Search Results (36,132)

Asymmetric error characteristics in spin-transfer torque magnetic random-access memory (STT-MRAM), particularly the imbalance between logical ‘0’ and ‘1’ error probabilities, can significantly degrade system reliability under conventional modulation and error-correcting schemes. This issue is especially critical in sensor network applications, where STT-MRAM is widely adopted for its non-volatility, low standby power, and robustness under energy-constrained and intermittently active operation. Existing approaches typically optimize the detection threshold under the assumption of a fixed or equiprobable bit distribution, while sparse coding techniques impose a predefined imbalance without explicitly accounting for its interaction with threshold detection. In this paper, we formulate the bit error rate (BER) minimization problem as a joint optimization of the codeword bit distribution and the detection threshold over an asymmetric cascaded STT-MRAM channel. Analytical results reveal that the minimum BER is achieved when the error probabilities associated with transmitted ‘0’ and ‘1’ bits are balanced, which induces an intrinsic coupling between the optimal detection threshold and the codeword composition. Motivated by this insight, we propose a new family of threshold-matched probability codes (TMPCs), in which the proportion of logical ‘1’s in each codeword is explicitly designed to match the optimal detection threshold of the underlying channel. The proposed coding framework generalizes conventional sparse modulation by enabling adjustable bit distributions while preserving low-complexity linear encoding and syndrome-based decoding. Numerical evaluations demonstrate that the TMPC achieves consistently lower BERs than existing sparse and fixed-distribution coding schemes across a wide range of STT-MRAM operating conditions, particularly under severe write asymmetry and resistance variation. These results indicate that the proposed joint design offers a principled and flexible approach for improving reliability in STT-MRAM-based sensor networks and non-volatile memory systems. Full article

Magnetic metals are of considerable importance for stealth technology and electromagnetic pollution control. However, they suffer from inherent limitations, such as the Snoek limit and narrow absorption bandwidth, which restrict their applications in complex scenarios. To address these challenges, this review systematically summarizes the recent advances of magnetic metal-based microwave-absorbing materials (MAMs), focusing on four core directions: alloy design, composite engineering, structural regulation, and preparation technology. The intensity and frequency bands of absorption in alloys are dictated by the material’s composition as well as its structural attributes. Moreover, composite systems incorporating carbon materials, MXenes, oxides, ceramics, and conductive polymers are discussed, where the synergistic design of components optimizes impedance matching and loss mechanisms. Key structural design strategies include core-shell structures, interface engineering, self-assembled hierarchical structures, and macroscopic structural design. These structures achieve the synergistic improvement of thin, lightweight, broadband, and strong absorption performance by enhancing interface polarization, multiple scattering, and resonance effects, while endowing materials with excellent environmental stability. Notably, metamaterial-based designs can further achieve an ultrawide bandwidth spanning 0.3–18 GHz. Additionally, preparation processes are crucial for regulating the microstructure and activating loss mechanisms. This review aims to offer theoretical and practical insights for developing high-performance, multifunctional magnetic MAMs. Full article

The proliferation of urban substations situated near residential areas has intensified the need for effective noise control, particularly in the mid-to-high frequency range. Traditional sound barriers often rely on mass-increasing strategies, which are constrained by the mass law and practical installation limitations. This study investigates a lightweight sound barrier solution utilizing an embedded Acoustic Black Hole (ABH) structure to address this challenge. Numerical simulations predict a significant improvement in the Sound Transmission Loss (STL) of the ABH plate compared to uniform plates. Experimental validation conducted in a specific cavity setup demonstrates that the damped ABH plate (2.97 mm thick, 3.47 kg) achieves a superior noise reduction performance, matching or even exceeding that of a significantly heavier uniform plate (4 mm thick, 5.00 kg) above its characteristic frequency (254 Hz), while realizing a 30% weight reduction. The superior performance is explained by two synergistic mechanisms: the ABH’s power-law profile concentrates bending wave energy for highly efficient damping at the thin tip; it compresses the structural wavelength, reducing radiation efficiency synchronously. The findings confirm the ABH structure as a promising, lightweight technology for controlling substation equipment noise, with broad application prospects in urban acoustic environmental protection. Full article

