Search Results (3,573)

With advances in robotic manipulation in recent years, tactile sensing has become increasingly important in scenarios where visual information is unreliable or insufficient. However, the development of learning-based tactile algorithms and policies is limited by the cost and time required to collect large-scale physical datasets. While robotic simulation offers an alternative for synthetic data generation, current simulation platforms, such as NVIDIA Isaac Sim, lack integrated capacitive tactile sensors. This paper presents a finite element method (FEM)-based digital twin of a capacitive tactile sensor and an extension for NVIDIA Isaac Sim that enables the real-time generation of synthetic tactile data directly within the simulation environment. The proposed system extracts nodal deformations from the Isaac Sim PhysX engine and uses a convolutional neural network (CNN) to predict synthetic tactile maps that replicate the response of the physical sensor. We further demonstrate adaptability by retraining the model from an initial sensor to a second capacitive sensor, the Robotiq TSF-85, operating under the same sensing principle. The complete generation pipeline executes in 8.04 ms per frame on the laptop configuration and 6.71 ms on the workstation, enabling real-time operation at 60 Hz on both, and at 120 Hz on the workstation. The similarity of the generated tactile data for the Robotiq TSF-85 is evaluated using complementary similarity metrics, achieving a mean Structural Similarity Index Measure (SSIM) of 0.727 ± 0.16 and a mean Pearson correlation of 0.87 ± 0.16 against real measurements. To demonstrate the utility of the proposed framework, a shape-recognition task (cylinder, sphere, cube) was performed using only synthetic tactile data and evaluated on real-world sensor data, achieving an accuracy of 69.3% with zero real training labels. By enabling integrated tactile simulation and synthetic data generation within Isaac Sim, this work provides a practical tool for tactile perception using capacitive tactile sensors. Full article

Data Centers that implement Software-Defined Networks (SDN) are not required to employ the Address Resolution Protocol (ARP), but network hosts do. Therefore, there is a need to support this protocol without modifying the intrinsic functionality of the SDN controller. In this work, four strategies for handling ARP are evaluated using an SDN and OpenFlow rules. The strategies include disabling ARP at the host level, using static MAC addresses, introducing a fake gateway, and generating ARP replies using OpenFlow flows. To our knowledge, nobody has tested and compared the main characteristics and advantages offered by these four strategies. Experimental evaluation was conducted on a real SDN network and complemented with similar experiments using Mininet. Performance was assessed using metrics such as ping response time, address resolution response time, jitter, and packet loss ratio. The results show that OpenFlow-based ARP replies provide a good balance in terms of scalability, performance, and configuration effort. This strategy achieved the lowest average ping response time (0.641 ms) and ARP response time (0.6188 ms), while avoiding the manual configuration requirements of static approaches. Full article

Reliable cervical cytology classification remains difficult when rare diagnostic categories are represented by only a few labeled examples and exhibit substantial morphological overlap with neighboring classes. This study introduces HardNegRect, a lightweight inductive prototype-rectification module designed for low-support classification among fixed cervical cytology categories. The method constructs a class-specific hard-negative reference from the most similar competing prototypes and uses this inter-class context to predict a bounded, gated residual correction to each support-derived prototype. Because rectification depends exclusively on support information, the approach preserves independent query processing and avoids transductive access to the test distribution. HardNegRect was evaluated on two public cervical cytology benchmarks using common fold assignments, support sizes, held-out query sets, and draw-level metric aggregation for frozen-feature, metric-based, and optimization-based comparators. The study also includes a controlled component ablation study, a neighborhood-sensitivity analysis, and an additional-seed stability analysis. On Mendeley LBC, the clearest benefit occurred in the one-shot setting, where HardNegRect achieved a Macro-F1 of $0.9862\pm 0.0062$ and an SCC F1 of $0.9655\pm 0.0216$. On SIPaKMeD, the default $K_{\mathrm{hn}}=2$ configuration achieved Macro-F1 values of $0.9596\pm 0.0052$, $0.9626\pm 0.0048$, and $0.9616\pm 0.0048$ for $K=1,3,10$, respectively, numerically exceeding the strongest comparator mean at each support size. The controlled component ablation study associates the additional one-shot gain on Mendeley LBC with inter-class prototype correction rather than with embedding transformation alone. Overall, HardNegRect provides a lightweight, parameter-efficient, and geometry-aware extension to prototype-based low-support cytology classification, while patient-grouped, source-grouped, repeated-seed, and multi-center validation remain necessary before clinical generalization can be established. Full article

