Traditional active flutter suppression controllers are designed based on model. However, as the aircraft becomes more and more powerful, the modeling of aeroelastic system becomes difficult and the model-free requirement of controller design becomes more and more urgent. The complexity of industrial processes has brought about massive operational data generated online. Aviation industry development has entered the era of big data. Breaking through the traditional theoretical framework, mining the correlation, evolution and dynamic characteristics of the system from the data is the inevitable choice to meet this demand. In this paper, a data-driven model-free controller is designed, which relies on ridge regression of the input and output variation at each operating point of the closed-loop controlled system to recursively derive the iterative format of the control signals and ensure the numerical stability of the signals. The controller can only use the real-time measurement of the system’s online input and output data for continuous correction, to achieve the purpose of flutter suppression. Then flutter suppression of a three-degree-of-freedom binary wing with a control surface is studied, and the superiority of model-free controller is demonstrated by comparing it with the optimal controller.

Applying a coastal-geoarchaeological approach, we synthesize stratigraphic, sedimentological, mollusk-zooarchaeological, and radiometric datasets from recent excavations and sediment coring at Harbor Key (8MA15)—a shell-terraformed Native mound complex within Tampa Bay, on the central peninsular Gulf Coast of Florida. We significantly revise the chronological understanding of the site and place it among the relatively few early civic-ceremonial centers in the region. Analyses of submound contexts revealed that the early first millennium mound center was constructed atop a platform of sand and ex situ cultural shell deposits that were reworked during ancient storm landfalls around 2000 BP. We situate Harbor Key within a seascape-scale stratigraphic and paleoenvironmental framework and show that the shellworks comprise an artificial barrier protecting the leeward estuary basin (and productive inshore wetlands) from high-energy conditions of the open bay and swells from the Gulf of Mexico. The sedimentary and archaeological records attest to the long-term history of morphodynamic interaction between coastal processes and Indigenous shell terraforming in the region and suggest that early first millennium mound building in Tampa Bay was tied to the recognition and reuse of antecedent shellworks and the persistent management of encompassing cultural seascapes.

The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25–35 $\mu$Jy beam$^{-1}$. We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP$_{50}$ of 67.5% and 76.8% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link: https://github.com/Nikhel1/Gal-CAM

Over the past decade, transdiagnostic indicators in relation to neurobiological processes have provided extensive insight into youth’s risk for psychopathology. During development, exposure to childhood trauma and dysregulation (i.e., so-called AAA symptomology: anxiety, aggression, and attention problems) puts individuals at a disproportionate risk for developing psychopathology and altered network-level neural functioning. Evidence for the latter has emerged from resting-state fMRI studies linking mental health symptoms and aberrations in functional networks (e.g., cognitive control (CCN), default mode networks (DMN)) in youth, although few of these investigations have used longitudinal designs. Herein, we leveraged a three-year longitudinal study to identify whether traumatic exposures and concomitant dysregulation trigger changes in the developmental trajectories of resting-state functional networks involved in cognitive control (N = 190; 91 females; time 1 Mage = 11.81). Findings from latent growth curve analyses revealed that greater trauma exposure predicted increasing connectivity between the CCN and DMN across time. Greater levels of dysregulation predicted reductions in within-network connectivity in the CCN. These findings presented in typically developing youth corroborate connectivity patterns reported in clinical populations, suggesting there is predictive utility in using transdiagnostic indicators to forecast alterations in resting-state networks implicated in psychopathology.

In recent years, there has been significant momentum in applying deep learning (DL) to machine health monitoring (MHM). It has been widely claimed that DL methodologies are superior to more traditional techniques in this area. This paper aims to investigate this claim by analysing a real-world dataset of helicopter sensor faults provided by Airbus. Specifically, we will address the problem of machine sensor health unsupervised classification. In a 2019 worldwide competition hosted by Airbus, Fujitsu Systems Europe (FSE) won first prize by achieving an F1-score of 93% using a DL model based on generative adversarial networks (GAN). In another comprehensive study, various modified and existing image encoding methods were compared for the convolutional auto-encoder (CAE) model. The best classification result was achieved using the scalogram as the image encoding method, with an F1-score of 91%. In this paper, we use these two studies as benchmarks to compare with basic statistical analysis methods and the one-class supporting vector machine (SVM). Our comparative study demonstrates that while DL-based techniques have great potential, they are not always superior to traditional methods. We therefore recommend that all future published studies of applying DL methods to MHM include appropriately selected traditional reference methods, wherever possible.

The Australian SKA Pathfinder (ASKAP) is being used to undertake a campaign to rapidly survey the sky in three frequency bands across its operational spectral range. The first pass of the Rapid ASKAP Continuum Survey (RACS) at 887.5 MHz in the low band has already been completed, with images, visibility datasets, and catalogues made available to the wider astronomical community through the CSIRO ASKAP Science Data Archive (CASDA). This work presents details of the second observing pass in the mid band at 1367.5 MHz, RACS-mid, and associated data release comprising images and visibility datasets covering the whole sky south of $\delta_{\text{J2000}}=+49^\circ$. This data release incorporates selective peeling to reduce artefacts around bright sources, as well as accurately modelled primary beam responses. The Stokes I images reach a median noise of 198 $\mu$Jy PSF$^{-1}$ with a declination-dependent angular resolution of 8.1–47.5 arcsec that fills a niche in the existing ecosystem of large-area astronomical surveys. We also supply Stokes V images after application of a widefield leakage correction, with a median noise of 165 $\mu$Jy PSF$^{-1}$. We find the residual leakage of Stokes I into V to be $\lesssim 0.9$–$2.4$% over the survey. This initial RACS-mid data release will be complemented by a future release comprising catalogues of the survey region. As with other RACS data releases, data products from this release will be made available through CASDA.

