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Objective. This work proposes, for the first time, an image-based end-to-end self-normalization framework for positron emission tomography (PET) using conditional generative adversarial networks (cGANs). Approach. We evaluated different approaches by exploring each of the following three methodologies. First, we used images that were either unnormalized or corrected for geometric factors, which encompass all time-invariant factors, as input data types. Second, we set the input tensor shape as either a single axial slice (2D) or three contiguous axial slices (2.5D). Third, we chose either Pix2Pix or polarized self-attention (PSA) Pix2Pix, which we developed for this work, as a deep learning network. The targets for all approaches were the axial slices of images normalized using the direct normalization method. We performed Monte Carlo simulations of ten voxelized phantoms with the SimSET simulation tool and produced 26,000 pairs of axial image slices for training and testing. Main results. The results showed that 2.5D PSA Pix2Pix trained with geometric-factors-corrected input images achieved the best performance among all the methods we tested. All approaches improved general image quality figures of merit peak signal to noise ratio (PSNR) and structural similarity index (SSIM) from ∼15 % to ∼55 %, and 2.5D PSA Pix2Pix showed the highest PSNR (28.074) and SSIM (0.921). Lesion detectability, measured with region of interest (ROI) PSNR, SSIM, normalized contrast recovery coefficient, and contrast-to-noise ratio, was generally improved for all approaches, and 2.5D PSA Pix2Pix trained with geometric-factors-corrected input images achieved the highest ROI PSNR (28.920) and SSIM (0.973). Significance. This study demonstrates the potential of an image-based end-to-end self-normalization framework using cGANs for improving PET image quality and lesion detectability without the need for separate normalization scans. [ABSTRACT FROM AUTHOR]

UVCANDELS is a Hubble Space Telescope Cycle-26 Treasury Program awarded 164 orbits of primary ultraviolet (UV) F275W imaging and coordinated parallel optical F435W imaging in four CANDELS fields—GOODS-N, GOODS-S, EGS, and COSMOS—covering a total area of ∼426 arcmin2. This is ∼2.7 times larger than the area covered by previous deep-field space UV data combined, reaching a depth of about 27 and 28 ABmag (5 σ in 0."2 apertures) for F275W and F435W, respectively. Along with new photometric catalogs, we present an analysis of the rest-frame UV luminosity function (LF), relying on our UV-optimized aperture photometry method, yielding a factor of 1.5 increase over H-isophot aperture photometry in the signal-to-noise ratios of galaxies in our F275W imaging. Using well-tested photometric redshift measurements, we identify 5810 galaxies at redshifts 0.6 < z < 1, down to an absolute magnitude of M UV = −14.2. In order to minimize the effect of uncertainties in estimating the completeness function, especially at the faint end, we restrict our analysis to sources above 30% completeness, which provides a final sample of 4726 galaxies at −21.5 < M UV < −15.5. We performed a maximum likelihood estimate to derive the best-fit parameters of the UV LF. We report a best-fit faint-end slope of α = − 1.359 − 0.041 + 0.041 at z ∼ 0.8. Creating subsamples at z ∼ 0.7 and z ∼ 0.9, we observe a possible evolution of α with redshift. The unobscured UV luminosity density at M UV < −10 is derived as ρ U V = 1.339 − 0.030 + 0.027 (× 10 26 e r g s − 1 H z − 1 M p c − 3) using our best-fit LF parameters. The new F275W and F435 photometric catalogs from UVCANDELS have been made publicly available on the Barbara A. Mikulski Archive for Space Telescopes. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries content complex internal components, such as porous media and electrolytes, which result in strong scattering and high attenuation of ultrasonic waves in these batteries. The low attenuative feature of the quasi-static components (QSCs) of ultrasonic waves offers great potential for nondestructive assessment of highly attenuating and porous materials. This paper presents an innovative approach for estimating the state-of-charge (SOC) of lithium-ion batteries using QSC of ultrasonic waves. Experimental results demonstrate a clear and repeatable linear relationship between the amplitudes of the generated QSC and the SOC of lithium-ion batteries. In addition, the relationships between different SOCs of the battery and the conventional linear ultrasonic parameters, second harmonic generation (SHG), and the QSC were compared to verify the improved sensitivity of the proposed approach. Notably, compared to linear ultrasonic features and the SHG, the generated QSC shows much higher sensitivity to the variations of SOC. We employ the phase-reversal method to further enhance the signal-to-noise ratio of measured QSC signals. The experimental results demonstrate that the proposed method exhibits a heightened sensitivity to changes in the SOC of batteries, resulting in significantly enhanced detection accuracy and resolution. This method effectively addresses the deficiencies observed in the current detection methods such as limited accuracy and sluggish response times. This method provides a new solution to overcome this challenge. Meanwhile, it also confirms that nonlinear ultrasound promises an alternative method for SOC assessment, providing a foundation for efficient and safe battery management practices. [ABSTRACT FROM AUTHOR]

Pulsar timing arrays (PTAs) are effective in detecting low-frequency gravitational waves (GWs), especially in circular binary systems of supermassive black holes. To evaluate the effectiveness and accuracy of PTAs in searching for GW parameter estimation, we use the Cramer-Rao lower bound to estimate the parameters of the Virgo source under various search conditions, including both evolving and non-evolving scenarios, as well as Earth and pulsar term searches, and only Earth term searches. The results show that the estimation accuracy of the inclination angle and GW strain is lower in the evolving condition than in the non-evolving condition because the two parameters participate in the evolving process. In addition, we find that the parameters estimable relates to signal-to-noise ratio, which is beneficial for reducing the dimensionality in the search process. In summary, our method serves as a reference for exploring lower parameter limits under different conditions and assists in assessing the optimization of parameter estimation in GW detection algorithms. This helps lay the theoretical foundation for advances in the field of GWs and the optimization of PTAs. [ABSTRACT FROM AUTHOR]

This research aimed to investigate the accuracy of picking of P-wave arrival times in rock fracture acoustic emission signals. In order to simulate the mining scenario, Gaussian white noise and pulse noise were added to the data collected in the laboratory. Complete ensemble empirical mode decomposition with adaptive noise + Wavelet (CEEMDAN + Wavelet) was improved in this paper, where the Spearman rank correlation coefficient was adopted to effectively select intrinsic mode functions for denoising which retained the inherent characteristics of the rock fracture signal. The absolute amplitude and energy change rate of the envelope signal, calculated based on the Hilbert transform, were used as the input of the short term average/long term average (STA/LTA) normalization algorithm to pickup the P-wave arrival time. The reliability of this method was tested on 30 groups of recorded rock fracture laboratory data and 60 groups of added noise data. Taking the manual pickup results as the standard, the errors of CEEMDAN + Wavelet + STA/LTA + AIC (Akaike information criterion) method with the absolute amplitude of the signal as the input are all within 10 ms, and 86.67% of the results are within 5 ms. The method proposed in this paper effectively addressing the issue of false pickup caused by the sensitivity of AIC and traditional STA/LTA method for strong noise, and achieving relatively high accuracy and stability in processing low signal-to-noise ratio signals. This work contributes to monitor microscopic changes in rock bodies and is of great significance for the prediction and monitoring of geological disasters. [ABSTRACT FROM AUTHOR]

Line intensity mapping (LIM) serves as a potent probe in astrophysics, relying on the statistical analysis of integrated spectral line emissions originating from distant star-forming galaxies. While LIM observations hold the promise of achieving a broad spectrum of scientific objectives, a significant hurdle for future experiments lies in distinguishing the targeted spectral line emitted at a specific redshift from undesired line emissions originating at different redshifts. The presence of these interloping lines poses a challenge to the accuracy of cosmological analyses. In this study, we introduce a novel approach to quantify line–line cross-correlations (LIM-LLX), enabling us to investigate the target signal amid instrumental noise and interloping emissions. For example, at a redshift of z ∼ 3.7, we observed that the measured auto-power spectrum of C ii 158 exhibited substantial bias, from interloping line emission. However, cross-correlating C ii 158 with CO(6–5) lines using an FYST-like experiment yielded a promising result, with a signal-to-noise ratio of ∼10. This measurement is notably unbiased. Additionally, we explore the extensive capabilities of cross-correlation by leveraging various CO transitions to probe the tomographic Universe at lower redshifts through LIM-LLX. We further demonstrate that incorporating low-frequency channels, such as 90 and 150 GHz, into FYST's EoR-Spec-like experiment can maximize the potential for cross-correlation studies, effectively reducing the bias introduced by instrumental noise and interlopers. [ABSTRACT FROM AUTHOR]