Chinese local gazetteer maps contain visual evidence of historical cities and architecture, but their hand-drawn, low-texture, and small-sample characteristics make automatic recognition difficult. This study develops a transfer-learning workflow to classify gazetteer-derived architectural images and examines its usefulness for digital heritage image organization, indexing, and retrieval. A dataset of 685 images covering nine classification labels was constructed from Chinese local gazetteer maps. Five representative models, ResNet-18, MobileNetV3-Small, EfficientNet-B0, DenseNet-121, and ViT-B/16, were trained and compared under the same transfer-learning setting. Their performance was evaluated using accuracy, macro-average F1 score, class-level metrics, confusion matrices, difficult-sample analysis, and Grad-CAM visualization. CNN-based models outperformed the Vision Transformer on this task. EfficientNet-B0 achieved the best results, with a test accuracy of 87.38% and a macro-average F1 score of 86.60%; after disabling class weights, the macro-average F1 score increased to 86.81%. Misclassifications mainly occurred in visually similar or context-dependent category pairs, including gate tower and drum tower, bell tower and drum tower, and buildings inside and outside the city. The results show that compact CNNs can support classification, indexing, and retrieval of hand-drawn architectural images in gazetteer archives, although the workflow should be regarded as an auxiliary tool rather than direct evidence for specific extant buildings. Full article

Accurate modeling of wind turbine power curves is essential for performance assessment, energy forecasting, condition monitoring, and operational optimization in wind energy systems. This study presents a SCADA-based comparative assessment of established power curve modeling approaches through a single-site case study conducted on a low-power Vertical Axis Wind Turbine (VAWT) operating under real environmental conditions at the University of the Isthmus, located in the Isthmus of Tehuantepec, Oaxaca, Mexico. The evaluated methods included the maximum power curve, an aerodynamic model based on Blade Element Momentum Theory (BEMT), parametric approaches using polynomial and logistic regressions, and non-parametric data-driven methods based on Random Forest (RF), Gaussian Process Regression (GPR), and Kernel Density Estimation (KDE). One year of SCADA data, including wind speed and generated power measurements, was analyzed, while model performance was assessed using RMSE, MAE, and R² metrics. The results showed that the machine learning approaches achieved the lowest prediction errors among the evaluated models, with RF providing the best overall performance (RMSE = 7.4%, MAE = 4.8%, R² = 0.9814), followed by GPR and KDE under the investigated operating conditions. Additionally, Weibull analysis yielded parameters of k = 1.887 and c = 7.875 m/s, while the largest prediction errors were observed within the partial-load operating region (approximately 4–10 m/s) and as the turbine approached the rated operating condition (approximately 10–12 m/s). These findings indicate that, for the investigated low-power VAWT operating at the experimental site, non-parametric approaches provided the most accurate representation of the power curve among the evaluated models, highlighting the potential of SCADA-based data-driven techniques for comparative model assessment under similar operating conditions. Full article

The rapid advancement of quantum technologies has led to a growing and diverse landscape of quantum computing devices, each with varying architectures and capabilities. Most of them are evaluated using hardware-centric benchmarks such as gate fidelity, quantum volume, or coherence time. However, these metrics often fail to capture performance in problem-specific contexts, particularly in applications involving quantum image processing. In this work, we address this gap by introducing the Quantum Image Encoding Fidelity (QIEF) metric, a novel task-specific benchmark designed to evaluate how accurately quantum devices can encode classical image data into quantum representations. By focusing on image-specific criteria rather than device-agnostic parameters, QIEF offers a more application-relevant assessment of device capability, enabling a direct comparison of quantum hardware based on their effectiveness in real-world image-related tasks. We present the theoretical formulation of the QIEF, validate its relevance through quantum simulators and experiments on real quantum devices, and discuss its implications for guiding future hardware development tailored to quantum image processing applications. Full article

