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Abstract The plate tectonic cycle produces chemically distinct mid-ocean ridge basalts and arc volcanics, with the latter enriched in elements such as Ba, Rb, Th, Sr and Pb and depleted in Nb owing to the water-rich flux from the subducted slab. Basalts from back-arc basins, with intermediate compositions, show that such a slab flux can be transported behind the volcanic front of the arc and incorporated into mantle flow. Hence it is puzzling why melts of subduction-modified mantle have rarely been recognized in mid-ocean ridge basalts. Here we report the first mid-ocean ridge basalt samples with distinct arc signatures, akin to back-arc basin basalts, from the Arctic Gakkel Ridge. A new high precision dataset for 576 Gakkel samples suggests a pervasive subduction influence in this region. This influence can also be identified in Atlantic and Indian mid-ocean ridge basalts but is nearly absent in Pacific mid-ocean ridge basalts. Such a hemispheric-scale upper mantle heterogeneity reflects subduction modification of the asthenospheric mantle which is incorporated into mantle flow, and whose geographical distribution is controlled dominantly by a “subduction shield” that has surrounded the Pacific Ocean for 180 Myr. Simple modeling suggests that a slab flux equivalent to ~13% of the output at arcs is incorporated into the convecting upper mantle. 

Power system state estimation (PSSE) aims at finding the voltage magnitudes and angles at all generation and load buses, using meter readings and other available information. PSSE is often formulated as a nonconvex and nonlinear least-squares (NLS) cost function, which is traditionally solved by the Gauss-Newton method. However, Gauss-Newton iterations for minimizing nonconvex problems are sensitive to the initialization, and they can diverge. In this context, we advocate a deep neural network (DNN) based “trainable regularizer” to incorporate prior information for accurate and reliable state estimation. The resulting regularized NLS does not admit a neat closed form solution. To handle this, a novel end-to-end DNN is constructed subsequently by unrolling a Gauss-Newton-type solver which alternates between least-squares loss and the regularization term. Our DNN architecture can further offer a suite of advantages, e.g., accommodating network topology via graph neural networks based prior. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation performance of the proposed scheme compared with state-of-the-art alternatives. Interestingly, our results suggest that a simple feed forward network based prior implicitly exploits the topology information hidden in data. 

Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control (a.k.a., policy) is parameterized in the present work by a deep neural network, whose unknown weights are updated by minimizing the accumulated power loss over a number of historical and simulated training pairs, using the policy gradient method. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the ‘optimal’ reactive power control decision with only several matrix-vector multiplications. The merits of this novel deep policy gradient approach include its computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real solar and consumption data corroborate these practical merits. 

Abstract El Niño–Southern Oscillation (ENSO) is an important but not the only source of interannual variability over the Indo–western Pacific. Non-ENSO forced variability in the region has received recent attention because of the implications for rainy-season prediction. Using a 35-member CESM1 Large Ensemble (CESM-LE) and 30 CMIP6 models, this study shows that the ensemble means project intensified interannual variability for precipitation, low-level winds, and sea level pressure under global warming, associated with the enhanced large-scale anomalous anticyclone (AAC) over the tropical northwestern (NW) Pacific after the ENSO signal is removed. A decomposition based on the column water vapor budget reveals that enhanced precipitation variability is due to the increased background specific humidity. The resultant anomalous diabatic heating intensifies the AAC, which further strengthens the precipitation anomalies. Over the tropical NW Pacific, the wind-induced evaporative cooling on the southeastern flank of the AAC is countered by the increased shortwave radiation due to the strengthened precipitation reduction. Tropospheric temperature anomalies in the ensemble means show no significant change, suggesting no apparent change of the interbasin positive feedback between the AAC and northern Indian Ocean SST. Intermodel analysis based on CMIP6 reveals that models with a larger increase in ENSO-unrelated precipitation variability over the NW Pacific are associated with stronger background warming in the eastern equatorial Pacific, due to the modulated Walker and Hadley circulations. 

ABSTRACT ATLASGAL is an 870-µm dust survey of 420 deg2 the inner Galactic plane and has been used to identify ∼10 000 dense molecular clumps. Dedicated follow-up observations and complementary surveys are used to characterize the physical properties of these clumps, map their Galactic distribution, and investigate the evolutionary sequence for high-mass star formation. The analysis of the ATLASGAL data is ongoing: We present an up-to-date version of the catalogue. We have classified 5007 clumps into four evolutionary stages (quiescent, protostellar, young stellar objects and H ii regions) and find similar numbers of clumps in each stage, suggesting a similar lifetime. The luminosity-to-mass (Lbol/Mfwhm) ratio curve shows a smooth distribution with no significant kinks or discontinuities when compared to the mean values for evolutionary stages indicating that the star formation process is continuous and that the observational stages do not represent fundamentally different stages or changes in the physical mechanisms involved. We compare the evolutionary sample with other star formation tracers (methanol and water masers, extended green objects and molecular outflows) and find that the association rates with these increases as a function of evolutionary stage, confirming that our classification is reliable. This also reveals a high association rate between quiescent sources and molecular outflows, revealing that outflows are the earliest indication that star formation has begun and that star formation is already ongoing in many of the clumps that are dark even at 70 µm. 