Multi-source domain generalization provides a good idea for intelligent fault diagnosis of unseen machines. However, the acquisition of multi-source training sets would not only affect the generalization effect, but also cause huge economic loss. Too much difference between source training sets would lead to negative transfer, while too much similarity between the sets would cause zero transfer. Simultaneously, the current methods based on the assumption of stable speed cannot realize intelligent diagnosis under varying speed. Therefore, a semi-physical simulation-driven contrastive decoupling network is presented. First, a phenomenological model is used to generate large amount of simulation data with different modulation frequencies and signal-to-noise ratios to provide support for the training of the net. Next, with the joint help of simulation data and physical data, a contrastive framework is introduced to extract domain-independent features. Finally, a decoupling framework is proposed to minimize speed-related information while maximizing health-related features, thereby enabling intelligent diagnosis of unseen devices at varying speed. The proposed method is tested on two cases operating under sharp speed variation and compared with some state-of-the-art methods. The results show the superiority of the proposed net in intelligent diagnosis of unseen equipment under drastically varying speed. [ABSTRACT FROM AUTHOR]

Accurate modeling of selection effects is a key ingredient to the success of gravitational-wave astronomy. The detection probability plays a crucial role in both statistical population studies, where it enters the hierarchical Bayesian likelihood, and astrophysical modeling, where it is used to convert predictions from population-synthesis codes into observable distributions. We review the most commonly used approximations, extend them, and present some recipes for a straightforward implementation. These include a closed-form expression capturing both multiple detectors and noise realizations written in terms of the so-called Marcum Q -function and a ready-to-use mapping between signal-to-noise ratio (SNR) thresholds and false-alarm rates from state-of-the-art detection pipelines. The bias introduced by approximating the matched filter SNR with the optimal SNR is not symmetric: sources that are nominally below threshold are more likely to be detected than sources above threshold are to be missed. Using both analytical considerations and software injections in detection pipelines, we confirm that including noise realizations when estimating the selection function introduces an average variation of a few %. This effect is most relevant for large catalogs and specific subpopulations of sources at the edge of detectability (e.g. high redshifts). [ABSTRACT FROM AUTHOR]

We evaluate the performance of the Legacy Survey of Space and Time Science Pipelines Difference Image Analysis (DIA) on simulated images. By adding synthetic sources to galaxies on images, we trace the recovery of injected synthetic sources to evaluate the pipeline on images from the Dark Energy Science Collaboration Data Challenge 2. The pipeline performs well, with efficiency and flux accuracy consistent with the signal-to-noise ratio of the input images. We explore different spatial degrees of freedom for the Alard–Lupton polynomial-Gaussian image subtraction kernel and analyze for trade-offs in efficiency versus artifact rate. Increasing the kernel spatial degrees of freedom reduces the artifact rate without loss of efficiency. The flux measurements with different kernel spatial degrees of freedom are consistent. We also here provide a set of DIA flags that substantially filter out artifacts from the DIA source table. We explore the morphology and possible origins of the observed remaining subtraction artifacts and suggest that given the complexity of these artifact origins, a convolution kernel with a set of flexible bases with spatial variation may be needed to yield further improvements. [ABSTRACT FROM AUTHOR]

Objective. Multi-contrast magnetic resonance imaging (MC MRI) can obtain more comprehensive anatomical information of the same scanning object but requires a longer acquisition time than single-contrast MRI. To accelerate MC MRI speed, recent studies only collect partial k-space data of one modality (target contrast) to reconstruct the remaining non-sampled measurements using a deep learning-based model with the assistance of another fully sampled modality (reference contrast). However, MC MRI reconstruction mainly performs the image domain reconstruction with conventional CNN-based structures by full supervision. It ignores the prior information from reference contrast images in other sparse domains and requires fully sampled target contrast data. In addition, because of the limited receptive field, conventional CNN-based networks are difficult to build a high-quality non-local dependency. Approach. In the paper, we propose an Image-Wavelet domain ConvNeXt-based network (IWNeXt) for self-supervised MC MRI reconstruction. Firstly, INeXt and WNeXt based on ConvNeXt reconstruct undersampled target contrast data in the image domain and refine the initial reconstructed result in the wavelet domain respectively. To generate more tissue details in the refinement stage, reference contrast wavelet sub-bands are used as additional supplementary information for wavelet domain reconstruction. Then we design a novel attention ConvNeXt block for feature extraction, which can capture the non-local information of the MC image. Finally, the cross-domain consistency loss is designed for self-supervised learning. Especially, the frequency domain consistency loss deduces the non-sampled data, while the image and wavelet domain consistency loss retain more high-frequency information in the final reconstruction. Main results. Numerous experiments are conducted on the HCP dataset and the M4Raw dataset with different sampling trajectories. Compared with DuDoRNet, our model improves by 1.651 dB in the peak signal-to-noise ratio. Significance. IWNeXt is a potential cross-domain method that can enhance the accuracy of MC MRI reconstruction and reduce reliance on fully sampled target contrast images. [ABSTRACT FROM AUTHOR]

To address the problem of instability in phase retrieval, ptychography technology was introduced to intensity interferometry (II), called ptychography II imaging (PIII). The resolution of PIII system is limited by the size of detection aperture. However, large-aperture detector means high cost. In this paper, the concept of aperture synthesis is brought to PIII. We theoretically derive the periodicity of speckle pattern produced by PIII, which can help guide the system design. Our findings also suggested that discontinuous speckle patterns can still be used to reconstruct the image of object. Additionally, the aperture-synthesis-based PIII system was found to have stronger tolerance to intensity noise and could reconstruct images at low signal-to-noise ratios. Finally, by conducting simulations and experiments, we demonstrated the effectiveness of the aperture-synthesis-based PIII system. These results indicate that aperture synthesis is a valuable approach for enhancing the performance of PIII system. [ABSTRACT FROM AUTHOR]

Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution. Furthermore, long scan time is not clinically acceptable. Conventional high-resolution images generated using statistical or analytical methods include the limitation of capturing complex, high-dimensional image data with intricate patterns and structures. This study aims to harness cutting-edge diffusion probabilistic deep learning techniques to create a framework for generating high-resolution MRI from low-resolution counterparts, improving the uncertainty of denoising diffusion probabilistic models (DDPM). Approach. DDPM includes two processes. The forward process employs a Markov chain to systematically introduce Gaussian noise to low-resolution MRI images. In the reverse process, a U-Net model is trained to denoise the forward process images and produce high-resolution images conditioned on the features of their low-resolution counterparts. The proposed framework was demonstrated using T2-weighted MRI images from institutional prostate patients and brain patients collected in the Brain Tumor Segmentation Challenge 2020 (BraTS2020). Main results. For the prostate dataset, the bicubic interpolation model (Bicubic), conditional generative-adversarial network (CGAN), and our proposed DDPM framework improved the noise quality measure from low-resolution images by 4.4%, 5.7%, and 12.8%, respectively. Our method enhanced the signal-to-noise ratios by 11.7%, surpassing Bicubic (9.8%) and CGAN (8.1%). In the BraTS2020 dataset, the proposed framework and Bicubic enhanced peak signal-to-noise ratio from resolution-degraded images by 9.1% and 5.8%. The multi-scale structural similarity indexes were 0.970 ± 0.019, 0.968 ± 0.022, and 0.967 ± 0.023 for the proposed method, CGAN, and Bicubic, respectively. Significance. This study explores a deep learning-based diffusion probabilistic framework for improving MR image resolution. Such a framework can be used to improve clinical workflow by obtaining high-resolution images without penalty of the long scan time. Future investigation will likely focus on prospectively testing the efficacy of this framework with different clinical indications. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced chromatic dispersion probe for simultaneous measurement of dual-axis absolute and relative displacement with nanometric resolutions based on a 4 f optical system and a signal processing algorithm. In the 4 f optical system, a light source is generated by a pinhole in such a way that the employed pinhole is placed on the focal planes of the two achromatic lenses, aimed at generating a low-pass filter in the frequency domain. With a smaller pinhole, side lobes of the measured spectra are effectively suppressed and the signal-to-noise ratio is optimized, resulting in an expanded absolute measurement range and improved resolution of the two axes, and an enhanced relative measurement resolution of the dual-axis. A signal processing algorithm is proposed in such a way that in the spectral curvature region of the dual-axis output spectra, the original fitted spectrum is replaced by a linear fitting line and a newly-intersected wavelength can be obtained, resulting in an expanded measurement range of the relative displacement of the dual-axis. Numerical simulation and experiments have indicated that the absolute measurement range has been expanded to 225 μm with an enhanced resolution of 40 nm, and the measurement range of the relative displacement of the dual-axis has been expanded to 370 μm with an enhanced relative measurement resolution of 90 nm. [ABSTRACT FROM AUTHOR]