Background: In our recent research, we established a strong and statistically significant association between the highest quartile of lipoprotein(a) [Lp(a)] and mildly reduced estimated glomerular filtration rate (eGFR). Comparisons with similar studies were hindered by varying Lp(a) analytical methods and different eGFR equations. We analyzed how replacing the diagnostic standard CKD-EPI 2021 equation with alternatives (CKD-EPI 2009, CKD-MDRD, CKD-EKFC) affects predictive models under comprehensive demographic and clinical confounder adjustments. Methods: We calculated eGFR for 310 adults using the four equations. Creatinine and Lp(a) were measured via IFCC-recommended methods. The cohort was divided into a main (eGFR 60–80 mL/min/1.73 m²) and a control group (eGFR > 80 mL/min/1.73 m²). Multivariable logistic regression and Area Under the Curve (AUC) metrics evaluated model performance. Results: Alternative equations systematically underestimated eGFR, artificially increasing the reduced filtration group. After full multivariable adjustment, only CKD-EPI 2021 preserved the independent association between the highest Lp(a) quartile and mildly decreased eGFR (OR = 2.65, p = 0.008), achieving an AUC of 0.732. Conversely, CKD-EPI 2009 lost significance; CKD-MDRD exhibited mathematical instability from control group depletion, and EKFC showed severe over-adjustment when demographic covariates were included. Conclusions: Older equations and the age-embedded EKFC equation may influence regression models, potentially obscuring the true biomarker associations. CKD-EPI 2021 demonstrated the highest precision, successfully isolating physiological aging and suggesting an independent association between high Lp(a) and mildly reduced eGFR, irrespective of metabolic and vascular confounders. Full article

The taxonomic status of the H17N10 and H18N11 influenza A viruses isolated from bats remains unclear due to the absence of quantitative classification criteria at this taxonomic level. A total of 3328 representative IAV genomes, encompassing all eight protein-coding segments, were analysed. Various genetic distance-based metrics were assessed at the pairwise level, including intra- and intergroup nucleotide distances, dN/dS ratios, and transition/transversion ratios, to facilitate the differentiation of the Alphainfluenzavirus genus into distinct taxa. Pairwise distances for seven of the eight segments (PB2, PB1, PA, NP, M, NA, NS) consistently differentiated the H17–H18 group from H1–H16. Across segments, intergroup nucleotide divergence was consistently above a lower bound of ~25%, with segment-specific values extending to higher levels (up to ~40% in PB2 and PA), while intragroup divergence remained substantially lower. The HA segment did not conform to this pattern, which is consistent with the hypothesis of ancient reassortment. The distribution of pairwise dN/dS values for the PB2, PB1, PA, and NP segments is evidently bimodal. Intergroup comparisons were consistently higher across all segments, whereas intragroup values remained lower. A similar lower boundary of approximately 0.12 was observed across segments, while the upper range of intergroup values varied by gene. Overall, the results support a consistent gene-specific separation pattern. Previously demonstrated absence of reassortment compatibility between bat viruses (H17–H18) and canonical influenza A (H1–H16) viruses indicates that these lineages have evolved independently over an extended period. These consistent genomic patterns provide support for the hypothesis that H17N10 and H18N11 viruses may represent a separate species within the genus Alphainfluenzavirus. Full article

Background: Medical imaging datasets often suffer from class imbalance and limited availability of pathology-rich cases, which limit the performance of machine learning models for segmentation, classification, and vision–language tasks. We propose POWDR, a pathology-preserving outpainting framework for 3D MRI that uses real pathological regions as conditioning evidence while generating anatomically plausible surrounding tissue. Methods: Our approach leverages wavelet-domain conditioning to enhance high-frequency detail and mitigate blurring common in latent diffusion models. We introduce a random connected mask training strategy to reduce conditioning-induced collapse and improve diversity outside the lesion. POWDR is evaluated on brain MRI using BraTS datasets and extended to knee MRI to assess applicability beyond brain imaging. Results: Quantitative metrics (FID, MS-SSIM, LPIPS) were used to assess image realism. Random connected mask training improved diversity, reducing cosine similarity from 0.9947 to 0.9580 and increasing KL divergence from 0.00026 to 0.01494. To validate pathology preservation, we compared lesion overlap, volume, intensity, and morphology. For downstream segmentation, nnU-Net performance improved from 0.6992 to 0.7137 Dice after augmentation with 50 synthetic cases, representing a modest but statistically significant improvement (paired t-test, p = 0.016). Tissue volume analysis showed no significant differences for CSF and GM compared to real images, while WM volume was lower in synthetic images. Conclusions: POWDR provides a framework for generating diverse, pathology-preserving synthetic MRI data. The results suggest potential utility for data augmentation while maintaining clinically relevant lesion characteristics. Full article

Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (η_p² = 0.63). CIELUV v* explained the greatest model variation (R²m = 0.94) and responded most to fertilizer (η_p² = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems. Full article

