Search Results (767)

In event-based neuromorphic processing, computer vision finds an efficient alternative capable of optimizing computational and energy resources, inspired by the dynamics of biological neural systems. In the development of real-time processing systems, it is crucial to visually represent the information captured by sensors and to explore its content with precision. Thus, machine learning models are implemented with the capability of being deployed on hardware devices with limited capabilities, depending on the intended purpose, ensuring savings in computational resources. The aim of this work was to evaluate the limits of the implemented neuron model, leaky-integrate and fire (LIF), for fitting convolutional layers of a neural network. To this end, the characteristics of the LIF neuron model used are summarized, as well as the details of its implementation in a hardware design, using configurable parameters. The experimental phase considered two convolution approaches to compare performance, Matlab R2022a software and a spiking convolutional processor for an FPGA, using sample recordings from the MNIST-DVS dataset and Sobel kernels for edge detection. The results reflect that the number of spikes generated by both approaches is very similar and their distribution by frame addresses is directly proportional. Full article

Background/Objectives: Umbilical–portal–systemic venous shunts (UPSVS) are rare fetal vascular anomalies with heterogeneous embryologic origins and variable perinatal implications. Although prenatal diagnosis has increased with advances in fetal imaging, data correlating prenatal subclassification with structural/genetic abnormalities and neonatal outcomes remain limited. Methods: This retrospective study included 50 fetuses prenatally diagnosed with UPSVS at a tertiary referral perinatology center between 2021 and 2025. Cases were subclassified according to the Achiron prenatal classification into Type 1 umbilical–systemic shunt (USS), Type 2 ductus venosus–systemic shunt (DVSS), Type 3a intrahepatic portosystemic shunt (IHPSS), and Type 3b extrahepatic portosystemic shunt (EHPSS). Prenatal ultrasound, Doppler, fetal echocardiography, and genetic testing (karyotype and chromosomal microarray) were analyzed. Perinatal metrics—including structural/genetic anomalies, fetal growth restriction (FGR), termination of pregnancy (TOP), and neonatal outcomes—were evaluated with postnatal verification. Results: The distribution of subtypes was Type 1: 28% (14/50), Type 2: 48% (24/50), Type 3a: 20% (10/50), and Type 3b: 4% (2/50). Gestational age at diagnosis was significantly higher in Type 3a compared with Type 1 and Type 2 (32.2 ± 2.4 vs. 21.1 ± 6.7 and 22.4 ± 5.8 weeks; p < 0.001). Structural anomalies were most frequent in Type 1 (13/14, 92.9%; p < 0.001), while FGR predominated in Type 3a (9/10, 90%; p = 0.006). Ductus venosus (DV) agenesis was universal in Type 1 (14/14) and Type 3b (2/2), absent in Type 2 (0/24), and present in 20% of Type 3a (2/10) (p < 0.001). Genetic abnormalities were detected in 57% of Type 1 (4/7) and 56% of Type 2 (9/16) fetuses, with trisomy 21 most prevalent in Type 2. TOP was highest in Type 1 (8/14, 57.1%; p < 0.001). Adverse neonatal outcomes occurred primarily in Type 1 and Type 3b (p < 0.001), whereas Type 2 demonstrated favorable neonatal outcomes. Conclusions: UPSVS subtype is strongly associated with structural/genetic anomalies, FGR, and neonatal outcomes, underscoring the importance of prenatal subclassification in prognostic assessment and counseling. Type 1 and Type 3b represent the highest—risk subgroups requiring delivery planning in tertiary centers, while Type 2 generally exhibits a benign perinatal course. The association between Type 3a and FGR highlights the need for detailed evaluation of the hepatic venous system in growth-restricted fetuses. However, interpretation of subgroup-specific associations should consider the relatively small sample size of Type 3b cases and the limited genetic testing performed in some Type 3a fetuses. Multicenter prospective studies are warranted to standardize diagnostic algorithms, optimize genetic testing strategies, and refine perinatal management. Full article