Objective. Multi-contrast magnetic resonance (MR) imaging super-resolution (SR) reconstruction is an effective solution for acquiring high-resolution MR images. It utilizes anatomical information from auxiliary contrast images to improve the quality of the target contrast images. However, existing studies have simply explored the relationships between auxiliary contrast and target contrast images but did not fully consider different anatomical information contained in multi-contrast images, resulting in texture details and artifacts unrelated to the target contrast images. Approach. To address these issues, we propose a dual contrast attention-guided multi-frequency fusion (DCAMF) network to reconstruct SR MR images from low-resolution MR images, which adaptively captures relevant anatomical information and processes the texture details and low-frequency information from multi-contrast images in parallel. Specifically, after the feature extraction, a feature selection module based on a dual contrast attention mechanism is proposed to focus on the texture details of the auxiliary contrast images and the low-frequency features of the target contrast images. Then, based on the characteristics of the selected features, a high- and low-frequency fusion decoder is constructed to fuse these features. In addition, a texture-enhancing module is embedded in the high-frequency fusion decoder, to highlight and refine the texture details of the auxiliary contrast and target contrast images. Finally, the high- and low-frequency fusion process is constrained by integrating a deeply-supervised mechanism into the DCAMF network. Main results. The experimental results show that the DCAMF outperforms other state-of-the-art methods. The peak signal-to-noise ratio and structural similarity of DCAMF are 39.02 dB and 0.9771 on the IXI dataset and 37.59 dB and 0.9770 on the BraTS2018 dataset, respectively. The image recovery is further validated in segmentation tasks. Significance. Our proposed SR model can enhance the quality of MR images. The results of the SR study provide a reliable basis for clinical diagnosis and subsequent image-guided treatment. [ABSTRACT FROM AUTHOR]

Objective. Magnetic particle imaging (MPI) shows potential for contributing to biomedical research and clinical practice. However, MPI images are effectively affected by noise in the signal as its reconstruction is an ill-posed inverse problem. Thus, effective reconstruction method is required to reduce the impact of the noise while mapping signals to MPI images. Traditional methods rely on the hand-crafted data-consistency (DC) term and regularization term based on spatial priors to achieve noise-reducing and reconstruction. While these methods alleviate the ill-posedness and reduce noise effects, they may be difficult to fully capture spatial features. Approach. In this study, we propose a deep neural network for end-to-end reconstruction (DERnet) in MPI that emulates the DC term and regularization term using the feature mapping subnetwork and post-processing subnetwork, respectively, but in a data-driven manner. By doing so, DERnet can better capture signal and spatial features without relying on hand-crafted priors and strategies, thereby effectively reducing noise interference and achieving superior reconstruction quality. Main results. Our data-driven method outperforms the state-of-the-art algorithms with an improvement of 0.9–8.8 dB in terms of peak signal-to-noise ratio under various noise levels. The result demonstrates the advantages of our approach in suppressing noise interference. Furthermore, DERnet can be employed for measured data reconstruction with improved fidelity and reduced noise. In conclusion, our proposed method offers performance benefits in reducing noise interference and enhancing reconstruction quality by effectively capturing signal and spatial features. Significance. DERnet is a promising candidate method to improve MPI reconstruction performance and facilitate its more in-depth biomedical application. [ABSTRACT FROM AUTHOR]

A variety of interstellar complex organic molecules (COMs) have been detected in various physical conditions. However, in the protostellar and protoplanetary environments, their complex kinematics make line profiles blend together and the line strength of weak lines weaker. In this paper, we utilize the principal component analysis technique to develop a filtering method that can extract COM spectra from the main kinematic component associated with COM emission and increase the signal-to-noise ratio (S/N) of spectra. This filtering method corrects non-Gaussian line profiles caused by the kinematics. For this development, we adopt the ALMA BAND 6 spectral survey data of V883 Ori, an eruptive young star with a Keplerian disk. A filter was, first, created using 34 strong and well-isolated COM lines and then applied to the entire spectral range of the data set. The first principal component (PC1) describes the most common emission structure of the selected lines, which is confined within the water sublimation radius (∼0.″3) in the Keplerian disk of V883 Ori. Using this PC1 filter, we extracted high-S/N kinematics-corrected spectra of V883 Ori over the entire spectral coverage of ∼50 GHz. The PC1-filtering method reduces the noise by a factor of ∼2 compared to the average spectra over the COM emission region. One important advantage of this PC1-filtering method over the previously developed matched-filtering method is the ability to preserve the original integrated intensities of COM lines. [ABSTRACT FROM AUTHOR]

We present the Simple Intensity Map Producer for Line Emission (Simple), a public code for quickly simulating mock line-intensity maps, and an analytical framework for modeling intensity maps including observational effects. Simple can be applied to any spectral line sourced by galaxies. The Simple code is based on lognormal mock catalogs of galaxies including positions and velocities and assigns luminosities following the luminosity function. After applying a selection function to distinguish between detected and undetected galaxies, the code generates an intensity map, which can be modified with anisotropic smoothing, noise, a mask, and sky subtraction, and it calculates the power spectrum multipoles. We show that the intensity auto-power spectrum and the galaxy-intensity cross-power spectrum agree well with the analytical estimates in real space. We derive and show that the sky subtraction suppresses the intensity autopower spectrum and the cross-power spectrum on scales larger than the size of an individual observation. As an example application, we make forecasts for the sensitivity of an intensity mapping experiment similar to the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX) to the cross-power spectrum of Ly α -emitting galaxies and the Ly α intensity. We predict that HETDEX will measure the galaxy-intensity cross-power spectrum with a high signal-to-noise ratio on scales of 0.04 h Mpc−1 < k < 1 h Mpc−1. [ABSTRACT FROM AUTHOR]

We report the first statistical analyses of [C ii ] and dust continuum observations in six strong O i absorber fields at the end of the reionization epoch obtained by the Atacama Large Millimeter/submillimeter Array (ALMA). Combined with one [C ii ] emitter reported in Wu et al., we detect one O i -associated [C ii ] emitter in six fields. At redshifts of O i absorbers in nondetection fields, no emitters are brighter than our detection limit within impact parameters of 50 kpc and velocity offsets between ±200 km s−1. The averaged [C ii ]-detection upper limit is <0.06 Jy km s−1(3 σ), corresponding to the [C ii ] luminosity of L [C II ]< 5.8 × 107 L ⊙ and the [C ii ]-based star formation rate of SFR[C II ]<5.5 M ⊙ yr−1. Cosmological simulations suggest that only ∼10−2.5 [C ii ] emitters around O i absorbers have comparable SFR to our detection limit. Although the detection in one out of six fields is reported, an order of magnitude number excess of emitters obtained from our ALMA observations supports that the contribution of massive galaxies that caused the metal enrichment cannot be ignored. Further, we also found 14 tentative galaxy candidates with a signal-to-noise ratio of ≈4.3 at large impact parameters (>50 kpc) and having larger outflow velocities within ±600 km s−1. If these detections are confirmed in the future, then the mechanism of pushing metals at larger distances with higher velocities needs to be further explored from the theoretical side. [ABSTRACT FROM AUTHOR]

