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Additive manufacturing (AM) methods have become mainstream in many industry sectors, especially aeronautics and space structures, where production volume for components is low and designs are highly customized. The frequency of launching space missions is increasing around the world. Some of these missions are sending landers and rovers to moon, mars, and other planets. Such space structures require numerous parts that are unique in design or are produced in just one or a very small production run. Such parts produced for high stake and very expensive missions require complete confidence in the quality of each part. Characterization of parts manufactured by AM is a significant challenge for many existing methods due to the geometric complexity, feature size in the structure, and size of the part. This paper discusses various challenges in applying current characterization methods to the AM sector. Machine learning (ML) methods are considered promising in materials and manufacturing fields. However, generating the training dataset by creating a large number of parts is expensive and impractical. New methods are required to train the ML algorithms on small datasets, especially for parts of unique geometry that are produced in limited production run such as space structures. 

In this paper, we discuss the convergence analysis of the conjugate gradient-based algorithm for the functional linear model in the reproducing kernel Hilbert space framework, utilizing early stopping results in regularization against over-fitting. We establish the convergence rates depending on the regularity condition of the slope function and the decay rate of the eigenvalues of the operator composition of covariance and kernel operator. Our convergence rates match the minimax rate available from the literature. 

The tools and techniques such as imaging and machine learning used in the measurement of many material and microstructural properties are rapidly evolving. In metals, the grain size is routinely measured to estimate the yield strength. This paper describes some of the algorithms used in processing the microstructures to conduct quantitative measurements. The image processing methods provide the possibility to go beyond calculating the ASTM grain size number and calculate the actual surface area of each grain, grain boundary length, and the shape of the grains. The image analysis methods can be very helpful in conducting detailed quantitative analysis with greater accuracy than many labour-intensive manual methods currently in use. The work describes the complexities in applying the imaging methods and approaches in the metallurgical and materials fields. Successful application of such methods can reduce the time and effort required to characterise microstructures and can provide more precise information. 

Abstract A hallmark of many unconventional superconductors is the presence of many-body interactions that give rise to broken-symmetry states intertwined with superconductivity. Recent resonant soft X-ray scattering experiments report commensurate 3a₀charge density wave order in infinite-layer nickelates, which has important implications regarding the universal interplay between charge order and superconductivity in both cuprates and nickelates. Here we present X-ray scattering and spectroscopy measurements on a series of NdNiO_2+xsamples, which reveal that the signatures of charge density wave order are absent in fully reduced, single-phase NdNiO₂. The 3a₀superlattice peak instead originates from a partially reduced impurity phase where excess apical oxygens form ordered rows with three-unit-cell periodicity. The absence of any observable charge density wave order in NdNiO₂highlights a crucial difference between the phase diagrams of cuprate and nickelate superconductors. 

Abstract Among known neutron stars, the Vela pulsar is one of the best targets for gravitational-wave searches. It is also one of the most prolific in terms of glitches, which are sudden frequency changes in a pulsar’s rotation. Such glitches could cause a variety of transient gravitational-wave signals. Here, we search for signals associated with a Vela glitch on 2024 April 29 in data of the two LIGO detectors from the fourth LIGO–Virgo–KAGRA observing run. We search both for seconds-scale burst-like emission, primarily from fundamental (f-)mode oscillations, and for longer quasi-monochromatic transients up to 4 months in duration, primarily from quasi-static quadrupolar deformations. We find no significant detection candidates, but for the first time we set direct observational upper limits on gravitational strain amplitude that are stricter than what can be indirectly inferred from the overall glitch energy scale. We discuss the short- and long-duration observational constraints in the context of specific emission models. These results demonstrate the potential of gravitational-wave probes of glitching pulsars as detector sensitivity continues to improve. 

Abstract Version 4.0 of the Gravitational-Wave Transient Catalog (GWTC-4.0) adds new candidates detected by the LIGO, Virgo, and KAGRA observatories through the first part of the fourth observing run (O4a: 2023 May 24 15:00:00 to 2024 January 16 16:00:00 UTC) and a preceding engineering run. In these new data, we find 128 compact binary coalescence candidates that are identified by at least one of our search algorithms with a probability of astrophysical originp_astro≥ 0.5 and that are not vetoed during event validation. We also provide detailed source property measurements for 86 of these that have a false-alarm rate <1 yr⁻¹. Based on the inferred component masses, these candidates are consistent with signals from binary black holes (BBHs) and neutron star–black hole binaries (GW230518_125908 and GW230529_181500). Median-inferred component masses of BBHs in the catalog now range from 5.79M⊙ (GW230627_015337) to 137 $M_{\odot }$(GW231123_135430), while GW231123_135430 was probably produced by the most massive binary observed in the catalog. For the first time, we have discovered BBH signals with network signal-to-noise ratio exceeding 30, GW230814_230901 and GW231226_101520, enabling high-fidelity studies of the waveforms and astrophysical properties of these systems. Combined with the 90 candidates included in GWTC-3.0, the catalog now contains 218 candidates withp_astro≥ 0.5 and not otherwise vetoed, more than doubling the size of the catalog and further opening our view of the gravitational-wave Universe. 

Abstract LIGO, Virgo, and KAGRA form a network of gravitational-wave observatories. Data and analysis results from this network are made publicly available through the Gravitational Wave Open Science Center. This paper describes open data from this network, including the addition of data from the first part of the fourth observing run and selected periods from the preceding engineering run, collected from 2023 May to 2024 January. The public dataset includes calibrated strain time series for each instrument, data from additional channels used for noise subtraction and detector characterization, and analysis data products from version 4.0 of the Gravitational-Wave Transient Catalog.