Machine learning (ML), including deep learning, kernel-based classifiers, and ensemble methods, is increasingly used to support clinical diagnosis in medical imaging, biosignal interpretation, and electronic health record (EHR)-based decision support. Despite rapid progress, many diagnostic AI studies still rely on limited retrospective evaluation and single summary measures (e.g., accuracy or AUC), creating a gap between reported model performance and evidence required for safe clinical adoption. This review proposes TRIAGE, a clinically grounded evaluation framework designed to organize diagnostic AI testing as an evidence pipeline aligned with real clinical use cases (screening, triage, second reading, and confirmatory testing). We summarize core discrimination metrics derived from the confusion matrix (sensitivity, specificity, predictive values, likelihood ratios, diagnostic odds ratio, and F-scores) and highlight the importance of prevalence and spectrum effects for interpreting predictive value and clinical workload. We further review evaluation strategies for multi-class and multi-label diagnostic tasks using appropriate aggregation methods (micro, macro, and weighted averaging) and set-based measures such as Hamming loss, exact match ratio, and Jaccard/IoU. Because diagnostic deployment is threshold-dependent, we integrate representation curves (ROC, precision–recall, lift, and cumulative gain) with calibration assessment and clinical utility analysis, including calibration slope, Brier score, and decision-curve analysis. We also address robustness and fairness evaluation, leakage-resistant validation designs (patient-grouped splits, stratified and temporal validation, and external validation), computational constraints relevant to deployment (latency, throughput, and energy use), and statistically sound model comparison with multiplicity control. A structured TRIAGE checklist table summarizing the evaluation parameters described in this review is provided in the main text to support reproducible and clinically interpretable reporting. Full article

Background and Objectives: Obstetric anal sphincter injuries (OASISs) are severe complications of vaginal delivery that can result in long-term pelvic floor dysfunction and reduced quality of life. Global data indicate a rising incidence of OASISs, including in Lithuania. This study aimed to identify risk factors for OASISs and evaluate their impact on urinary (UI) and fecal incontinence (FI), pelvic organ prolapse (POP), and quality of life in affected women. Materials and Methods: A retrospective case–control study was conducted at the Lithuanian University of Health Sciences Hospital (LUHS) Kauno Klinikos in 2024. Women who gave birth between 2004 and 2023 and experienced OASIS (n = 90) were compared with women matched for birth history but without perineal tears (n = 90). Data were collected from medical records and electronic questionnaires, including the International Consultation on Incontinence Questionnaire—Short Form (ICIQ-SF), Wexner score, Pelvic Organ Prolapse Symptom Score (POP-SS), and Pelvic Floor Impact Questionnaire (PFIQ-7). Participants were grouped by delivery year (2004–2013 or 2014–2023). Statistical analysis was performed using Mann–Whitney U, Chi-square, Fisher’s exact and Student’s t-tests, with p < 0.05 considered significant. Results: Newborn weight and vacuum-assisted delivery were significantly associated with OASIS (p < 0.05 and p = 0.029). In the 2014–2023 cohort, women with OASIS reported significantly higher rates and severity of UI, FI, and POP symptoms compared to controls. Quality of life scores related to UI and FI were significantly worse in the recent OASIS group, whereas no significant differences were observed in the 2004–2013 cohort. Conclusions: Between 2004 and 2023, 0.4% of women who gave birth at LUHS experienced third- or fourth-degree perineal tears, with newborn weight and vacuum extraction identified as risk factors. These women reported higher rates of UI and FI and POP, and those who delivered between 2014 and 2023 rated their related quality of life significantly worse than women without OASIS. Full article