Background/Objectives: Standardized reporting of astigmatism outcomes is essential for comparability, reproducibility, and interpretation of refractive surgery studies. Vectorial analyses based on established metrics are increasingly required by major journals, yet no freely available tool exists for generating publication-ready vector analysis tables with statistical comparisons. This study presents AstigMETRICS, a standalone application for automated calculation, formatting, and statistical comparison of standard vector metrics in refractive surgery. Methods: AstigMETRICS was developed in MATLAB and compiled as a standalone executable requiring no programming knowledge. The software accepts preoperative, intended, and postoperative astigmatism data in spreadsheet format for both refractive and corneal measurements. It calculates seven standard vector metrics following the Alpins method: the target-induced astigmatism (TIA), surgically induced astigmatism (SIA), difference vector (DV), correction index (CI), magnitude of error (ME), angle of error (AE), and index of success (IOS). Statistical comparisons are performed automatically using appropriate parametric or nonparametric tests for paired and unpaired study designs, with p-values and Cohen’s d effect sizes reported. Results: AstigMETRICS generates standardized tables reporting the means, standard deviations, and clinically relevant proportions (percentage of eyes with an ME within ±0.50 D or ±1.00 D, and an AE within ±15°). Three simulated datasets were created to validate the software functionality across common surgical scenarios: a contralateral eye laser vision correction, toric phakic IOL implantation, and cataract surgery with toric IOLs. The output tables are displayed in standardized format and saved as high-resolution TIFF images suitable for publication. The software is freely available and a download link is provided in this article. Conclusions: AstigMETRICS enables clinicians and researchers to perform standardized, reproducible astigmatism vector analyses with built-in statistical comparisons. This freely available tool simplifies outcome reporting and improves methodological consistency in refractive surgery research. Full article

Background: Kawasaki disease (KD) is a systemic vasculitis of unknown origin, though recent evidence implicates viral pathogens in its pathogenesis. Given the central role of T cell receptors (TCRs) in antigen recognition and immune response, this study investigated the association between KD and viral infection through comparative analysis of TCR repertoires. Methods: TCR repertoires from KD patients, healthy children, and individuals with viral infections were comparatively analyzed. TCR diversity and V(D)J usage were assessed using Shannon’s entropy, the Mann–Whitney U test, and Fisher’s exact test. Positional motif enrichment analysis within CDR3 regions was performed based on paratope hotspot classification. Results: Relatively reduced TCR clonal abundance and diversity were observed in KD patients compared to healthy controls. While substantial overlap in VJ gene segment usage was detected between KD and cytomegalovirus (CMV) infection, limited overlap in clonal TCRαβ chains was found between KD and viral infection groups. A predominant TCR combination, TRAV14DV4-J13-TRBV20-1-J2-5, enriched with characteristic amino acid motifs (EET, YNE, LAG, GQG, and AYE), was frequently identified in KD. Conclusions: These observations suggest potential differences in TCR repertoire features between KD patients and both healthy and virus-infected groups. However, the relationship between KD pathogenesis and the viruses examined requires further investigation with larger cohorts. Full article

Abiotic stresses such as drought and heat increasingly threaten plant growth and ornamental quality, particularly in climate-sensitive floricultural crops. Arbuscular mycorrhizal fungi (AMF) are known to enhance plant resilience under such conditions, yet their role in lilies remains insufficiently explored. In this study, we used a two-tier experimental approach to evaluate AMF-mediated benefits in lilies. First, different AMF strains, namely Funneliformis mosseae (FM), Rhizophagus intraradices (RI), Rhizophagus irregularis (RIG), Claroideoglomus etunicatum (CE), Diversispora versiformis (DV), and a mixed consortium (MIX), were screened for growth-promoting effects in two Lilium species, Taiwan lily and Lilium cv. Sorbonne, under non-stress conditions. Second, a selected AMF–host combination from the screening was evaluated to improve tolerance to drought, heat, and combined drought + heat stress. Among the tested strains, DV and MIX showed the most consistent improvements across key growth traits and root colonization. In the stress experiment, stress treatments reduced growth and physiological performance, particularly under combined drought + heat. AMF inoculation enhanced plant performance by improving shoot and root biomass, improving root system architecture, and leading to a higher chlorophyll content, greater relative water content, and enhanced flower traits. Biochemical analyses further revealed that AMF mitigated stress-induced oxidative damage by reducing reactive oxygen species (ROS) accumulation, as shown by reduced O₂•⁻ and H₂O₂ staining. This reduction in oxidative stress was supported by increased activities of key antioxidant enzymes, indicating that AMF activate cellular defense mechanisms. These findings underscore the potential of AMF as a sustainable biotechnological tool for improving stress tolerance in lilies and enhancing floricultural productivity under climate-challenged environments. Full article