Using deep near-infrared Keck/MOSFIRE observations, we analyze the rest-optical spectra of eight star-forming galaxies in the COSMOS and GOODS-N fields. We reach integration times of ∼10 hr in the deepest bands, pushing the limits on current ground-based observational capabilities. The targets fall into two redshift bins, of five galaxies at z ∼ 1.7 and three galaxies at z ∼ 2.5, and were selected as likely to yield significant auroral-line detections. Even with long integration times, detection of the auroral lines remains challenging. We stack the spectra together into subsets based on redshift, improving the signal-to-noise ratio on the [O iii ] λ 4364 auroral emission line and, in turn, enabling a direct measurement of the oxygen abundance for each stack. We compare these measurements to commonly employed strong-line ratios alongside measurements from the literature. We find that the stacks fall within the distribution of z > 1 literature measurements, but a larger sample size is needed to robustly constrain the relationships between strong-line ratios and oxygen abundance at high redshift. We additionally report detections of [O i ] λ 6302 for nine individual galaxies and composite spectra of 21 targets in the MOSFIRE pointings. We plot their line ratios on the [O iii ] λ 5008/H β versus [O i ] λ 6302/H α diagnostic diagram, comparing our targets to local galaxies and H ii regions. We find that the [O i ]/H α ratios in our sample of galaxies are consistent with being produced in gas ionized by α -enhanced massive stars, as has been previously inferred for rapidly forming galaxies at early cosmic times. [ABSTRACT FROM AUTHOR]

The observations from the Dark Energy Spectroscopic Instrument (DESI) will significantly increase the numbers of known extremely metal-poor stars by a factor of ∼10, improving the sample statistics to study the early chemical evolution of the Milky Way and the nature of the first stars. In this paper we report follow-up observations with high signal-to-noise ratio of nine metal-poor stars identified during the DESI commissioning with the Optical System for Imaging and Low-Resolution Integrated Spectroscopy (OSIRIS) instrument on the 10.4 m Gran Telescopio Canarias. The analysis of the data using a well-vetted methodology confirms the quality of the DESI spectra and the performance of the pipelines developed for the data reduction and analysis of DESI data. [ABSTRACT FROM AUTHOR]

Objective. Addition of a denoising filter step in ultrasound localization microscopy (ULM) has been shown to effectively reduce the error localizations of microbubbles (MBs) and achieve resolution improvement for super-resolution ultrasound (SR-US) imaging. However, previous image-denoising methods (e.g. block-matching 3D, BM3D) requires long data processing times, making ULM only able to be processed offline. This work introduces a new way to reduce data processing time through deep learning. Approach. In this study, we propose deep learning (DL) denoising based on contrastive semi-supervised network (CS-Net). The neural network is mainly trained with simulated MBs data to extract MB signals from noise. And the performances of CS-Net denoising are evaluated in both in vitro flow phantom experiment and in vivo experiment of New Zealand rabbit tumor. Main results. For in vitro flow phantom experiment, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of single microbubble image are 26.91 dB and 4.01 dB, repectively. For in vivo animal experiment, the SNR and CNR were 12.29 dB and 6.06 dB. In addition, single microvessel of 24 μ m and two microvessels separated by 46 μ m could be clearly displayed. Most importantly,, the CS-Net denoising speeds for in vitro and in vivo experiments were 0.041 s frame−1 and 0.062 s frame−1, respectively. Significance. DL denoising based on CS-Net can improve the resolution of SR-US as well as reducing denoising time, thereby making further contributions to the clinical real-time imaging of ULM. [ABSTRACT FROM AUTHOR]

Theoretical models and observations suggest that the abundances of molecular ions in protoplanetary disks should be highly sensitive to the variable ionization conditions set by the young central star. We present a search for temporal flux variability of HCO+ J = 1–0, which was observed as a part of the Molecules with Atacama Large Millimeter/submillimeter Array (ALMA) at Planet-forming Scales ALMA Large Program. We split out and imaged the line and continuum data for each individual day the five sources were observed (HD 163296, AS 209, GM Aur, MWC 480, and IM Lup, with between three and six unique visits per source). Significant enhancement (>3 σ) was not observed, but we find variations in the spectral profiles in all five disks. Variations in AS 209, GM Aur, and HD 163296 are tentatively attributed to variations in HCO+ flux, while variations in IM Lup and MWC 480 are most likely introduced by differences in the uv coverage, which impact the amount of recovered flux during imaging. The tentative detections and low degree of variability are consistent with expectations of X-ray flare-driven HCO+ variability, which requires relatively large flares to enhance the HCO+ rotational emission at significant (>20%) levels. These findings also demonstrate the need for dedicated monitoring campaigns with high signal-to-noise ratios to fully characterize X-ray flare-driven chemistry. [ABSTRACT FROM AUTHOR]

Herein, we report a simple non-enzymatic electrochemical sensor for the detection of serotonin (5-HT) in blood serum using ZnO oxide nanoparticles-copper metal-organic framework (MOF) composite on 3D porous nickel foam, namely, ZnO-Cu MOF/NF. The x-ray diffraction analysis reveals the crystalline nature of synthesized Cu MOF and Wurtzite structure of ZnO nanoparticles, whereas SEM characterization confirms the high surface area of the composite nanostructures. Differential pulse voltammetry analysis under optimal conditions yields a wide linear detection range of 1 ng ml−1 to 1 mg ml−1 to 5-HT concentrations and a LOD (signal to noise ratio = 3.3) of 0.49 ng ml−1, which is well below the lowest physiological concentration of 5-HT. The sensitivity of the fabricated sensor is found to be 0.0606 mA ng−1 ml−1.cm2, and it exhibited remarkable selectivity towards serotonin in the presence of various interferants, including dopamine and AA, which coexist in the real biological matrix. Further, successful determination of 5-HT is achieved in the simulated blood serum sample with a good recovery percentage from ∼102.5% to ∼99.25%. The synergistic combination of the excellent electrocatalytic properties and surface area of the constituent nanomaterials proves the overall efficacy of this novel platform and shows immense potential to be used in developing versatile electrochemical sensors. [ABSTRACT FROM AUTHOR]

Background. Recently, approaches have utilized the superior anatomical information provided by magnetic resonance imaging (MRI) to guide the reconstruction of positron emission tomography (PET). One of those approaches is the Bowsher's prior, which has been accelerated lately with a convolutional neural network (CNN) to reconstruct MR-guided PET in the imaging domain in routine clinical imaging. Two differently trained Bowsher-CNN methods (B-CNN0 and B-CNN) have been trained and tested on brain PET/MR images with non-PSMA tracers, but so far, have not been evaluated in other anatomical regions yet. Methods. A NEMA phantom with five of its six spheres filled with the same, calibrated concentration of 18F-DCFPyL-PSMA, and thirty-two patients (mean age 64 ± 7 years) with biopsy-confirmed PCa were used in this study. Reconstruction with either of the two available Bowsher-CNN methods were performed on the conventional MR-based attenuation correction (MRAC) and T1-MR images in the imaging domain. Detectable volume of the spheres and tumors, relative contrast recovery (CR), and background variation (BV) were measured for the MRAC and the Bowsher-CNN images, and qualitative assessment was conducted by ranking the image sharpness and quality by two experienced readers. Results. For the phantom study, the B-CNN produced 12.7% better CR compared to conventional reconstruction. The small sphere volume (<1.8 ml) detectability improved from MRAC to B-CNN by nearly 13%, while measured activity was higher than the ground-truth by 8%. The signal-to-noise ratio, CR, and BV were significantly improved (p < 0.05) in B-CNN images of the tumor. The qualitative analysis determined that tumor sharpness was excellent in 76% of the PET images reconstructed with the B-CNN method, compared to conventional reconstruction. Conclusions. Applying the MR-guided B-CNN in clinical prostate PET/MR imaging improves some quantitative, as well as qualitative imaging measures. The measured improvements in the phantom are also clearly translated into clinical application. [ABSTRACT FROM AUTHOR]

