Neutral hydrogen (HI) intensity mapping with single-dish experiments is a powerful approach for probing cosmology in the post-reionization epoch. It is challenging to extract it, however, because of the bright foregrounds, which are stronger than the HI signal by more than four orders of magnitude. While all methods perform well when a Gaussian beam is assumed that is degraded to the lowest resolution, most methods degrade significantly in a more realistic beam model. The complexity introduced by frequency-dependent beam effects means that we need methods that explicitly account for the instrument response. We investigate the performance of SDecGMCA. This method extends DecGMCA to spherical data by combining sparse component separation with beam deconvolution. Our goal is to evaluate this method in comparison with established foreground removal techniques by assessing its ability to recover the cosmological HI signal from single-dish intensity mapping observations under varying beam conditions. We used simulated HI signals and foregrounds informed by existing observational and theoretical models that cover the frequency ranges relevant to MeerKAT and SKA-Mid. The foreground removal techniques we tested fall into two main categories: model-fitting methods (polynomial and parametric), and blind source separation methods (PCA, ICA, GMCA, and SDecGMCA). Their effectiveness was evaluated based on the recovery of the HI angular and frequency power spectra under progressively more realistic beam conditions. While all methods performed adequately under a uniform degraded beam, SDecGMCA remained robust when frequency-dependent beam distortions were introduced. For an oscillating beam, SDecGMCA suppressed the spurious spectral peak at k_ν ∼ 0.3 and achieved an accuracy of łesssim 5% at intermediate angular scales (10 < < 200); it outperformed other methods. Furthermore, the masking of bright Galactic regions significantly improved the recovery of the HI signal, in particular, for SDecGMCA, which benefited most when contaminated lines of sight were excluded. The beam inversion, however, remained intrinsically unstable beyond ell ∼ 200. This sets a practical limit on the method. Our findings highlight the limitations of simple fitting and standard blind source separation methods for realistic beam effects, and they establish SDecGMCA as a particularly promising approach for future single-dish intensity mapping surveys. Its robustness for various beam models, combined with the improvements that can be achieved through masking strategies and forthcoming refinements to its thresholding scheme, suggest that SDecGMCA might provide reliable spherical harmonics reconstructions of the HI power spectrum in upcoming experiments.

Enhancing seismic image resolution while effectively suppressing noise remains a critical challenge in accurately characterizing subsurface geological structures for oil and gas exploration. Traditional methods often fail to balance the recovery of fine details with robustness to noise, particularly in complex geological settings or under high-noise conditions. This study proposes a deep learning-based joint model, U-Net Shifted Window (Swin) Transformer-based dense residual network (U-STDRNet). The model integrates the global modeling capability of the Swin Transformer, the hierarchical feature reuse mechanism of the residual dense network, and an attention-guided strategy to jointly perform seismic image super-resolution and denoising. Built upon the U-Net encoder-decoder architecture, the model embeds Swin Transformer-based convolutional residual blocks. These blocks employ both a feature fusion block with the Swin Transformer and a feature fusion block with a convolutional neural network to effectively capture stratigraphic continuity and enhance detailed features such as fault edges. Residual dense blocks further improve weak signal recovery (e.g., thin-layer interfaces) through dense residual connections. Furthermore, the convolutional block attention module is integrated into skip connections, employing a dual-channel spatial weighting mechanism to suppress noise and emphasize key geological regions. Experimental results and field-data experiments demonstrate that U-STDRNet achieves a higher peak signal-to-noise ratio than the traditional U-Net. In addition, the model successfully restores fault and fold continuity details while exhibiting superior noise suppression compared to existing methods.

Neuroprotective effects of Gnetin H from Paeonia lactiflora via CREB-BDNF pathway restoration in a scopolamine-induced memory deficit model.

DiffSR: A Conditional diffusion model-based surface electromyography distorted signal recovery technique

Delay-based reservoir computing for signal recovery in optical communication systems

On the issue of low-frequency EEG generators and methods of their spatial localization.