Plant leaf drought tolerance is regulated by the coordinated effects of water transport efficiency, transpirational water loss, and hydraulic safety. Although cotton is considered drought-tolerant, the mechanisms that coordinate water transport and gas exchange to confer drought tolerance remain incompletely understood. In this study, four soil moisture gradients were established under field conditions and maintained consistently throughout the growing season. The relationships among leaf turgor loss point (Ψ_tlp), gas exchange, and hydraulic traits were examined in two cotton cultivars at the peak flowering stage. With increasing drought treatments, Ψ_tlp, stomatal aperture ratio (g_ratio), leaf hydraulic conductance (K_leaf), leaf hydraulic conductance inside the xylem (K_x) and leaf hydraulic conductance outside the xylem (K_ox) declined significantly, with K_x showing the greatest reduction. Both K_x and g_ratio were strongly positively correlated with Ψ_tlp. Anatomically, vein density (D_v) and vessel number (Np) increased, whereas xylem vessel area (Ap) decreased. The reduction in Ap was the primary structural factor driving the decline in K_x and contributing to lower Ψ_tlp. We conclude that cotton enhances drought tolerance through a coordinated hydraulic and osmotic strategy, by modifying xylem anatomy (reducing Ap) to downregulate K_x and by adjusting osmotically to depress Ψ_tlp. The synergistic reduction in K_x and g_ratio slows the decline in leaf water potential, thereby delaying Ψ_tlp and enhancing leaf hydraulic safety during drought. This integration optimizes stomatal regulation and water transport while ensuring hydraulic safety. The findings provide a key theoretical basis and potential breeding targets for the targeted improvement of drought tolerance and water use efficiency in cotton. Full article

This study systematically investigates the flow characteristics, mixing efficiency, and concentration gradient generation (CGG) capabilities of three types of vertical-stepped main-channel microfluidic concentration gradient generators—the upward vertical-step (UVS-GG), downward vertical-step (DVS-GG), and straight horizontal channel (SHC-GG)—under different Reynolds numbers (Re) through numerical simulation and comparative analysis. Using numerical simulations, the research reveals the universal transition of flow regimes from diffusion-dominated to convection-dominated and reports the emergence of a “multi-peak oscillatory concentration gradient” phenomenon under stepped geometries and high Re (Re = 100, 200). The results indicate that the SHC-GG can generate monotonic gradients at low Re, making it an ideal baseline configuration. In contrast, UVS-GG and DVS-GG enhance mixing and enable the programming of complex concentration distributions through unique inertia–geometry coupling effects. The synergistic interaction between geometric configuration and Re is identified as the core mechanism for regulating concentration field morphology and device performance. This study provides key theoretical and design foundations for the rational design of microfluidic gradient generators targeting applications such as biological screening, chemical analysis, and material synthesis. Full article

Dating violence (DV) is an increasingly prevalent phenomenon among emerging adults (aged 18–25 years), and the relationship between awareness and behavior remains poorly understood. This study explores emerging adults’ attitudes and beliefs toward DV and summarizes recommendations for designing prevention programs. A qualitative study using three focus groups (n = 16 emerging adults aged 18–25; 56% female) was conducted. Data were collected via semi-structured interviews and analyzed using thematic analysis. Three main themes emerged: (1) gender roles, (2) healthy intimate relationships, and (3) dating violence. Participants demonstrated high awareness of DV types, severity, and prevalence. However, they also exhibited an attitude–behavior inconsistency, reflected in the normalization and excusing of violence, and difficulty recognizing violent situations in their own relationships. Myths of romantic love and cognitive dissonance between general knowledge and personal experience create barriers to recognizing abuse—particularly psychological abuse, which is often confused with concern. Participants suggested integrating prevention strategies into schools and communities, with interventions tailored to their interests and realities (e.g., mobile applications, games and social media awareness campaigns). This study reveals that awareness and knowledge alone are insufficient for prevention. Efforts should shift from a knowledge-focused to a behavior-change approach, promoting emotional regulation, interpersonal skills, and addressing social and gender norms. Relevant implications for practice and preventive intervention design are discussed. Full article