Time-of-flight (TOF) positron emission tomography (PET) detectors improve the signal-to-noise ratio of PET images by limiting the position of the generation of two 511 keV gamma-rays in space using the arrival time difference between the two photons. Unfortunately, bismuth germanate (BGO), widely used in conventional PET detectors, was limited as a TOF PET scintillator due to the relatively slow decay time of the scintillation photons. However, prompt Cerenkov light in BGO has been identified in addition to scintillation photons. Using Cerenkov photons for timing has significantly improved the coincidence timing resolution (CTR) of BGO. Based on this, further research on improving the CTR for a BGO-based TOF PET system is being actively conducted. Wrapping materials for BGO pixels have primarily employed white reflectors to most efficiently collect scintillation light. White reflectors have customarily been used as reflectors for BGO pixels even after Cerenkov light began to be utilized for timing calculations in pixel-level experiments. However, when the arrival-time differences of the two 511 keV annihilations photons were measured with pure Cerenkov radiators, painting the lateral sides of the radiators black can improve CTR by suppressing the reflection of Cerenkov photons. The use of BGO for TOF PET detectors requires simultaneously minimizing scintillation loss for good energy information and suppressing reflected Cerenkov photons for better timing performance. Thus, reflectors for BGO pixels should be optimized for better timing and energy performance. In this study, colored polytetrafluoroethylene (PTFE) tapes with discontinuous reflectance values at specific wavelengths were applied as a BGO reflector. We hypothesized that CTR could be enhanced by selectively suppressing reflected Cerenkov photons with an optimum colored reflector on the BGO pixel while minimizing scintillation photon loss. CTRs were investigated utilizing white and three colors (yellow, red, and green) PTFE tapes as a reflector. In addition, black-painted PTFE tape and enhanced specular reflector film were investigated as reference reflector materials. When 3 × 3 × 20 mm3 BGO pixels were wrapped with the yellow PTFE reflector, the CTR was significantly improved to 365 ± 5 ps from 403 ± 14 ps measured with the conventional white PTFE reflector. Adequate energy information was still obtained with only 4.1% degradation in light collection compared to the white reflector. Colored reflectors show the possibility to further improve CTR for BGO pixels with optimum reflectance design. [ABSTRACT FROM AUTHOR]

Mergers of galaxies are a ubiquitous phenomenon in the universe and represent a natural consequence of the "bottom-up" mass accumulation and galaxy evolution cosmological paradigm. It is generally accepted that the peak of active galactic nucleus (AGN) accretion activity occurs at nuclear separations of ≲10 kpc for major mergers. Here we present new NuSTAR and XMM-Newton observations for a subsample of mid-IR preselected dual AGN candidates in an effort to better constrain the column densities along the line of sight (LOS) for each system. Only one dual AGN candidate, J0841+0101, is detected as a single, unresolved source in the XMM-Newton and NuSTAR imaging, while the remaining three dual AGN candidates, J0122+0100, J1221+1137, and J1306+0735, are not detected with NuSTAR; if these nondetections are due to obscuration alone, these systems are consistent with being absorbed by column densities of log(N H/cm−2) ≥ 24.9, 24.6, and 24.3, which are roughly consistent with previously inferred column densities in these merging systems. In the case of J0841+0101, the analysis of the 0.3–30 keV spectra reveal an LOS column density of N H ≳ 1024 cm−2, significantly larger than the column densities previously reported for this system and demonstrating the importance of the higher signal-to-noise ratio XMM-Newton spectra and access to the >10 keV energies via NuSTAR. Though it is unclear if J0841+0101 truly hosts a dual AGN, these results are in agreement with the high obscuring columns expected in AGNs in late-stage mergers. [ABSTRACT FROM AUTHOR]

For bearing fault diagnosis at time-varying speed with tachometer-free and non-resampling, the crucial process is to obtain a high-resolution time-frequency representation and extract fault features. However, current multi-component non-stationary signal feature extraction methods based on time-frequency transform suffer from fixed parameter settings and insufficient resolution for low signal-to-noise ratio signals. To address these issues, a novel adaptive clustered fractional Gabor transform is proposed and applied to extract bearing fault features at time-varying speed. Firstly, the grey wolf optimization is utilized to adaptively search for the optimal fractional order and Gauss window length based on the maximum spectral kurtosis and the generalized time-bandwidth product to achieve the most adequate fractional Gabor spectrum (FrGS). Then, the Clustering by Fast Search and Find of Density Peaks algorithm reconstructs the sparse representation of the FrGS, remapping multi-component signals into single-component clusters. Bearing fault diagnosis is achieved by matching the relative order of each cluster with the bearing fault characteristic coefficients. Simulation signals validate the superiority of the feature extraction method, and experimental signals validate the feasibility of the bearing fault diagnosis method. [ABSTRACT FROM AUTHOR]

Objective. Positron emission tomography (PET) is a functional imaging widely used in various applications such as tumour detection. PET image reconstruction is an ill-posed inverse problem, and the model-based iterative reconstruction methods commonly used in clinical practice have disadvantages such as long time consumption and low signal-to-noise ratio, especially at low doses. Approach. In this study, we propose a deep learning-based reconstruction method that is capable of reconstructing images directly from low-count sinograms. Our network consists of two parts, a truncated inverse radon layer for implementing domain transform and a U-shaped network for image enhancement. Main result. We validated our method on both simulation data and real data. Compared to ordered subset expectation maximization with a post-Guassian filter, the structural similarity can be improved from 0.9357 to 0.9613 and the peak signal-to-noise ratio can be improved by 5 dB. Significance. The proposed method can directly convert low-count sinograms into PET images, while obtaining improved image quality and having less time consumption than iterative reconstruction algorithms and the state-of-the-art convolutional neural network. [ABSTRACT FROM AUTHOR]

Objective. The goal of this study is to develop a robust semi-weakly supervised learning strategy for vessel segmentation in laser speckle contrast imaging (LSCI), addressing the challenges associated with the low signal-to-noise ratio, small vessel size, and irregular vascular aberration in diseased regions, while improving the performance and robustness of the segmentation method. Approach. For the training dataset, the healthy vascular images denoted as normal-vessel samples were manually labeled, while the diseased LSCI images involving tumor or embolism were denoted as abnormal-vessel samples and annotated as pseudo labels by the traditional semantic segmentation methods. In the training phase, the pseudo labels were constantly updated to improve the segmentation accuracy based on DeepLabv3+. Objective evaluation was conducted on the normal-vessel test set, while subjective evaluation was performed on the abnormal-vessel test set. Main results. The proposed method achieved an IOU of 0.8671, a Dice of 0.9288, and a mean relative percentage difference (mRPD) with supervised learning of 0.5% in the objective evaluation. In the subjective evaluation, our method significantly outperformed other methods in main vessel segmentation, tiny vessel segmentation, and blood vessel connection. Additionally, our method exhibited robustness when abnormal-vessel style noise was added to normal-vessel samples using a style translation network. Significance. The proposed semi-weakly supervised learning strategy demonstrates high efficiency and excellent robustness for vascular segmentation in LSCI, providing a potential tool for assessing the morphological and structural features of vessels in clinical applications. [ABSTRACT FROM AUTHOR]

Objective. 3D ultrasound non-rigid registration is significant for intraoperative motion compensation. Nevertheless, distorted textures in the registered image due to the poor image quality and low signal-to-noise ratio of ultrasound images reduce the accuracy and efficiency of the existing methods. Approach. A novel 3D ultrasound non-rigid registration objective function with texture and content constraints in both image space and multiscale feature space based on an unsupervised generative adversarial network based registration framework is proposed to eliminate distorted textures. A similarity metric in the image space is formulated based on combining self-structural constraint with intensity to strengthen the robustness to abnormal intensity change compared with common intensity-based metrics. The proposed framework takes two discriminators as feature extractors to formulate the texture and content similarity between the registered image and the fixed image in the multiscale feature space respectively. A distinctive alternating training strategy is established to jointly optimize the combination of various similarity loss functions to overcome the difficulty and instability of training convergence and balance the training of generator and discriminators. Main results. Compared with five registration methods, the proposed method is evaluated both with small and large deformations, and achieves the best registration accuracy with average target registration error of 1.089 mm and 2.139 mm in cases of small and large deformations, respectively. The performance on peak signal to noise ratio (PSNR) and structural similarity (SSIM) also proves the effective constraints on distorted textures of the proposed method (PSNR is 31.693 dB and SSIM is 0.9 in the case of small deformation; PSNR is 28.177 dB and SSIM is 0.853 in the case of large deformation). Significance. The proposed 3D ultrasound non-rigid registration method based on texture and content constraints with the distinctive alternating training strategy can eliminate the distorted textures with improving the registration accuracy. [ABSTRACT FROM AUTHOR]