Loss of signal (LOS) at intraoperative nerve monitoring (IONM) is defined as an>100 mV amplitude decrease and a >10% latency reduction and represents a predictor of postoperative impaired vocal cord motility (VCM). We aimed to evaluate if an intraoperative signal recovery (ISR) after LOS may predict a positive outcome of VCM. Among 5884 consecutive intermittent IONM-guided thyroidectomies (April 2021- March 2025) all the patients in whom a LOS was observed were evaluated. Topic and intravenous corticosteroids were administered to all of them. Eventual recovery was evaluated after 20minutes. Patients with anISRless than 50% compared to thebaseline were included. The rate of vagal signal (VS)ISR was defined as a percent from the minimum value: VS-recovery-VS-minimal/VS-predissection-VS-minumum. ISR was correlated to VCM (ROC curve analysis). Among 169 patients with LOS, 65 (38.5%) showed ISR, with 48 (73.8%) of themexhibiting normal VCM on postoperative day 1 (POD-1). The remaining 17 patients with impaired VCMon POD-1 recovered normal VCM on POD-15 (7-10.8%) or POD-30 (10-15.4%). The AUC for impaired VCM at POD-1 was 0.938 (95% CI: 0.849-0.983, p <0.0001) and the ISR cut-off was 13%, with a 94.1% sensitivity and a 89.6% specificity. All patients with ISR >31% showed normal VCM. All patients with ISR <13% exhibited impaired motility at POD-15 but recovered at POD-30. ISR can predict full recovery of VCM. ISR >31% is associated with normal postoperative VCM and staged thyroidectomy could be avoided in this subgroup of patients with LOS.

Millimeter-wave (mmWave) massive MIMO systems use many antennas. These systems offer high data rates. But using many radio frequency (RF) chains increases cost and power use. To solve this, lens antenna arrays are used. Energy is focused, allowing the use of fewer RF chains. However, this creates a new challenge. With fewer RF chains, it is hard to estimate the wireless channel. Accurate channel estimation is needed for good system performance. In beamspace, the channel is sparse. This shows that only a few values are large. The rest are close to zero. Because of this, the problem is seen as sparse signal recovery. AMP (Approximate Message Passing) is one popular algorithm used for this. A better version named LAMP (Learned AMP) uses deep learning. But it still does not give the best results. This paper proposes a new method GM-LAMP. It improves the channel estimation accuracy. It uses prior knowledge about the channel. It assumes that the beamspace channel follows a Gaussian mixture distribution. First, a new shrinkage function is created based on this distribution. Then, the original function in the LAMP network is replaced with the new one. As a result, a better deep learning model is developed. The final GM-LAMP network estimates the beamspace channel more precisely. It works well with both theoretical models and real-world data. Simulations show that GM-LAMP performs better than earlier methods. This approach combines math knowledge and deep learning. It shows that using prior information helps deep networks make smarter predictions. The proposed method offers better accuracy and is useful for future mmWave systems.

Abstract. The Limerick Syncline, part of the Irish Zn-Pb Orefield in southwest Ireland, represents a geologically complex and relatively underexplored region, despite hosting the Stonepark and Pallas Green Zn-Pb deposits. The mineral deposits in the Syncline are largely stratabound Zn-Pb systems hosted within Mississippian carbonates. In the area, a thick volcanic sequence overlies and interfingers with the carbonate host rocks, mineralisation and alteration. This has posed significant challenges to seismic imaging in the region, resulting in a poor understanding of the overall structural setting. This study presents an optimised seismic processing workflow tailored to these geological complexities and applied to a 2D seismic reflection profile. The workflow integrates information from newly acquired downhole and laboratory P-wave velocity data with first-arrival travel-time tomography to produce a new velocity model for post-stack migration. This resulted in better signal recovery and enhanced reflector coherence, in particular, reflection continuity. As a result, imaging of key stratigraphic boundaries, internal form lines and the lateral interfingering of volcanic and carbonate units was enhanced. Acoustic impedance analysis using laboratory density data enabled a better understanding of the origins of seismic reflectivity and a more confident geological interpretation of the laterally variable lithologies. A chaotic, low-amplitude seismic facies was recognised representing laterally persistent breccia corridors which may provide a practical indirect seismic proxy for significantly hydrothermally altered zones in the carbonates. Critically, two major previously unrecognised basin-scale faults were identified to the south of the Stonepark and Pallas Green deposits, bounding a significant (half-)graben. Thickness patterns and igneous packages indicate late Tournaisian to early Viséan syn-depositional faulting coeval with emplacement of the Limerick Igneous Suite, with subsequent Variscan inversion providing a net-zero displacement at the surface. These results expand the exploration research space beyond the known mineralisation areas, especially around normal faults on the southern flank of the Syncline.