Spiking Neural Networks (SNNs) have attracted increasing attention due to their energy efficiency and suitability for neuromorphic data processing. Despite these advantages, the security of SNNs—particularly their robustness against backdoor attacks—remains underexplored. This study revisits fine-pruning, a widely adopted backdoor defense technique in deep neural networks, and adapts it to the unique spatio-temporal characteristics of SNNs. We propose two SNN-specific fine-pruning methods: Hook–Surrogate Gradient-based fine-pruning (HS-FP) and Spike–STDP-based fine-pruning (SS-FP). HS-FP leverages hook-based activation analysis with surrogate gradient learning, while SS-FP integrates total spike activity with hybrid STDP and surrogate gradient fine-tuning. We evaluate both methods against static, moving, and smart backdoor attacks on two neuromorphic benchmarks, N-MNIST and DVS128-Gesture. Experimental results show that both approaches reduce the attack success rate down to approximately 10% while preserving model accuracy above 99% on N-MNIST and achieving substantial recovery on DVS128-Gesture. Moreover, our analysis reveals that several phenomena observed in fine-pruning-based defenses for deep neural networks—such as mixed-function neurons and backdoor reactivation during fine-tuning—also manifest in SNNs. These findings highlight both the effectiveness and limitations of fine-pruning in the SNN domain and suggest promising directions for extending existing DNN security methodologies to neuromorphic systems. Full article

The deployment of Discrete Variable Quantum Key Distribution (DV-QKD) in high-traffic, short-reach environments, such as intra-data center networks, is currently constrained by the saturation of single-photon detectors. While CMOS Single-Photon Avalanche Diodes (SPADs) offer a cost-effective solution, their Secure Key Rate (SKR) is limited by detector dead time. To the best of the authors’ knowledge, this work is the first to derive a generalized detection rate model for SiPMs that addresses the dead-time bottlenecks of gigahertz-rate quantum cryptography. While methods for managing deadtime via active optical switching have been proposed, our model quantifies the benefits of passive spatial multiplexing inherent in standard SiPM arrays. Furthermore, contrasting with models designed to optimize energy resolution or characterize nonlinear charge response to light pulses, our work focuses on maximizing the detection count rate. We derive exact detection rate models for both analog (paralyzable) and digital (non-paralyzable) SiPM architectures, incorporating correlated noise sources such as optical crosstalk and afterpulsing. Simulation results indicate that SiPMs can increase detection rates by over an order of magnitude compared to single SPADs. Full article

The McMurdo Dry Valleys (DVs) of South Victoria Land, Antarctica, constitute the largest ice-free region on the continent and one of Earth’s most Mars-analog environments. Their hyper-arid polar desert conditions offer a unique setting for investigating surface weathering and mineralogical processes under extreme climates. This study presents the first regional-scale mapping of alteration and crystalline weathering minerals across the McMurdo DVs. It uses Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral data; visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands were analyzed through a Spectral Hourglass Workflow, endmember extraction, and spectral unmixing with Matched Filtering (MF) and Constrained Energy Minimization (CEM). Inter-algorithm consistency analysis between MF and CEM yielded 78.83% overall agreement with a Kappa coefficient of 0.75, indicating strong methodological consistency in mineral discrimination using ASTER VNIR+SWIR data. It should be noted that this agreement reflects internal algorithmic robustness rather than independent geological validation. Geological reliability is instead supported by documented field observations, lithological map comparisons, and spectral correspondence with the USGS spectral library. Validation employed documented field observations, lithological maps, and the USGS spectral library. Results reveal distinct spatial distributions of hematite-limonite/goethite, jarosite, kaolinite/smectite-illite-pyrophyllite-alunite, muscovite, hydrous silica/sericite/jarosite/hematite, epidote/chlorite, and calcite, closely associated with lithological units and unconsolidated deposits in Taylor, Wright, Victoria, and McKelvey Valleys. An inter-algorithm consistency check achieved 78.83% overall accuracy with a Kappa coefficient of 0.75, underscoring the robustness of ASTER VNIR+SWIR data for Antarctic mineral discrimination despite localized spectral mixing. Beyond refining the geological understanding of the McMurdo DVs, these results establish ASTER as an effective tool for regional mineralogical mapping in inaccessible polar terrains. The findings further strengthen the role of the Dry Valleys as a terrestrial analog for Mars, where similar mineralogical assemblages and spectral ambiguities have been observed, thereby contributing to both Antarctic geoscience and planetary exploration frameworks. Full article