The lensing effect of the cosmic microwave background (CMB) is a powerful tool for our study of the distribution of matter in the universe. The quadratic estimator (QE) method, which is widely used to reconstruct lensing potential, has been known to be suboptimal for the low noise level polarization data from next-generation CMB experiments. To improve the performance of the reconstruction, other methods, such as the maximum-likelihood estimator and machine-learning algorithms, have been developed. In this work, we present a deep convolutional neural network model named the Residual Dense Local Feature U-net (RDLFUnet) for reconstructing the CMB lensing convergence field. By simulating lensed CMB data with different noise levels to train and test network models, we find that for noise levels less than 5 μ K-arcmin, RDLFUnet can recover the input gravitational potential with a higher signal-to-noise ratio than the previous deep-learning and traditional QE methods at almost the entire observation scale. [ABSTRACT FROM AUTHOR]

For the astronomical adaptive optics systems, the observation condition is always in a regime of low photon flux. We present a method of superimposing subapertures for the Shark–Hartmann wave front sensing with faint objects. This approach significantly improves the signal-to-noise ratio (S/N) of the spot pattern by superimposing adjacent subapertures, thus circumventing the invalid wave front sensing with faint object, enhancing the accuracy of the wave front detection. In addition, we demonstrate that the validity of this approach is not an incidental result under a specific condition, which can be explained theoretically by exploiting the atmospheric turbulence model. Then we validate the approach with simulations and experimental realization under weak-light and bright-light conditions. Results show that the proposed subaperture superposition approach can achieve accurate wave front reconstruction for low S/N or faint object even in cases beyond the ability of conventional methods. It brings out a new idea, besides long exposure and variable sampling, that comes with a tradeoff between temporal sampling frequency and spatial resolution. In addition, this method is also valid for extended objects; we preliminarily validate this method on a 600 mm aperture solar telescope, achieving stable closed-loop control with lower-contrast objects. [ABSTRACT FROM AUTHOR]

We perform X-ray spectral analyses to derive the characteristics (e.g., column density, X-ray luminosity) of ≈10,200 active galactic nuclei (AGNs) in the XMM-Spitzer Extragalactic Representative Volume Survey, which was designed to investigate the growth of supermassive black holes across a wide dynamic range of cosmic environments. Using physical torus models (e.g., Borus02) and a Bayesian approach, we uncover 22 representative Compton-thick (CT; N H > 1.5 × 1024 cm−2) AGN candidates with good signal-to-noise ratios as well as a large sample of 136 heavily obscured AGNs. We also find an increasing CT fraction (f CT) from low (z < 0.75) to high (z > 0.75) redshift. Our CT candidates tend to show hard X-ray spectral shapes and dust extinction in their spectral energy distribution fits, which may shed light on the connection between AGN obscuration and host-galaxy evolution. [ABSTRACT FROM AUTHOR]

We present spectroscopic chemical abundances of red giant branch stars in Andromeda (M31), using medium-resolution (R ∼ 6000) spectra obtained via the Spectroscopic and Photometric Landscape of Andromeda's Stellar Halo survey. In addition to individual chemical abundances, we coadd low signal-to-noise ratio spectra of stars to obtain a high enough signal to measure average [Fe/H] and [ α /Fe] abundances. We obtain individual and coadded measurements for [Fe/H] and [ α /Fe] for M31 halo stars, covering a range of 9–180 kpc in projected radius from the center of M31. With these measurements, we greatly increase the number of outer halo (R proj > 50 kpc) M31 stars with spectroscopic [Fe/H] and [ α /Fe], adding abundance measurements for 45 individual stars and 33 coadds from a pool of an additional 174 stars. We measure the spectroscopic metallicity ([Fe/H]) gradient, finding a negative radial gradient of −0.0084 ± 0.0008 for all stars in the halo, consistent with gradient measurements obtained using photometric metallicities. Using the first measurements of [ α /Fe] for M31 halo stars covering a large range of projected radii, we find a positive gradient (+0.0027 ± 0.0005) in [ α /Fe] as a function of projected radius. We also explore the distribution in [Fe/H]–[ α /Fe] space as a function of projected radius for both individual and coadded measurements in the smooth halo, and compare these measurements to those stars potentially associated with substructure. These spectroscopic abundance distributions add to existing evidence that M31 has had an appreciably different formation and merger history compared to our own Galaxy. [ABSTRACT FROM AUTHOR]

Stochastic resonance has been widely used in bearing fault diagnosis due to the enhancement principle of energy conversion from noise to weak signals. However, the diagnosis efficiency and robustness is still challenging. Under the mechanism of generalized stochastic resonance (GSR), in this paper we propose a scale-transformed linear oscillator (SLO) and obtain the analytical expression of fault feature amplification (FFA) to replace the numerical implementation of output signal-to-noise ratio in the multi-parameter optimization. This brings a substantial benefit to the reduction of time complexity in fast fault diagnosis, which is verified in both theory and simulations. In the experimental diagnosis for some typical cases, the results show that the proposed method is valid and exhibits superiority in diagnostic performance, efficiency and robustness, demonstrating that the FFA-guided GSR-SLO method has great potential in engineering applications, especially for real-time fault diagnosis in complex operating environments. [ABSTRACT FROM AUTHOR]

Objective. A phased or a curvilinear array produces ultrasound (US) images with a sector field of view, which inherently exhibits spatially-varying image resolution with inferior quality in the far zone and towards the two sides azimuthally. Sector US images with improved spatial resolutions are favorable for accurate quantitative analysis of large and dynamic organs, such as the heart. Therefore, this study aims to translate US images with spatially-varying resolution to ones with less spatially-varying resolution. CycleGAN has been a prominent choice for unpaired medical image translation; however, it neither guarantees structural consistency nor preserves backscattering patterns between input and generated images for unpaired US images. Approach. To circumvent this limitation, we propose a constrained CycleGAN (CCycleGAN), which directly performs US image generation with unpaired images acquired by different ultrasound array probes. In addition to conventional adversarial and cycle-consistency losses of CycleGAN, CCycleGAN introduces an identical loss and a correlation coefficient loss based on intrinsic US backscattered signal properties to constrain structural consistency and backscattering patterns, respectively. Instead of post-processed B-mode images, CCycleGAN uses envelope data directly obtained from beamformed radio-frequency signals without any other nonlinear postprocessing. Main results. In vitro phantom results demonstrate that CCycleGAN successfully generates images with improved spatial resolution as well as higher peak signal-to-noise ratio and structural similarity compared with benchmarks. Significance. CCycleGAN-generated US images of the in vivo human beating heart further facilitate higher quality heart wall motion estimation than benchmarks-generated ones, particularly in deep regions. The codes are available at https://github.com/xfsun99/CCycleGAN-TF2 [ABSTRACT FROM AUTHOR]

Objective. Denoising models based on the supervised learning have been proposed for medical imaging. However, its clinical availability in digital tomosynthesis (DT) imaging is limited due to the necessity of a large amount of training data for providing acceptable image quality and the difficulty in minimizing a loss. Reinforcement learning (RL) can provide the optimal pollicy, which maximizes a reward, with a small amount of training data for implementing a task. In this study, we presented a denoising model based on the multi-agent RL for DT imaging in order to improve the performance of the machine learning-based denoising model. Approach. The proposed multi-agent RL network consisted of shared sub-network, value sub-network with a reward map convolution (RMC) technique and policy sub-network with a convolutional gated recurrent unit (convGRU). Each sub-network was designed for implementing feature extraction, reward calculation and action execution, respectively. The agents of the proposed network were assigned to each image pixel. The wavelet and Anscombe transformations were applied to DT images for delivering precise noise features during network training. The network training was implemented with the DT images obtained from the three-dimensional digital chest phantoms, which were constructed by using clinical CT images. The performance of the proposed denoising model was evaluated in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). Main results. Comparing the supervised learning, the proposed denoising model improved the SNRs of the output DT images by 20.64% while maintaining the similar SSIMs and PSNRs. In addition, the SNRs of the output DT images with the wavelet and Anscombe transformations were 25.88 and 42.95% higher than that for the supervised learning, respectively. Significance. The denoising model based on the multi-agent RL can provide high-quality DT images, and the proposed method enables the performance improvement of machine learning-based denoising models. [ABSTRACT FROM AUTHOR]