Background: Persistent inflammation and endothelial dysfunction have been proposed as key mechanisms of post-COVID cardiovascular sequelae and may contribute to atrial fibrillation (AF). We examined whether inflammatory/prothrombotic biomarkers and endothelial function differ between post-COVID patients and controls, and whether baseline inflammation/endothelial dysfunction relates to AF burden at 12 months. Methods: In this single-center, retrospective observational study, 198 outpatients were enrolled: 99 post-COVID patients evaluated 3–6 months after documented SARS-CoV-2 infection (Group 1) and 99 age- and sex-matched controls without prior COVID-19 (Group 2). At baseline (t0), clinical characteristics, inflammatory/prothrombotic biomarkers, brachial artery flow-mediated dilation (FMD), and 24 h Holter ECG were assessed in both groups. Univariable linear regression tested associations between baseline variables and FMD in Group 1. At 12 months (t1), 24 h Holter ECG was repeated in both groups. Quartile analyses were performed according to baseline neutrophil-to-lymphocyte ratio (NLR) to explore AF distribution across inflammatory strata. Results: At baseline, post-COVID patients had higher inflammatory and prothrombotic markers than controls (ESR, CRP, fibrinogen, and D-dimer; all p < 0.0001) and markedly lower FMD (7.72 vs. 13.72; p < 0.0001). In Group 1, FMD was inversely associated with multiple inflammatory/prothrombotic markers (all p < 0.0001), with the strongest association for ESR (R² = 0.6297). Holter-detected AF prevalence at baseline did not differ significantly between groups (25/99 [25.3%] vs. 18/99 [18.2%]). At 12 months, AF prevalence was numerically higher in the post-COVID group (32/99 [32.3%] vs. 21/99 [21.2%]); on two-sided testing, this difference was borderline (p = 0.047) and should be interpreted cautiously. Across increasing baseline NLR quartiles, AF prevalence increased stepwise in both groups (post-COVID: 2/25, 5/25, 10/24, 15/25; controls: 1/25, 3/25, 7/24, 10/25), consistent with the enrichment of AF in higher-inflammatory strata. Conclusions: Post-COVID patients exhibited a persistent inflammatory–prothrombotic profile and pronounced endothelial dysfunction at baseline. At 12 months, AF burden was numerically higher post-COVID, and AF clustered in strata characterized by higher baseline NLR and lower FMD, consistent with an inflammation–endothelial dysfunction axis associated with subsequent AF burden. Prospective studies with standardized rhythm monitoring and comprehensive multivariable adjustment are warranted. Full article

Optical and synthetic aperture radar (SAR) image registration faces challenges from nonlinear radiometric distortions and geometric deformations caused by different imaging mechanisms. This paper proposes a coarse-to-fine registration algorithm integrating geographic information constraints with cross-modal feature consistency mapping. The coarse stage employs imaging geometry-based coordinate transformation with airborne navigation data to eliminate scale and rotation differences. The fine stage constructs a multi-scale phase congruency-based feature response aggregation model combined with rotation-invariant descriptors and global-to-local search for sub-pixel alignment. Experiments on integrated airborne optical/SAR datasets demonstrate superior performance with an average RMSE of 2.00 pixels, outperforming both traditional handcrafted methods (3MRS, OS-SIFT, POS-GIFT, GLS-MIFT) and state-of-the-art deep learning approaches (SuperGlue, LoFTR, ReDFeat, SAROptNet) while reducing execution time by 37.0% compared with the best-performing baseline. The proposed coarse registration also serves as an effective preprocessing module that improves SuperGlue’s matching rate by 167% and LoFTR’s by 109%, with a hybrid refinement strategy achieving 1.95 pixels RMSE. The method demonstrates robust performance under challenging conditions, enabling real-time UAV-based multi-sensor fusion applications. Full article