ABSTRACT This paper introduces a Bayesian total least‐squares (B‐TLS) formulation to address the perturbed compressive sensing problem. Due to the presence of a nonconvex nonseparable penalty, applying the traditional alternating minimization method to this formulation poses mathematical and computational challenges. To mitigate this problem, we propose a Bayesian proximal alternating linearized minimization (BPALM) algorithm. In this algorithm, each subproblem is characterized by convexity and yields solutions that are explicit and analytical. We further demonstrate the convergence of the proposed algorithm towards a stationary point of the objective function. Numerical experiments in sparse signal recovery are conducted to exhibit the proposed algorithm's effectiveness and superiority, focusing on its computational efficiency and estimation accuracy.

Modulation and amplification are two fundamental processes in optoelectronics. While discrete implementations have achieved widespread success, the challenge of monolithically integrating sufficient gain and electro-optic bandwidth remains a significant barrier, limiting optical systems’ miniaturization and scalability. We unify these two functions in the Er-doped thin-film lithium niobate (Er:TFLN) platform, achieving a record-high internal net gain of 38 dB in a 9.16-cm-long waveguide amplifier. Meanwhile, leveraging the host material’s strong Pockels effect, we realize ultra wide-range electro-optic modulation with a bandwidth of 53 GHz and operation up to 170 GHz, fabricated alongside waveguide amplifiers using a zero-change process. Additionally, we validate this functional fusion through two signal processing scenarios: self-amplified digital signal encoding and pre-amplified broadband radio frequency front-end receiving, demonstrating improved signal recovery quality compared to off-chip gain. The modulation-amplification integration holds broad potential for increasing system complexity and network depth in applications such as optical interconnections, Lidar, and microwave photonics.

Distributed acoustic sensing (DAS) has attracted much attention in seismic data acquisition because of its low cost, anti-electromagnetic interference, and high acquisition density. Unfortunately, the acquired DAS records are usually accompanied by various kinds of complex noise, affecting subsequent interpretation and inversion. Traditional methods have difficulties in effectively attenuating the intense background noise. In general, the denoising task of DAS data is challenging. Recently, convolutional neural networks (CNNs) exhibit a good ability in suppressing the noise in DAS records. However, traditional CNN-based frameworks always have a relatively simple network architecture, bringing negative impacts on the denoising capability. To solve this problem, we propose a dual-branch dense network (DBD-Net) in this paper. Specifically, DBD-Net introduces a novel combination of dual-branch modules and an attention mechanism: the dual-branch modules extract multi-scale coarse-to-fine features, while the attention mechanism highlights the most informative features. This joint design strengthens feature representation and signal recovery compared with conventional CNN structures such as denoising CNN (DnCNN) and U-Net. Moreover, an attention module is employed to enhance the effective features. To verify the denoising ability, we compare DBD-Net with other competing methods, including band-pass filter, DnCNN, and U-Net, in terms of denoising capability and processing accuracy. Experimental results verify that DBD-Net can improve the quality of DAS records with a signal-to-noise ratio increment of nearly 26 dB. Meanwhile, the intense DAS background noise is also perfectly suppressed and the weak signals are effectively restored, representing advantages over the competing methods.

Agricultural products are usually susceptible to mycotoxin contamination; the rapid and reliable detection of mycotoxins at the early stage is crucial for food safety. Here, a photocurrent-polarity-switchable cathodic photoelectrochemical (PEC) aptasensing platform was established for fumonisin B1 (FB1) analysis based on a novel ZnS/Bi2S3/Bi2Ti4O11 (BTO) electrospun nanoheterojunction. Introducing S/Zn sources on Bi4Ti3O12 electrospun nanofibers induced the component transformation of Bi4Ti3O12 into BTO to generate ZnS/Bi2S3/BTO with double Z-scheme heterojunctions, thereby switching from anodic to cathodic photocurrent. On the photocathodic aptasensing interfaces of ZnS/Bi2S3/BTO, the FB1 aptamer-modified Au NPs/NiCo2O4 as peroxidase-like mimics accelerate the oxidation of 4-chloro-1-naphthol in a H2O2 environment to produce insoluble precipitates, quenching the photocathodic current with a 77.6% quenching efficiency. After incubation with FB1, the specific binding between FB1 and the aptamer could effectively reduce the catalytic sites of Au NPs/NiCo2O4, resulting in the recovery of photocathodic current signals for the detection of FB1 with a linear range of 0.5 pg/mL-1000 ng/mL and a detection limit of 0.385 pg/mL. In a word, this work for the first time synthesized the novel ZnS/Bi2S3/BTO double Z-scheme electrospun heterojunctions as highly active photocathodic materials, offering a new perspective in the advancing polarity-switchable PEC bioanalysis.