We report the observation by the Atacama Large Millimeter/submillimeter Array (ALMA) of a z ≳ 10 galaxy candidate (GHZ1) discovered from the GLASS–JWST Early Release Science Program. Our ALMA program aims to detect the [O iii ] emission line at the rest-frame frequency 3393.0062 GHz (88.36 μ m) and far-IR continuum emission with the spectral window setup seamlessly covering a 26.125 GHz frequency range (10.10 < z < 11.14). A total of 7 hr of on-source integration was employed, using four frequency settings to cover the full range (1.7 hr per setting), with 0.″7 angular resolution. No line or continuum is clearly detected, with 5 σ upper limits on the line emission of 0.93 mJy beam−1 at 25 km s−1 channel−1 and on the continuum emission of 30 μ Jy beam−1. We report marginal spectral (at 225 km s−1 resolution) and continuum features (4.1 σ and 2.6 σ peak signal-to-noise ratio, respectively), within 0.″17 from the JWST position of GHZ1. This spectral feature implies z = 10.38 and needs to be verified with further observations. Assuming that the best estimate of photometric redshift ( z = 10.60 − 0.60 + 0.52 ) is correct, the model of the galaxy's broadband spectral energy distribution for the 3 σ upper limit of the continuum flux from GHZ1 suggests that GHZ1 has a small amount of dust (M d ≲ 104 M ⊙) at a high temperature (T d ≳ 90 K). The 5 σ upper limit on the [O iii ]88 μ m line luminosity and the inferred star formation rate of GHZ1 are consistent with the properties of low-metallicity dwarf galaxies. We also report serendipitous clear detections of six continuum sources at the locations of the JWST galaxy counterparts in the field. [ABSTRACT FROM AUTHOR]

Objective. Freehand 3D ultrasound volume reconstruction has received considerable attention in medical research because it can freely perform spatial imaging at a low cost. However, the uneven distribution of the original ultrasound images in space reduces the reconstruction effect of the traditional method. Approach. An adaptive tetrahedral interpolation algorithm is proposed to reconstruct 3D ultrasound volume data. The algorithm adaptively divides the unevenly distributed images into numerous tetrahedrons and interpolates the voxel value in each tetrahedron to reconstruct 3D ultrasound volume data. Main results. Extensive experiments on simulated and clinical data confirm that the proposed method can achieve more accurate reconstruction than six benchmark methods. Specifically, the averaged interpolation error at the gray level can be reduced by 0.22–0.82, and the peak signal-to-noise ratio and the mean structure similarity can be improved by 0.32–1.83 dB and 0.01–0.05, respectively. Significance. With the parallel implementation of the algorithm, one 3D ultrasound volume data with size 279 × 279 × 276 can be reconstructed from 100 slices 2D ultrasound images with size 200 × 200 at 1.04 s. Such a quick and accurate approach has practical value in medical research. [ABSTRACT FROM AUTHOR]

Gradient of spin accumulation in spintronic devices such as lateral spin-valves allows to generate pure spin-current without charge-current. Spin accumulation is an out-of-equilibrium magnetization in which thermal fluctuations can occur. These fluctuations may constitute a source of noise in lateral spin-valve structures. In this study, the thermally induced fluctuations of the vector of spin-accumulation were investigated theoretically in diffusive regime. It is shown that paramagnetic resonance may arise in the spin-current carrying channel due to electron-electron interactions and exchange splitting induced by the spin-accumulation. This leads to an effect that was not previously considered: resonant increase of the magnetic susceptibility of the paramagnetic channel material and an associated decrease in signal-to-noise ratio around the resonance frequency. Frequency dependence of the magnetic susceptibility and signal-to-noise ratio were calculated analytically in the case of a specific T-shaped lateral spin-valve structures. It was shown however that this noise caused by thermally induced fluctuations in spin-accumulation is generally negligible in comparison to other sources of noise present in lateral spin-valves such as Johnson noise or thermal fluctuations of magnetization in the magnetic electrodes. [ABSTRACT FROM AUTHOR]

Gradient of spin accumulation in spintronic devices such as lateral spin-valves allows to generate pure spin-current without charge-current. Spin accumulation is an out-of-equilibrium magnetization in which thermal fluctuations can occur. These fluctuations may constitute a source of noise in lateral spin-valve structures. In this study, the thermally induced fluctuations of the vector of spin-accumulation were investigated theoretically in diffusive regime. It is shown that paramagnetic resonance may arise in the spin-current carrying channel due to electron-electron interactions and exchange splitting induced by the spin-accumulation. This leads to an effect that was not previously considered: resonant increase of the magnetic susceptibility of the paramagnetic channel material and an associated decrease in signal-to-noise ratio around the resonance frequency. Frequency dependence of the magnetic susceptibility and signal-to-noise ratio were calculated analytically in the case of a specific T-shaped lateral spin-valve structures. It was shown however that this noise caused by thermally induced fluctuations in spin-accumulation is generally negligible in comparison to other sources of noise present in lateral spin-valves such as Johnson noise or thermal fluctuations of magnetization in the magnetic electrodes. [ABSTRACT FROM AUTHOR]

Hundreds of millions of supermassive black hole binaries are expected to contribute to the gravitational-wave signal in the nanohertz frequency band. Their signal is often approximated either as an isotropic Gaussian stochastic background with a power-law spectrum or as an individual source corresponding to the brightest binary. In reality, the signal is best described as a combination of a stochastic background and a few of the brightest binaries modeled individually. We present a method that uses this approach to efficiently create realistic pulsar timing array data sets using synthetic catalogs of binaries based on the Illustris cosmological hydrodynamic simulation. We explore three different properties of such realistic backgrounds that could help distinguish them from those formed in the early universe: (i) their characteristic strain spectrum, (ii) their statistical isotropy, and (iii) the variance of their spatial correlations. We also investigate how the presence of confusion noise from a stochastic background affects detection prospects of individual binaries. We calculate signal-to-noise ratios of the brightest binaries in different realizations for a simulated pulsar timing array based on the NANOGrav 12.5 yr data set extended to a time span of 15 yr. We find that ∼6% of the realizations produce systems with signal-to-noise ratios larger than 5, suggesting that individual systems might soon be detected (the fraction increases to ∼41% at 20 yr). These can be taken as a pessimistic prediction for the upcoming NANOGrav 15 yr data set, since it does not include the effect of potentially improved timing solutions and newly added pulsars. [ABSTRACT FROM AUTHOR]

Objectives. Most existing heartbeat-detection algorithms rely heavily on cardiovascular signals, namely electrocardiogram (ECG) and arterial blood pressure (ABP), which are often corrupted by noise, leading to unreliable heart-rate estimates. Simultaneously recorded non-cardiovascular (NC) signals help with reliable heart-rate estimates when both cardiovascular signals are corrupted by noise. This study aims to: (i) propose a modified beat signal quality index-based majority voting fusion (MMVF) method to deal with extremely noisy cardiovascular signals; (ii) generate synthetic noise datasets from standard PhysioNet datasets by adding different types of ECG noises, i.e. baseline wander (bw), electrode motion (em), muscle artifact (ma), and realistic artificial ABP noises in clean or low-noise ECG and ABP signals, respectively; and (iii) analyze the effectiveness of the MMVF method for heart-rate estimation with different combinations of beat detectors. Approach. The modified beat signal quality index in the proposed method can identify the quality of the signal even when it contains long durations of noise. The MMVF method assigns uniform weights to the beats detected from all multimodal physiological signals, thus enabling effective participation of beats from NC signals when both cardiovascular signals are corrupted. Results. Fusion of the NC signals with noisy cardiovascular signals using the MMVF method improves heart-rate estimation accuracy over that of single ECG beat detectors like gqrs, epltd, and slope sum function and Teager–Kaiser energy (SSF-TKE) up to 98.81%, 97.95%, and 87.98%, respectively. This method has yielded robust heart-rate estimation within clinically acceptable error limits in concurrently highly noisy cardiovascular signals (ECG: up to a signal-to-noise ratio (SNR) of −70 dB and ABP: up to 100% noise duration in noisy segments) by their fusion with NC signals. Significance. This study serves as empirical evidence for the robustness of the MMVF method in scenarios where there are extremely noisy cardiovascular signals and NC signals with ECG R-peak artifacts. [ABSTRACT FROM AUTHOR]