With the increasing development and utilization of natural resources, the importance of rock property characterization is becoming increasingly prominent. Artificial intelligence (AI) technology, with its rapid and accurate identification and analysis capabilities, is driving the evolution of a new generation of intelligent rock property analysis technologies. This paper systematically reviews the application and development trends of AI in rock property analysis. Key topics include: using AI methods to identify and analyze rock structure, composition, and texture; introducing commonly used AI models, analytical metrics, and public datasets in this field to help researchers more comprehensively evaluate model performance and match appropriate rock data; and summarizing AI solutions, future challenges, and coping strategies in four key areas of rock property analysis. This study emphasizes that the current application of AI methods to rock property analysis still faces challenges such as data quality, model generalization, and interpretability. To address these challenges, this paper proposes constructive suggestions, including the development of industry standards for intelligent rock analysis, the integration of petrological theory and fluid dynamics equations, and the adoption of weakly supervised learning strategies, in order to overcome existing technical bottlenecks. Full article

Multi-group homomorphic encryption (MGHE) is a pivotal advance in secure multi-party computation, integrating merits of multi-party homomorphic encryption (MPHE) and multi-key homomorphic encryption (MKHE) to eliminate MPHE’s fixed-party limitation and mitigate MKHE’s ciphertext expansion from dynamic enrollment. However, the efficient single-key FINAL scheme cannot extend to multi-party scenarios due to the challenge of defining valid multiplication for vector NTRU ciphertexts, which hinders its use in multi-group bootstrapping and curbs efficiency. To address this, additive secret sharing was adopted to convert vector NTRU ciphertext multiplication into secret share multiplication, enabling shared bootstrapping key generation within groups. A new multi-group ciphertext bootstrapping algorithm for MGHE was developed via the integration of LWE and NTRU cryptographic primitives. Bootstrapping tasks were decomposed for parallel processing, and a hybrid product algorithm was designed to aggregate subtask outputs, boosting multi-group bootstrapping speed to match that of single-key ciphertexts. Noise accumulation was analyzed, with 100-bit and 128-bit security parameter sets selected for validation. Experiments showed that 30- and 50-party multi-group bootstrapping takes only 1.87 s and 2.58 s respectively. Full article

Amid global climate change and rapid urbanization, megacities such as Shanghai confront prominent ecological challenges. A critical issue is the growing mismatch between the supply of and demand for urban green space (UGS) ecosystem services. This study aims to explore the supply–demand compatibility of Shanghai’s UGS ecosystem services and simulate future scenarios. Guided by the SSP1-2.6 scenario, it integrates the PLUS model, InVEST model, and nSFCA method to conduct dynamic analysis, quantifying supply–demand alignment and identifying imbalance areas. Results show a significant spatial mismatch: high demand but low supply in Shanghai’s inner ring and low demand but high supply in the outer ring. UGS attractiveness presents a core-concentrated and peripheral-diffused pattern by level. By 2030, a coordinated supply framework of “city-level dominance, community-level support, and neighborhood-level supplementation” will form, improving supply–demand alignment, though accessibility gaps persist. The study reveals that urbanization, planning policies, and population–spatial expansion asynchrony drive these patterns, providing scientific decision-making support for optimizing Shanghai’s green space planning and building an ecologically livable city. Full article