Background: Bacillus clausii SKB/BCL21 (MCC 0569) is a novel strain that shows promise as a probiotic for both human and animal healthcare. Objectives: The objective is to evaluate the safety profile of B. clausii SKB/BCL21 through genomic and toxicity assessments in Wistar rats, as well as to assess its efficacy as a probiotic in broiler chickens challenged with Clostridium perfringens. Methods: The identification of genus and species was performed using 16S rRNA and whole genome sequencing (WGS). A genomic analysis was conducted through bioinformatic screening of the B. clausii SKB/BCL21 genome to identify virulence factors, genes encoding toxins, mobile genetic elements, and antibiotic resistance genes. In vitro biosafety assays were conducted to evaluate mucin degradation, gelatinase, hemolytic activity, and DNase activity. The in vivo safety evaluation was performed by acute and subacute oral toxicity studies as per the OECD 423 guidelines. In efficacy testing broilers challenged with C. perfringens were administered with low dose (1 × 10⁸ cfu/kg of feed) and high dose (1 × 10⁹ cfu/kg of feed) of B. clausii SKB/BCL21. Performance metrics, such as average weight gain, feed conversion ratio (FCR), and mortality rates, were evaluated in comparison to a positive control group that received Virginiamycin 50% (15 ppm). Results: The isolate SKB/BCL21 was identified as Bacillus clausii based on 16S rRNA and whole genome sequencing (WGS). The bioinformatic analysis of the B. clausii SKB/BCL21 genome reveals that it lacks genes associated with toxins, mobile genetic elements, and virulence factors. However, it does contain intrinsic and non-transferable antibiotic resistance genes within its chromosomal DNA. In the acute toxicity study, an oral dose of 2000 mg/kg (400 billion cfu/kg) body weight was found to be nontoxic. The No Observed Adverse Effect Level (NOAEL) for B. clausii SKB/BCL21 was found to be 1000 mg/kg (200 billion cfu) body weight/day by oral route in the subacute toxicity study. The findings of in vivo toxicity studies indicate that there were no treatment-related changes in any of the endpoints assessed. The effects of low (1 × 10⁸ cfu/kg of feed) and high (1 × 10⁹ cfu/kg of feed) doses of B. clausii SKB/BCL21 on the growth performance metrics, including average weight gain, feed conversion ratio, and mortality rates in broiler chickens infected with C. perfringens, showed results similar to those of the positive control (Virginiamycin 50%, 15 ppm). Conclusions: Based on these preliminary studies, B. clausii SKB/BCL21 can serve as a potential alternative to antibiotic growth promotors in broiler production. These results suggest that the B. clausii SKB/BCL21 is safe and could be a potential probiotic for animal feed supplements. Full article

Soybean seed variety identification is essential for seed quality control, germplasm management, and variety authentication. However, few-shot classification remains challenging because different varieties often exhibit highly similar one-dimensional hyperspectral signatures, and labeled samples are limited in practical seed-testing scenarios. This study proposes a Residual Derivative-Guided Spectral Fusion (RDSF) module to improve spectral representation under limited-sample conditions. RDSF uses the raw spectrum and its first- and second-order derivatives to characterize global reflectance patterns, local slope variations, and spectral curvature, respectively. The three representations are processed by separate branches and combined through bounded learnable residual fusion, with the raw spectrum serving as the primary representation and the derivatives providing complementary corrections. As a plug-and-play component, RDSF was integrated into Prototypical Network (ProtoNet), Relation Network (RelationNet), and Model-Agnostic Meta-Learning (MAML). The module was evaluated using spectra from 11,000 individual soybean seeds representing 11 varieties under known-class and strict class-disjoint unseen-class protocols. Under the representative known-class 3-way 10-shot setting with 15 query samples per class, RDSF increased the meta-test accuracy of RelationNet from 0.8898 ± 0.0201 to 0.9184 ± 0.0060. Under the unseen-class protocol, RDSF consistently improved ProtoNet and RelationNet across all evaluated shot settings; the largest gain was observed for ProtoNet in the 5-shot setting, with the meta-test accuracy increasing from 0.8848 ± 0.0253 to 0.9094 ± 0.0080. In contrast, RDSF did not consistently improve MAML under this protocol, indicating that its effectiveness depended partly on the underlying meta-learning mechanism. Ablation experiments and architecture comparisons further showed the complementary contributions of the derivative branches and the advantages of bounded residual fusion over a three-channel architecture and direct feature concatenation. Overall, RDSF provides an effective spectral representation module for metric-based few-shot classification of soybean seed varieties under the evaluated known-class and unseen-class conditions. Full article

