of simulated realistic RFI signals. SK is a per-channel RFI detection metric that estimates the kurtosis of a collection of M power values in a single channel to discern between human-made RFI and incoherent astronomical signals of interest. We briefly test the ability of SK to flag signals with various representative modulation types, data rates, and duty cycles, as well as accumulation lengths M and multi-scale SK bin shapes. Multi-scale SK uses a rolling window to combine information from adjacent time-frequency pixels to mitigate weaknesses in single-scale SK. High data rate RFI signals with significant sidelobe emission are harder to flag, as well as signals with a 50% effective duty cycle. Multi-scale SK using at least one extra channel can detect both the center channel and side-band interference, flagging most of the signal at the expense of larger false positive rates. 

Abstract We investigate the effectiveness of the statistical radio frequency interference (RFI) mitigation technique spectral kurtosis ( SK ^ ) in the face of simulated realistic RFI signals. SK ^ estimates the kurtosis of a collection of M power values in a single channel and provides a detection metric that is able to discern between human-made RFI and incoherent astronomical signals of interest. We test the ability of SK ^ to flag signals with various representative modulation types, data rates, duty cycles, and carrier frequencies. We flag with various accumulation lengths M and implement multiscale SK ^ , which combines information from adjacent time-frequency bins to mitigate weaknesses in single-scale SK ^ . We find that signals with significant sidelobe emission from high data rates are harder to flag, as well as signals with a 50% effective duty cycle and weak signal-to-noise ratios. Multiscale SK ^ with at least one extra channel can detect both the center channel and sideband interference, flagging greater than 90% as long as the bin channel width is wider in frequency than the RFI. 

A cyclostationary process is one whose autocorrelation function is periodic or nearly periodic. The modulation schemes used to encode information give rise to cyclostationarity in many human-generated sources of interference. In contrast, nearly all astrophysical signals are expected to be wide-sense stationary on timescales of interest, making cyclostationarity a potentially robust way of discriminating between interference and astronomical sources. We are developing an algorithm that employs a well-known method of detecting cyclostationary signals and testing its efficacy against a suite of simulated interference covering a wide range of modulation schemes. We present receiver operating characteristic curves and binary classification scores for different types of interfering signals. Our algorithm performs well for many modulation schemes, with F1 and φ coefficient scores in excess of 0.9 in some cases, though it shows weaknesses in the case of frequency modulation. We also apply our algorithm to archived Robert C. Byrd Green Bank Telescope observations of a bright millisecond pulsar. We use standard pipelines for blindly detecting and timing pulsars and preliminarily find improvement in data quality according to several metrics, though some undesirable effects are still present. We also show that our algorithm has no negative impact when detecting Galactic HI emission. We thus believe that cyclostationary signal processing shows promise as a means of interference mitigation and discuss opportunities and challenges for employing it more widely.