The myriad applications of artificial intelligence (AI) by the private and public sectors have exploded in the public consciousness in the postpandemic period. However, researchers and businesses have been working on AI technology applications for decades, and in many ways, governments are rushing to catch up. This article presents an argument that the future of AI policy in the United States will be driven in large part by current and future state-level policy experiments. This argument is presented by drawing on scholarship surrounding federalism, regulatory fragmentation, and the effects of fragmentation on business and social equity. The article then presents the case of autonomous vehicle policy in the states to illustrate the degree of current fragmentation and considers the effects of layering new AI policies on top of existing rules surrounding privacy, licensing, and more. Following this consideration of existing research and its application of AI policy, the article presents a research agenda for leveraging state differences to study the effects of AI policy and develop a cohesive framework for governing AI.

Many Aboriginal Australian communities are undergoing language shift from traditional Indigenous languages to contact varieties such as Kriol, an English-lexified Creole. Kriol is reportedly characterised by lexical items with highly variable phonological specifications, and variable implementation of voicing and manner contrasts in obstruents (Sandefur, 1986). A language, such as Kriol, characterised by this unusual degree of variability presents Kriol-acquiring children with a potentially difficult language-learning task, and one which challenges the prevalent theories of acquisition. To examine stop consonant acquisition in this unusual language environment, we present a study of Kriol stop and affricate production, followed by a mispronunciation detection study, with Kriol-speaking children (ages 4-7) from a Northern Territory community where Kriol is the lingua franca. In contrast to previous claims, the results suggest that Kriol-speaking children acquire a stable phonology and lexemes with canonical phonemic specifications, and that English experience would not appear to induce this stability.

In this paper, an adaptive neural output-constrained control algorithm is proposed for a class of non-affine kinetic kill vehicle (KKV) systems. The key point is that the non-affine control law can be designed and the output of the KKV system conform to the output limit with the aid of the proposed method. Due to the aerodynamic moments, the actual control torque is non-affine, which can be addressed by introducing an integral process to the design of the controller. Besides, in order to improve the control precision, a nonlinear mapping is put forward so that the output constraint can be transformed to the constraint of the introduced dynamic signal that can be simply achieved. From the simulation results it can be concluded that the states of the KKV system can track the desired trajectories in spite of different working conditions and the control precision is higher compared with other control methods.

With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to $z = 3$ before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify $\approx$76% of the highest redshift sources—sources at redshift $z > 2.51$—as being in either the highest bin ($z > 2.51$) or second highest ($z = 2.25$).

We present the third data release from the Parkes Pulsar Timing Array (PPTA) project. The release contains observations of 32 pulsars obtained using the 64-m Parkes “Murriyang” radio telescope. The data span is up to 18 years with a typical cadence of 3 weeks. This data release is formed by combining an updated version of our second data release with ∼ 3 years of more recent data primarily obtained using an ultra-wide-bandwidth receiver system that operates between 704 and 4032 MHz. We provide calibrated pulse profiles, flux-density dynamic spectra, pulse times of arrival, and initial pulsar timing models. We describe methods for processing such wide-bandwidth observations, and compare this data release with our previous release.

We examined the effect of an antimicrobial stewardship program (ASP), procalcitonin testing and rapid blood-culture identification on hospital mortality in a prospective quality improvement project in critically ill septic adults. Secondarily, we have reported antimicrobial guideline concordance, acceptance of ASP interventions, and antimicrobial and health-resource utilization.

For 147 hospital-onset bloodstream infections, we assessed the sensitivity, specificity, positive predictive value, and negative predictive value of the National Healthcare Safety Network surveillance definitions of central-line–associated bloodstream infections against the gold standard of physician review, examining the drivers of discrepancies and related implications for reporting and infection prevention.

We investigate the diversity in the sizes and average surface densities of the neutral atomic hydrogen (H i) gas discs in $\sim$280 nearby galaxies detected by the Widefield ASKAP L-band Legacy All-sky Blind Survey (WALLABY). We combine the uniformly observed, interferometric H i data from pilot observations of the Hydra cluster and NGC 4636 group fields with photometry measured from ultraviolet, optical, and near-infrared imaging surveys to investigate the interplay between stellar structure, star formation, and H i structural parameters. We quantify the H i structure by the size of the H i relative to the optical disc and the average H i surface density measured using effective and isodensity radii. For galaxies resolved by $>$$1.3$ beams, we find that galaxies with higher stellar masses and stellar surface densities tend to have less extended H i discs and lower H i surface densities: the isodensity H i structural parameters show a weak negative dependence on stellar mass and stellar mass surface density. These trends strengthen when we limit our sample to galaxies resolved by $>$2 beams. We find that galaxies with higher H i surface densities and more extended H i discs tend to be more star forming: the isodensity H i structural parameters have stronger correlations with star formation. Normalising the H i disc size by the optical effective radius (instead of the isophotal radius) produces positive correlations with stellar masses and stellar surface densities and removes the correlations with star formation. This is due to the effective and isodensity H i radii increasing with mass at similar rates while, in the optical, the effective radius increases slower than the isophotal radius. Our results are in qualitative agreement with previous studies and demonstrate that with WALLABY we can begin to bridge the gap between small galaxy samples with high spatial resolution H i data and large, statistical studies using spatially unresolved, single-dish data.