Seismic airguns generate high intensity noise that can affect marine life. Emerging alternative seismic sources offer promising opportunities to reduce acoustic impacts, yet their performance and integration into regulatory processes remain poorly quantified. We evaluated four recently developed alternative seismic sources to characterize their acoustic outputs, assess their relative impacts on marine mammals, and classify them within the tiered framework of the Operational National Mitigation Protocol for Geophysical Surveys—developed collaboratively by the National Marine Fisheries Service (NMFS), Bureau of Ocean Energy Management (BOEM), and Bureau of Safety and Environmental Enforcement (BSEE). The assessment included three ultra low-frequency (ULF) pneumatic sources (Tuned Pulse Source^TM [TPS^TM], Gemini Source^TM, and Harmony^TM) and a representative marine vibroseis (MV) system. Using NMFS auditory injury and behavioral disturbance thresholds alongside a new (60 s M-weighted sound exposure level) metric, we found that all alternatives produced lower 60 s energy and smaller auditory injury zones than conventional high impact airgun arrays. ULF pneumatic sources concentrate energy below 10 Hz, contributing to reduced exposure at ecologically relevant frequencies. TPS^TM and the Gemini Source^TM align with moderate impact tiers, while Harmony^TM is more similar to higher impact sources. For MV systems, single and dual element configurations are placed in a lower impact tier, though an 18-element array requires further characterization before classification. Remaining uncertainties, including limited behavioral data and incomplete source characterization, highlight the need for continued empirical research. By offering a science-based framework, this work contributes to regulatory modernization, advances efforts to reduce ocean noise, and enables the responsible adoption of emerging “quieter” seismic technologies within a more consistent and streamlined approach. Full article

The Global Warming Potential (GWP) is among the metrics used as midpoints in the Life Cycle Impact Assessment (LCIA) phase of the Life Cycle Assessment. However, GWP values are not always available. In recent years, predictive modelling approaches have been increasingly developed to address existing data gaps. In this study, new Quantitative Structure–Activity Relationship (QSAR) models were developed to support climate impact assessment by predicting GWP and Radiative Efficiency of chemicals from the molecular structure. These models, which are built using Multiple Linear Regression, are designed to comply with the OECD (Organization for Economic Co-operation and Development) principles for QSAR use for regulatory purposes: they are transparent, validated, characterized by a quantitative domain of applicability and based on interpretable molecular descriptors. These QSARs are characterized by lower complexity, in terms of the number and type of descriptors, and easier applicability than other models with similar performance available in the literature for the same endpoints. A dedicated case study shows how the new QSARs can be applied to screen a large dataset including more than 13,000 possible alternatives to harmful chemicals. These results show that the new models may be suggested as valuable support in LCIA procedure and for the screening of environmentally preferable and safer alternatives. Full article

Most existing event-driven image deblurring methods ignore inherent differences between the two modalities and lack explicit alignment strategies, leading to cross-modal mismatches and degraded feature reconstruction. To address this issue, a multi-stage dual encoder–decoder image deblurring method based on event image cross-modal fusion is proposed. The proposed network consists of an encoder and a decoder. The encoder employs dilated convolutional residual modules for feature extraction. It also integrates a cross-modal feature fusion module and a local scoring mechanism. These components combine event features with frame image features while suppressing noise. The decoder reconstructs image features via two directional decoding sub-networks. It also incorporates a feedback attention module. This module selects informative features along the feedback path. As a result, the image reconstruction quality is enhanced. In addition to the standard loss, mean absolute error, structural similarity, and frequency reconstruction losses are used to optimize deblurring performance. Extensive experiments are conducted on the GoPro, REBlur, and RwEvent datasets. For PSNR, our method exceeds REFID by 0.23 dB, 0.20 dB, and 0.49 dB on the three datasets. For SSIM, our model achieves gains of 0.002, 0.002, and 0.017 against REFID. In terms of computational cost and inference speed, our network adds only 3.4 M parameters and 105.2 GFLOPs, with an FPS reduction of only 2.12. This trivial efficiency loss delivers significant improvements in both pixel and structural restoration performance. Ablation experiments verify the independent positive contribution of each designed module. Both qualitative visual comparisons and quantitative metrics demonstrate that the proposed network has stronger deblurring and generalization capabilities. Full article