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Developing sustainable urban systems is a fundamental societal challenge for the 21st century, and central Texas faces particularly synergistic challenges of a rapidly growing urban population and a projected increasingly drought-prone climate. To assess the history of urbanization impacts on watersheds here, we analyzed 51 cores from bald cypress trees in paired urban and rural watersheds in Austin, Texas. We find a significant contrast between rural and urbanized watersheds. In the rural watershed, tree-ring-width growth histories (“chronologies”) from 1844–2018 significantly and positively correlate (p < 0.01) with (1) one another, and (2) regional instrumental and proxy records of drought. In the urbanized watershed, by contrast, chronologies weakly correlate with one another, with instrumental records of drought, and with the rural chronologies and regional records. Relatively weak drought limitations to urban tree growth are consistent with the significant present-day transfer of municipal water from urban infrastructure by leakage and irrigation to the natural hydrologic system. We infer a significant, long-term contribution from infrastructure to baseflow in urbanized watersheds. In contrast to the common negative impacts of ‘urban stream syndrome’, such sustained baseflow in watersheds with impaired or failing infrastructure may be an unintended positive consequence for stream ecosystems, as a mitigation against projected extended 21st-century droughts. Additionally, riparian trees may serve as a proxy for past impacts of urbanization on natural streams, which may inform sustainable urban development.

 

Radio frequency (RF) magnetometers based on nitrogen vacancy centers in diamond are predicted to offer femtotesla sensitivity, but previous experiments were limited to the picotesla level. We demonstrate a femtotesla RF magnetometer using a diamond membrane inserted between ferrite flux concentrators. The device provides ~300-fold amplitude enhancement for RF magnetic fields from 70 kHz to 3.6 MHz, and the sensitivity reaches ~70 fT√s at 0.35 MHz. The sensor detected the 3.6-MHz nuclear quadrupole resonance (NQR) of room-temperature sodium nitrite powder. The sensor’s recovery time after an RF pulse is ~35 μs, limited by the excitation coil’s ring-down time. The sodium-nitrite NQR frequency shifts with temperature as −1.00±0.02 kHz/K, the magnetization dephasing time isT₂*=887±51 μs, and multipulse sequences extend the signal lifetime to 332±23 ms, all consistent with coil-based studies. Our results expand the sensitivity frontier of diamond magnetometers to the femtotesla range, with potential applications in security, medical imaging, and materials science.

 

With two central galaxies engaged in a major merger and a remarkable chain of 19 young stellar superclusters wound around them in projection, the galaxy cluster SDSS J1531+3414 (z= 0.335) offers an excellent laboratory to study the interplay between mergers, active galactic nucleus (AGN) feedback, and star formation. New Chandra X-ray imaging reveals rapidly cooling hot (T∼ 10⁶K) intracluster gas, with two “wings” forming a concave density discontinuity near the edge of the cool core. LOFAR 144 MHz observations uncover diffuse radio emission strikingly aligned with the “wings,” suggesting that the “wings” are actually the opening to a giant X-ray supercavity. The steep radio emission is likely an ancient relic of one of the most energetic AGN outbursts observed, with 4pV> 10⁶¹erg. To the north of the supercavity, GMOS detects warm (T∼ 10⁴K) ionized gas that enshrouds the stellar superclusters but is redshifted up to +800 km s⁻¹with respect to the southern central galaxy. The Atacama Large Millimeter/submillimeter Array detects a similarly redshifted ∼10¹⁰M_⊙reservoir of cold (T∼ 10²K) molecular gas, but it is offset from the young stars by ∼1–3 kpc. We propose that the multiphase gas originated from low-entropy gas entrained by the X-ray supercavity, attribute the offset between the young stars and the molecular gas to turbulent intracluster gas motions, and suggest that tidal interactions stimulated the “beads-on-a-string” star formation morphology.

 

We investigated the optimal number of independent parameters required to accurately represent spectral remote sensing reflectances (R_rs) by performing principal component analysis on quality controlledin situand syntheticR_rsdata. We found that retrieval algorithms should be able to retrieve no more than four free parameters fromR_rsspectra for most ocean waters. In addition, we evaluated the performance of five different bio-optical models with different numbers of free parameters for the direct inversion of in-water inherent optical properties (IOPs) fromin situand syntheticR_rsdata. The multi-parameter models showed similar performances regardless of the number of parameters. Considering the computational cost associated with larger parameter spaces, we recommend bio-optical models with three free parameters for the use of IOP or joint retrieval algorithms.

 

Mentoring relationships in academia have traditionally been described as a dyad between a mentor and a mentee. The mentor provides the mentee with both technical and psychosocial support to move toward obtaining their Ph.D. or gaining tenure and promotion. While there is an embedded assumption that mentoring is best suited when people of common background support each other, scholars must consider the impact of mentoring across differences. Given the lack of diversity among senior faculty members in science education, and given the increasing diversity represented among graduate students and early career scholars in science education, inevitably mentoring relationships will be formed across differences in identities (race, ethnicity, gender, sexuality, ability). In our essay, we focus on two focal points: we explore those aspects of mentoring relationships that research suggests are critical to the success of relationships built across differences, foregrounding lessons science education researchers can take from this literature and arguing that it is too much to expect one individual mentor to provide to any given mentee. Next, we propose a brokering framework, leveraging technological advances, to work toward more transformative mentoring outcomes at scale, particularly when mentoring across differences. 

Ground truth depth information is necessary for many computer vision tasks. Collecting this information is chal-lenging, especially for outdoor scenes. In this work, we propose utilizing single-view depth prediction neural networks pre-trained on synthetic scenes to generate relative depth, which we call pseudo-depth. This approach is a less expen-sive option as the pre-trained neural network obtains ac-curate depth information from synthetic scenes, which does not require any expensive sensor equipment and takes less time. We measure the usefulness of pseudo-depth from pre-trained neural networks by training indoor/outdoor binary classifiers with and without it. We also compare the difference in accuracy between using pseudo-depth and ground truth depth. We experimentally show that adding pseudo-depth to training achieves a 4.4% performance boost over the non-depth baseline model on DIODE, a large stan-dard test dataset, retaining 63.8% of the performance boost achieved from training a classifier on RGB and ground truth depth. It also boosts performance by 1.3% on another dataset, SUN397, for which ground truth depth is not avail-able. Our result shows that it is possible to take information obtained from a model pre-trained on synthetic scenes and successfully apply it beyond the synthetic domain to real-world data.