In urban environments, autonomous vehicles face critical challenges in localization and perception under extreme lighting conditions, including rapid illumination changes, high contrast, and nighttime low-light scenarios. To address the performance degradation of traditional LiDAR-inertial odometry systems under such conditions, this study proposes a high-precision FAST-LIO2-EC algorithm that fuses event cameras into the FAST-LIO2 framework. Event cameras, with their microsecond temporal resolution and 140 dB dynamic range, provide asynchronous edge information that complements LiDAR point clouds and IMU measurements. We validate the proposed system through real-world road tests conducted on public roads and closed test tracks, covering three typical extreme lighting scenarios: tunnel entrance/exit transitions, high-contrast shadow boundaries, and nighttime sparse-lighting conditions. The experimental platform is equipped with a 32-beam LiDAR, a 6-axis IMU, a DVS event camera, and an RTK-GNSS system for ground truth trajectory acquisition. Real-world results demonstrate that the FAST-LIO2-EC system achieves significant improvements in localization accuracy and robustness. In illumination change scenarios, the Absolute Trajectory Error (ATE) is reduced by 32.5% compared to the baseline FAST-LIO2 system, with zero tracking loss events. The point cloud quality is substantially enhanced, with more uniform distribution and clearer obstacle boundaries. In high-contrast scenarios, both systems maintain comparable performance with ATE below 0.15 m. However, in nighttime scenarios, the fusion system shows moderate improvement (15.3% ATE reduction) but reveals sensitivity to event camera noise, indicating the need for adaptive thresholding strategies. Supplementary simulation experiments validate the system’s robustness under varying speeds and sensor noise levels. This work provides a practical solution for autonomous vehicle deployment in complex urban lighting environments, with a comprehensive analysis of real-world performance boundaries and deployment considerations. Full article

Dynamic Vision Sensor (DVS) is an event-based imaging technology inspired by biological photoreceptors, which holds great promise for edge computing. The event streams produced by DVS are often contaminated by Background Activity (BA) noise and hot-pixel noise, which degrade downstream processing. Existing filters typically use fixed parameters, resulting in poor adaptability to changing illumination. In this paper, we propose a lightweight Adaptive Event-based Filtering Spiking Neural Network (AEFSNN) to address these limitations. Inspired by homeostatic plasticity, AEFSNN dynamically adjusts neuronal thresholds by monitoring the input-to-output spike ratio, allowing the network to autonomously converge to an optimal operating point across different lighting conditions. Furthermore, we introduce a novel neuronal wake-up mechanism that inhibits processing neurons until triggered by valid input, which effectively suppresses redundant events generated by neighboring activity. Experiments show that AEFSNN is more robust under varying illumination. Compared with current filters, our method increases the Signal-to-Noise Ratio (SNR) of the output data by 1.42–2.33 dB. Additionally, the filtered data improves classification accuracy on downstream tasks, validating its practical value for neuromorphic vision systems. Full article

Digital village (DV) construction is the core driving force for high-quality development of the rural economy, and is a key strategy for achieving coordinated progress in urban development and rural revitalization. This study empirically analyzes the direct effect and enhancement mechanisms of DV construction on the high-quality development of grain production (HDGP) by panel data from 170 cities in China’s major grain-producing areas spanning 2013–2022; this study uses the CRITIC-EWM combined evaluation, two-way fixed effects, mediating effect and moderating effect model. The results show that: (1) HDGP appears more sluggish compared to the orderly growth of DV construction, but the level of DV construction and the level of HDGP are mismatched in spatial distribution. (2) DV construction has a significant promoting effect on HDGP, and the digitalization of economy and digitalization of life play more efficiently motivating role in HDGP. (3) This promoting effect is stronger in the population-shrinking regions than in the non-population-shrinking regions. (4) Approximately 8% of the promoting impact of DV construction on the HDGP is achieved indirectly through the scale of new agricultural business entities. (5) Government innovation planning exerts a significant enhancing moderating effect on the influence of DV construction on HDGP. Full article

In the context of digital certification systems, the demand for privacy-preserving authentication is increasingly vital, particularly for critical applications that involve sensitive personal data. Traditional digital signatures provide a robust means of implementing such systems. However, they raise significant privacy concerns due to their public verifiability, which allows verifiers to prove the authenticity of the received sensitive data to third parties. Universal designated verifier signature (UDVS) schemes address these privacy risks by offering non-transferability, ensuring that only the specified verifier can confirm the validity of the designated verifier signature (DVS). However, despite their advantages, existing UDVS models exhibit vulnerabilities that may allow tracking of the user’s authentications among cooperating verifiers and enable third parties to be convinced of the authenticity of sensitive user data by retrieving DVSs from different, non-cooperating verifiers. This paper presents a strategy to achieve strong non-transferability, which effectively addresses these vulnerabilities, by being the first to extend the concept of randomizability to UDVS schemes and their security properties. Our findings demonstrate that a randomizable UDVS scheme can serve as a solid foundation for constructing strong non-transferable UDVS schemes. Finally, we propose an efficient, strong, non-transferable UDVS scheme as an instantiation of our strategy, utilizing state-of-the-art Type 3 pairings, significantly improving upon previous constructions. Full article