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Abstract The solar-type subgiantβHyi has long been studied as an old analog of the Sun. Although the rotation period has never been measured directly, it was estimated to be near 27 days. As a Southern Hemisphere target, it was not monitored by long-term stellar activity surveys, but archival International Ultraviolet Explorer data revealed a 12 yr activity cycle. Previous ground-based asteroseismology suggested that the star is slightly more massive and substantially larger and older than the Sun, so the similarity of both the rotation rate and the activity cycle period to solar values is perplexing. We use two months of precise time-series photometry from the Transiting Exoplanet Survey Satellite to detect solar-like oscillations inβHyi and determine the fundamental stellar properties from asteroseismic modeling. We also obtain a direct measurement of the rotation period, which was previously estimated from an ultraviolet activity–rotation relation. We then use rotational evolution modeling to predict the rotation period expected from either standard spin-down or weakened magnetic braking (WMB). We conclude that the rotation period ofβHyi is consistent with WMB and that changes in stellar structure on the subgiant branch can reinvigorate the large-scale dynamo and briefly sustain magnetic activity cycles. Our results support the existence of a “born-again” dynamo in evolved subgiants—previously suggested to explain the cycle in 94 Aqr Aa—which can best be understood within the WMB scenario. 

In this research we introduce the application of an optical fiber Fabry-Pérot interferometer in smart manufacturing. We used an optical fiber Fabry-Pérot interferometer to measure the distance between a moving target and a fixed optical fiber. When the target moves, the distance between the fiber and the target can be precisely determined. First, we monitored the distance between a fixed fiber and the surface of a rotating tool. By measuring the distance, we reconstructed the three-dimensional (3D) profile of the tool. We also introduce the method to calculate the runout and tool wear. To further improve the speed of this method, we developed machine learning models to find out the distance from the spectrum of the interferometer since the spectrum analyzing method is relatively slow. It was found that the Deep Neural Network model predicts the distance between the fiber and the target surface with a sufficient precision (< 4 μm) when measuring the straightness error of a computer numerical control (CNC) machine tool. The proposed method provides possibilities for noncontact precise monitoring especially in a limited space. 

Abstract Coastal upwelling, driven by alongshore winds and characterized by cold sea surface temperatures and high upper-ocean nutrient content, is an important physical process sustaining some of the oceans’ most productive ecosystems. To fully understand the ocean properties in eastern boundary upwelling systems, it is important to consider the depth of the source waters being upwelled, as it affects both the SST and the transport of nutrients toward the surface. Here, we construct an upwelling source depth distribution for parcels at the surface in the upwelling zone. We do so using passive tracers forced at the domain boundary for every model depth level to quantify their contributions to the upwelled waters. We test the dependence of this distribution on the strength of the wind stress and stratification using high-resolution regional ocean simulations of an idealized coastal upwelling system. We also present an efficient method for estimating the mean upwelling source depth. Furthermore, we show that the standard deviation of the upwelling source depth distribution increases with increasing wind stress and decreases with increasing stratification. These results can be applied to better understand and predict how coastal upwelling sites and their surface properties have and will change in past and future climates. 

Abstract Autophagy in eukaryotes functions to maintain homeostasis by degradation and recycling of long-lived and unwanted cellular materials. Autophagy plays important roles in pathogenicity of various fungal pathogens, suggesting that autophagy is a novel target for development of antifungal compounds. Here, we describe bioluminescence resonance energy transfer (BRET)-based high-throughput screening (HTS) strategy to identify compounds that inhibit fungal ATG4 cysteine protease-mediated cleavage of ATG8 that is critical for autophagosome formation. We identified ebselen (EB) and its analogs ebselen oxide (EO) and 2-(4-methylphenyl)−1,2-benzisothiazol-3(2H)-one (PT) as inhibitors of fungal pathogensBotrytis cinereaandMagnaporthe oryzaeATG4-mediated ATG8 processing. The EB and its analogs inhibit spore germination, hyphal development, and appressorium formation inAscomycotapathogens,B. cinerea, M. oryzae,Sclerotinia sclerotiorumandMonilinia fructicola. Treatment with EB and its analogs significantly reduced fungal pathogenicity. Our findings provide molecular insights to develop the next generation of antifungal compounds by targeting autophagy in important fungal pathogens. 

We consider an approach based on property rights mismatch to analyze conflict over radio spectrum. A mismatch occurs when the bundle of property rights created to enable social coordination fails to achieve this objective, leading to missed opportunities for productive exchange. With radio spectrum, these conflicts often result from technological changes that increase prospects (and satisfy demand for) sharing spectrum. Our focus is on how property regimes contribute to conflict as a result of mismatch, as well as how they might be resolved, for two examples of spectrum: passive and active spectrum uses and mobile services on the unlicensed band.