Generative image steganography is a key technology for secure information transmission, but existing deep learning-based generative steganographic methods suffer from an extremely high bit error rate (BER) and degraded steganographic image quality in low-bit-rate embedding tasks in which secret information needs duplication or padding to match the model input size. In addition, it is difficult to balance BER reduction and imperceptibility of stego-images. To address these issues, this paper proposes a novel generative image steganography algorithm based on flip watermarking, with the core novelty of designing a mirror flipping preprocessing mechanism to achieve a redundant watermark and eliminate information errors caused by duplication or padding, and constructing an end-to-end Mini-Hide steganographic framework to integrate flip watermarking with generative steganography for the first time. Specifically, the proposed method first converts the binary bitstream of secret information into a square matrix, and performs vertical, horizontal and vertical–horizontal mirror flipping on the matrix to form a redundant basic watermark, which is then expanded to a secret image with the same size as the cover image. After that, the secret image is preprocessed by a preparation network and then input into an encoding network together with the cover image to generate a stego-image. Finally, the generated stego-image is input into the decoding network to extract the secret image. Subsequently, the inverse operation of flip watermarking is performed on the extracted secret image to recover the original binary bitstream. Extensive experiments are conducted on the public COCO dataset ($256\times 256$ pixels) with BER, PSNR, and SSIM, and the proposed method is compared with state-of-the-art generative steganographic methods. Quantitative results show that the proposed method achieves a $0\%$ BER for secret information of $8\times 8$ to $64\times 64$ bits, and the BER is only $0.00002\%$ for $256\times 256$-bit secret information; the PSNR of stego-images reaches 37.75 dB, and the SSIM hits 0.96, which are 7.07 dB and 0.02 higher than those of the classic HiDDeN method ($64\times 64$ bit) respectively. We also validated the flip watermark module by integrating into other methods; the results also show that the PSNR of FNNS-D is improved by 13.12 dB ($256\times 256$), and the BER of SteganoGAN is reduced by $99.99\%$ ($256\times 256$ bit). In addition, the proposed method breaks the embedding size limit of HiDDeN (≤$64\times 64$ bit) and supports up to $256\times 256$-bit secret information embedding with stable performance. This work significantly reduces the BER of generative image steganography while improving the visual quality of stego-images, provides a new preprocessing and optimization scheme for low-BER generative steganographic algorithm design, and also offers a universal lightweight module for performance improvement of existing steganographic methods, which has important theoretical and practical significance for enhancing the security and reliability of covert information transmission in the field of information security. Full article

Background: Speckle-tracking echocardiography (STE) has been increasingly used to uncover subtle cardiac dysfunction in patients with extracardiac sarcoidosis (ECS) who show no clinical evidence of heart disease. However, prior investigations were mostly retrospective, methodologically heterogeneous, and focused primarily on left ventricular (LV) function. We conducted a prospective study to provide a broader evaluation of myocardial deformation across both ventricles and atria in ECS without fibrotic pulmonary involvement. Methods: Forty-one patients with ECS (mean age 57.4 ± 10.2 years; 58.5% male) and 30 age- and sex-matched controls without ECS and without known structural heart disease (58.5 ± 11.1 years; 53.3% male) were enrolled. All participants underwent conventional transthoracic echocardiography (TTE) supplemented by comprehensive STE analysis of ventricular and atrial function. Subclinical myocardial dysfunction was defined as LV global longitudinal strain (GLS) less negative than −20%, and potential predictors were analyzed. Results: Standard TTE did not show echocardiographic features suggestive of overt infiltrative cardiomyopathy but revealed higher E/average e′ ratios in the ECS group, suggesting subtle diastolic dysfunction. While traditional indices of biventricular systolic function remained preserved, STE demonstrated significant reductions in LV-GLS, LV global circumferential strain, right ventricular-GLS, and both left and right atrial reservoir strain. Multivariate analysis identified disease duration as the sole independent determinant of LV-GLS impairment (OR 2.26, 95%CI 1.10–4.65; p = 0.03). A disease duration of ≥4.5 years predicted abnormal GLS with 88% sensitivity and 75% specificity (AUC 0.89; 95%CI 0.76–1.00). Conclusions: ECS without fibrotic pulmonary involvement is associated with early impairment of biventricular and biatrial strain despite preserved conventional function. The extent of dysfunction correlates strongly with disease duration, underscoring the value of STE for early detection and monitoring. Full article