The increasingly sophisticated at-home screening systems for obstructive sleep apnea (OSA), integrated with both contactless and contact-based sensing modalities, bring convenience and reliability to remote chronic disease management. However, the device pairing processes between system components are vulnerable to wireless exploitation from a noncompliant user wishing to manipulate the test results. This work presents SIENNA, an insider-resistant context-based pairing protocol. SIENNA leverages JADE-ICA to uniquely identify a user’s respiration pattern within a multi-person environment and fuzzy commitment for automatic device pairing, while using friendly jamming technique to prevent an insider with knowledge of respiration patterns from acquiring the pairing key. Our analysis and test results show that SIENNA can achieve reliable (> 90% success rate) device pairing under a noisy environment and is robust against the attacker with full knowledge of the context information. 

The morphology of the Milky Way is still a matter of debate. In order to shed light on uncertainties surrounding the structure of the Galaxy, in this paper, we study the imprint of spiral arms on the distribution and properties of its molecular gas. To do so, we take full advantage of the SEDIGISM (Structure, Excitation, and Dynamics of the Inner Galactic Interstellar Medium) survey that observed a large area of the inner Galaxy in the 13 CO (2–1) line at an angular resolution of 28′′. We analyse the influences of the spiral arms by considering the features of the molecular gas emission as a whole across the longitude–velocity map built from the full survey. Additionally, we examine the properties of the molecular clouds in the spiral arms compared to the properties of their counterparts in the inter-arm regions. Through flux and luminosity probability distribution functions, we find that the molecular gas emission associated with the spiral arms does not differ significantly from the emission between the arms. On average, spiral arms show masses per unit length of ~10 5 –10 6 M ⊙ kpc −1 . This is similar to values inferred from data sets in which emission distributions were segmented into molecular clouds. By examining the cloud distribution across the Galactic plane, we infer that the molecular mass in the spiral arms is a factor of 1.5 higher than that of the inter-arm medium, similar to what is found for other spiral galaxies in the local Universe. We observe that only the distributions of cloud mass surface densities and aspect ratio in the spiral arms show significant differences compared to those of the inter-arm medium; other observed differences appear instead to be driven by a distance bias. By comparing our results with simulations and observations of nearby galaxies, we conclude that the measured quantities would classify the Milky Way as a flocculent spiral galaxy, rather than as a grand-design one. 

Context. The Central Molecular Zone (CMZ), a ∼200 pc sized region around the Galactic Centre, is peculiar in that it shows a star formation rate (SFR) that is suppressed with respect to the available dense gas. To study the SFR in the CMZ, young stellar objects (YSOs) can be investigated. Here we present radio observations of 334 2.2 μm infrared sources that have been identified as YSO candidates. Aims: Our goal is to investigate the presence of centimetre wavelength radio continuum counterparts to this sample of YSO candidates which we use to constrain the current SFR in the CMZ. Methods: As part of the GLObal view on STAR formation (GLOSTAR) survey, D-configuration Very Large Array data were obtained for the Galactic Centre, covering −2° < l < 2° and −1° < b < 1° with a frequency coverage of 4-8 GHz. We matched YSOs with radio continuum sources based on selection criteria and classified these radio sources as potential H II regions and determined their physical properties. Results: Of the 334 YSO candidates, we found 35 with radio continuum counterparts. We find that 94 YSOs are associated with dense dust condensations identified in the 870 μm ATLASGAL survey, of which 14 have a GLOSTAR counterpart. Of the 35 YSOs with radio counterparts, 11 are confirmed as H II regions based on their spectral indices and the literature. We estimated their Lyman continuum photon flux in order to estimate the mass of the ionising star. Combining these with known sources, the present-day SFR in the CMZ is calculated to be ∼0.068 M⊙ yr−1, which is ∼6.8% of the Galactic SFR. Candidate YSOs that lack radio counterparts may not have yet evolved to the stage of exhibiting an H II region or, conversely, are older and have dispersed their natal clouds. Since many lack dust emission, the latter is more likely. Our SFR estimate in the CMZ is in agreement with previous estimates in the literature.