In modern communication systems, packets with different blocklengths often coexist, presenting new challenges for interference management and decoding. In scenarios where short-packet transmissions must meet strict latency and reliability requirements, conventional interference cancellation decoding strategies may be insufficient, especially when coexisting with long-packet services. This work proposes a novel interleaver design for polar codes that enables early decoding in successive interference cancellation (SIC) frameworks. To support this capability, a minimal yet essential modification to the interleaver used in the 5G New Radio (NR) polar coding scheme is introduced. This tailored interleaver facilitates the reliable recovery of short-packet signals before the complete decoding of coexisting long packets, substantially improving early decoding performance. Importantly, the proposed modification retains compatibility with the overall 5G NR polar code structure, ensuring practical implementability. Simulation results demonstrate that our approach yields significantly enhanced decoding accuracy in heterogeneous traffic scenarios representative of next-generation wireless systems.

Abstract Lack of sidewall fusion (LOSWF) is a critical defect in arc welding that compromises structural integrity, especially in multi-pass welds where buried discontinuities require highly advanced volumetric imaging techniques for detection. Traditional non-destructive testing (NDT) methods are often unable to identify such defects until fabrication is complete, increasing rework rates and overall build time. This study presents a novel approach, combining in-process ultrasonic imaging with controlled experimentation to enable LOSWF detection capability during welding. An experimental setup is introduced in which a static phased array probe is positioned ahead of the welding torch, allowing B-scan acquisition in real-time, during welding. Characteristic signal loss is observed prior to sidewall fusion, followed by echo recovery upon solidification—providing a dynamic indicator of fusion status, with a distinct amplitude drop from 60 to 0%, highlighting the binary nature of the monitoring. To benchmark detection limits, artificial LOSWF flaws were introduced into single-layer welds and evaluated using a roller probe configuration. In addition, experiments were performed to analyze signal degradation and recovery due to thermal disturbance, captured through C-scan sidewall echo analysis. The results demonstrate that ultrasonic imaging deployed during welding can offer both predictive and confirmatory information about fusion quality. This integrated approach provides a foundation for automated, embedded weld inspection systems that can identify fusion defects earlier in the process chain.

Abstract BACKGROUND Glioblastoma (GBM), primary lymphomas and metastases at times can have overlapping features on magnetic resonance imaging. In our study we used the advanced neuroimaging applications and calculated percentage signal recovery (PSR) in perfusion and apparent diffusion coefficient (ADC) on diffusion weighted imaging. METHODS This was a prospective study with recruitment of 54 treatment naive patients (107 observations). Post MRI histological diagnostic follow up, CSF analysis or PETCT correlation was done to confirm the radiological diagnosis. T2* DSC perfusion technique was used to obtain post contrast images, which were then processed to obtain relative cerebral blood volume (rCBV), PSR and relative percentage signal recovery (rPSR) of the lesion and perilesional area. RESULTS We analyzed 107 lesions out of which 39 were GBM, 44 were metastasis and 24 were lymphomas. •rCBV (lesion) shows hypoperfused lesions with mean values of 0.29 in PCNSL as opposed to both GBM and BM which show hyperfused lesions with rCBV ranging between 3.41 ± 2.32 and 2.78 ± 2.64 respectively. •Similarly, PSR (lesion) was also higher in PCNSL, in the range of 148.83 ± 22.37. PSR for GBM and BM had mean of 82.09 ± 9.66 and 81.4 ± 22.4 respectively. CONCLUSION There was a striking difference between the values of the perfusion parameters of PCNSL as against GBM and brain metastases, in terms of rCBV and PSR. The perilesional rCBV values were raised in GBM, indicating hyperperfused peritumoral areas, suggestive of infiltration. This was not observed in PCNSL or brain metastases. There was significant drop in ADC at b values 1000 x 10-3 mm2/s and 4000 x 10-3 mm2/s for PCNSL. The ADC values did not drop as much for GBM or brain metastases. ADC at b value of 4000 x 10-3 mm2/s was more sensitive than at 1000 x 10-3 mm2/s.