Addressing the challenge of stable cloud velocity field estimation under complex sky conditions in ground-based cloud imaging, this paper proposes a comprehensive 3D cloud velocity calculation framework. The methodology integrates binocular stereo vision geometry, self-supervised deep feature learning, and graph attention-based matching. First, a self-supervised feature detection and description model tailored to the radiometric characteristics of cloud images is developed. By incorporating a homography adaptation strategy constrained by physical priors, the model acquires robust feature representations for weakly textured and highly deformable cloud masses without requiring labeled datasets. Subsequently, a Transformer-based graph neural network matcher is employed to establish global feature correspondences across both cross-view and cross-temporal dimensions, thereby substantially augmenting matching robustness. On this basis, the framework establishes a rigorous calibration model for fisheye cameras to derive cloud base height (CBH) via binocular geometry. These geometric constraints are then coupled with sequential feature tracking results to construct 3D velocity inversion equations, enabling an end-to-end mapping from 2D pixel coordinates to 3D physical space and providing direct estimation of physical cloud motion velocity in meters per second (m/s). The experimental results show that the proposed method extracts 4.5 times more feature points than the traditional SIFT method. Furthermore, the Pearson correlation coefficient for cloud motion trends in continuous sequences reaches 0.662 relative to baseline models, indicating good relative consistency in motion estimation. The framework achieves high-precision and stable velocity estimation across diverse cloud types, including cirrus, cumulus, stratus, and mixed clouds. Full article

Artificial intelligence (AI) has been explored as a promising diagnostic aid for ocular surface diseases (OSDs). The spectrum of OSD ranges from highly prevalent benign conditions such as dry eye disease (DED) to rare but potentially dangerous disorders, including ocular surface squamous neoplasia (OSSN) and conjunctival melanoma. This review provides an overview of current applications of AI across the major categories of ocular surface pathology and specifically highlights anterior segment imaging modalities, including slit-lamp examination, optical coherence tomography (OCT), and in vivo confocal microscopy (IVCM). Meibography, tear film dynamics, biochemical profiling, and other DED-related measures are also examined. Across these domains, reported AI model performance matches or exceeds that of ophthalmologists, offering consistent, reproducible, and accurate approaches for guiding diagnosis. However, studies with limited external or prospective validation, variable labeling strategies, and small, device-specific datasets predominate in the current literature, thereby limiting generalizability. Large multicenter datasets, standardized diagnostic frameworks, multimodal integration, and prospective trials that assess human–AI cooperation in practical settings should be an emphasis in future research. By filling these gaps, AI systems could advance from experimental tools to clinically reliable applications that improve access and diagnostic accuracy in the care of ocular surface disease and tumors. Full article

Unmanned aerial vehicles (UAVs) have revolutionized plant protection spraying due to their high efficiency and adaptability. However, the lack of rapid, portable tools for assessing droplet deposition remains a bottleneck for optimizing spray quality and improving pesticide utilization. The main purpose of this study is to develop a portable, image-based detection device with improved algorithms for real-time analysis (<3 s per card) of droplet deposition on spray cards during UAV plant protection spraying, addressing the limitations of existing methods in portability, real-time capability, and field robustness. This study presents a portable detection device integrated with advanced image processing algorithms for real-time analysis of droplet deposition on copperplate paper cards during UAV operations. The device employs a Raspberry Pi 5 as the core processor, coupled with a high-resolution camera and a standard chessboard calibration board for field-portable image acquisition. Key innovations include an adaptive background subtraction and local contrast enhancement method to address variable field lighting conditions, and an improved adhesion droplet segmentation algorithm combining iterative morphological opening operations with refined distance transform-based concave point matching. Validation on 21 field-collected cards using ImageJ as reference demonstrated a droplet extraction accuracy of 89.4%, with coverage rate improvements of 25.4% and 15.2% compared to OTSU and block thresholding methods, respectively. The adhesion segmentation relative error averaged 6.3%. This low-cost, lightweight device provides farmers and researchers with an effective tool for on-site spray quality evaluation, contributing to precision agriculture and reduced pesticide waste. Full article