The problem of effective information extraction of dynamic signals is a key challenge of vibration-based structural health monitoring. Traditional ensemble empirical mode decomposition (EEMD) and complementary EEMD (CEEMD) are effective filtering methods but have the shortcomings of mode mixing, completeness and the endpoint effect in adaptive decomposition stage. To address the above problems, this paper proposes a noise mode identification method based on multiscale local pattern filtering with a machine learning algorithm. In addition, this method improves the filtering performance of mode decomposition and wavelet threshold denoising methods effectively. To validate the performance of this method, simulation experiment and real signal analysis were used for validation: their results showed that the proposed method has better adaptive decomposition performance than EEMD and CEEMD. Compared with the EEMD, CEEMD and wavelet threshold denoising methods, the proposed method can better balance smoothness and the power reserve, has optimized time- and frequency-domain characteristics, filters with a higher signal-to-noise ratio and reliability, and has more optimized filtering performance. The method proposed in this paper can be applied to bridge health monitoring technology, and has important scientific and engineering significance. [ABSTRACT FROM AUTHOR]

Recently, a modified non-negative Tikhonov regularization method within the Bayesian frame has been successfully proposed, and demonstrated to have excellent compensation performance towards the signal distortion induced by long-distance transmission in coaxial cable. However, how the parameters of this modified method affect compensation performance has not yet been investigated and analyzed, which may in turn bring some obstacles in the practical utilization of this modified method. Herein, we carried out a systematic study on the effect of parameters on the signal compensation performance of this modified method. The bias constant D has almost no influence on the compensation performance if the unknown input signal satisfies the non-negative constraint. When the signal-to-noise ratio (SNR) is less than 40 dB, the compensation performance is continuously improved as the SNR increases. Once the SNR attains ∼40 dB, the compensated signal is nearly identical to the input signal. Further increment of SNR does not give rise to an improved compensation performance and the relative error between the compensated signal and input signal remains nearly unchanged. The sampling frequency interval Î" f of S21 should satisfy the restriction of Î" f = F s / n , with F s and n representing the sampling frequency of the digital oscilloscope and number of points of the output signal, respectively. The compensation performance is excellent initially, whereas it rapidly deteriorates once Î" f exceeds a certain value that is highly related to the length of the impulse response function. This study provides a beneficial reference for application of the modified signal compensation method. [ABSTRACT FROM AUTHOR]

Robust development of energy storage materials requires a deep understanding of the relationship between the operation conditions and electrochemical properties. The influences of cell potential and environmental temperature on battery dynamics of commercially available LiNiMnCoO2(NMC)/graphite(C) coin cells were statistically investigated and discussed by using electrochemical impedance spectroscopy measurements and Taguchi statistical design. The Taguchi method reduced the variation in the electrochemical process with an experimental design; therefore, number and duration of impedance measurements were minimized. This design tested three levels for each categorical factor that are the cell potential and the cell temperature. The third level of both potential and temperature optimized the dynamic parameters of the batteries. The calculated signal-to-noise ratio and delta values for the cell temperature were higher for each battery resistance than that of the cell potential. In addition, the calculated probability, P value, was also lower (around zero) for the cell temperature. Thus, the temperature was determined to be the most significant factor, suggesting that this factor should be constrained carefully when the battery dynamics is desired to be optimized. In this paper, the electrochemical impedance spectroscopy method combined with the Taguchi design was performed to offer an unprecedented insight into the effects of the factors on the elements of the equivalent circuit model of the Li-ion coin cells. © 2022 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited. [ABSTRACT FROM AUTHOR]

We explore a method to assess the relative scale of the strain measured in the different detectors of the gravitational-wave (GW) network, using binary black hole (BBH) events detected during the third observing run (O3). The number of such signals is becoming sufficiently large to adopt a statistical approach based on the ratio of the signal-to-noise ratio of the events between the detectors and the number of observed events in each detector. We demonstrate the principle of the method on simulations of BBH signals and we present its application to published O3 events reported by the multi-band template analysis pipeline. Constraints on the relative calibration of the GW network for O3 are obtained at the level of ∼ 3.5 % between the two LIGO detectors and at the level of ∼ 10 % between the LIGO Livingston detector and the Virgo detector. [ABSTRACT FROM AUTHOR]

We present a study of the narrow Fe K α line in seven bright, nearby active galactic nuclei (AGN) that have been observed extensively with the Chandra High Energy Transmission Grating (HETG). The HETG data reveal a wider Fe K α line in the first-order spectrum than in the second- and third-order spectra, which we interpret as the result of spatially extended Fe K α emission. We utilize these differences in narrow Fe K α line widths in the multi-order Chandra HETG spectra to determine the spatial extent and intrinsic velocity width of the emitting material in each object. We find that there is modest evidence for spatially extended emission in each object, corresponding to extension of r ∼ 5â€"100 pc. These distances are significantly larger than those inferred from velocity widths assuming gravitational motions, which give r ∼ 0.01â€"1 pc. This implies either that the gas is emitting at a range of radii, with smaller radii dominating the velocity width and larger radii dominating the spatial extent, or that the gas is exhibiting nongravitational motions, which we suggest would be outflows due to slight excess redshift in the line and velocities that exceed the freefall velocity. We also use the spatial extent information to estimate the mass of the emitting gas by counting fluorescing iron atoms, finding masses on the order of M gas ∼ 105â€"108 M ⊙. Future work with observatories like XRISM will be able to extend this study to a larger number of AGN and decrease uncertainties that arise as a result of the low signal-to-noise ratio of the higher-order HETG data. [ABSTRACT FROM AUTHOR]

In this work, samarium vanadate (SmV) and single-walled carbon nanotubes (SWCNTs) were self-assembled by ultrasonic treatment to obtain a novel SmV/SWCNTs nanocomposite. Subsequently, an electrochemical sensor based on SmV/SWCNTs nanocomposite modified glassy carbon electrode (GCE) was constructed. SmV/SWCNTs/GCE exhibited high selectivity, excellent anti-interference, good stability and reproducibility toward paracetamol (PA) detection. The outstanding performances of the developed electrochemical sensor could be mainly attributed to its high electrocatalytic activity for PA, which might be caused by the synergistic effect of SmV and SWCNTs. Under optimized experimental conditions, SmV/SWCNTs/GCE exhibited a good linear relationship between current signal and PA concentration over a wide concentration range 1–100 μM, and limit of detection (LOD) (signal-to-noise ratio of 3, S/N = 3) was calculated to be 0.035 μM. The satisfactory recoveries of 98.06%–104.20% and 98.55%–101.88% could be obtained in the bovine serum and PA tablet samples, respectively. We anticipated this work could provide a reference for the future development and application of samarium-based materials in the field of electroanalysis. [ABSTRACT FROM AUTHOR]

Objective. Cone-Beam CT (CBCT) often results in severe image artifacts and inaccurate HU values, meaning poor quality CBCT images cannot be directly applied to dose calculation in radiotherapy. To overcome this, we propose a cycle-residual connection with a dilated convolution-consistent generative adversarial network (Cycle-RCDC-GAN). Approach. The cycle-consistent generative adversarial network (Cycle-GAN) was modified using a dilated convolution with different expansion rates to extract richer semantic features from input images. Thirty pelvic patients were used to investigate the effect of synthetic CT (sCT) from CBCT, and 55 head and neck patients were used to explore the generalizability of the model. Three generalizability experiments were performed and compared: the pelvis trained model was applied to the head and neck; the head and neck trained model was applied to the pelvis, and the two datasets were trained together. Main results. The mean absolute error (MAE), the root mean square error (RMSE), peak signal to noise ratio (PSNR), the structural similarity index (SSIM), and spatial nonuniformity (SNU) assessed the quality of the sCT generated from CBCT. Compared with CBCT images, the MAE improved from 28.81 to 18.48, RMSE from 85.66 to 69.50, SNU from 0.34 to 0.30, and PSNR from 31.61 to 33.07, while SSIM improved from 0.981 to 0.989. The sCT objective indicators of Cycle-RCDC-GAN were better than Cycle-GAN’s. The objective metrics for generalizability were also better than Cycle-GAN’s. Significance. Cycle-RCDC-GAN enhances CBCT image quality and has better generalizability than Cycle-GAN, which further promotes the application of CBCT in radiotherapy. [ABSTRACT FROM AUTHOR]