Deep Learning-Driven Recovery of Oversaturated Interferometric Signals for Continuous 3D Morphology Measurement of Complex Surfaces in OFC-TS-DFT Systems

Aiming to address the insulation and power supply challenges faced by electrical measurement in ultra-high voltage (UHV) environments, this study proposes and implements a nitrogen-vacancy (NV) center magnetic sensing system based on Power over Fiber (PoF) technology. The system adopts a high-voltage and low-voltage separation design, realizing the isolated transmission of electrical energy and the reliable recovery of measurement signals through an optical fiber link. The sensing unit on the high-voltage side is composed of NV center sensors, microwave excitation modules, and signal processing modules. Its power supply is provided by an independently developed high-power laser power converter (LPC) assembly via 830 nm optical fiber laser transmission. Under an optical input of 10 W, this assembly can achieve an electrical output of 4.88 W with a conversion efficiency of 48.9%. The experimental results show that the system can operate stably in a simulated UHV environment; by optimizing modulation parameters, the optimal magnetic measurement sensitivity reaches 6.1 nT/Hz1/2. This research provides a safe and reliable solution for the power supply and precise sensing of high-potential side equipment in UHV scenarios, and demonstrates the application potential of PoF technology in advanced sensing for power systems.

This paper focuses on the problem of recovering ternary sparse signals with s nonzero entries of 1 and −1. We propose three novel algorithms: ternary matching pursuit (TMP), ternary generalized orthogonal matching pursuit (TGOMP), and piecewise ternary generalized orthogonal matching pursuit (PTGOMP). First, inspired by the binary matching pursuit algorithm, we introduce the TMP algorithm, which assigns values of 1 or −1 based on the most correlated residual, and provide theoretical guarantees based on the mutual coherence, denoted by $µ$ and the restricted isometry property of the measurement matrix, respectively. Second, we propose the TGOMP algorithm, by selecting multiple $(M)$ indices at each iteration in order to improve the performance of the TMP algorithm. We establish a sufficient condition $µ &lt; 1/(2s − 1)$ that ensures the TGOMP algorithm selects $M$ correct indices and corresponding entries of $x$ in each iteration. Especially, all correct indices and entries can be selected in the first iteration. Additionally, we present a sufficient condition based on the restricted isometry property that guarantees all correct indices are selected in at most s iterations. Third, we propose the PTGOMP algorithm, which employs a piecewise selection strategy at each iteration, further improves recovery performance. Theoretical guarantees for the PTGOMP algorithm are derived based on the mutual coherence, showing its advantages in ternary sparse signal recovery. Finally, we validate the effectiveness of our algorithms through simulations and numerical experiments, demonstrating that combining appropriate matrix structures with suitable sparsity patterns can significantly improve recovery performance.

Abstract This paper investigates the computation of proximity operators for scale and signed permutation invariant functions. A scale invariant function remains unchanged under uniform scaling, while a signed permutation invariant function retains its structure despite permutations and sign changes applied to its input variables. Noteworthy examples include the $$\ell _0$$ ℓ 0 function, the ratio of $$\ell _1/\ell _2$$ ℓ 1 / ℓ 2 , and its square, with their proximity operators being particularly crucial in sparse signal recovery. We delve into the properties of scale and signed permutation invariant functions, delineating the computation of their proximity operators into three sequential steps: the $${\varvec{w}}$$ w -step, r -step, and d -step. These steps collectively form a procedure termed as WRD, with the $${\varvec{w}}$$ w -step being of utmost importance and requiring careful treatment. Leveraging this procedure, we present a method for explicitly and efficiently computing the proximity operator of $$(\ell _1/\ell _2)^2$$ ( ℓ 1 / ℓ 2 ) 2 and introduce an algorithm for the proximity operator of $$\ell _1/\ell _2$$ ℓ 1 / ℓ 2 . Numerical experiments on sparse signal recovery corroborate the analysis and show that first-order methods equipped with these proximity operators outperform $$\ell _1$$ ℓ 1 -based baselines in reconstruction accuracy.