Search Results (5,005)

Accurate rotating sound source localization requires precise knowledge of the relative pose between the microphone array and the rotating object. In practice, six-degrees-of-freedom (6-DOF) installation uncertainties often arise, degrading beamforming performance. This paper presents a 6-DOF compensation method formulated as an optimization problem. The method treats the array as a rigid body and estimates its translational and rotational offsets by minimizing the sum of Euclidean distances between beamforming peaks and a small set of stationary single-frequency reference sources. A novel cascaded local-Bayesian optimization (CLBO) algorithm is proposed, initialized via coordinate descent local search followed by Bayesian optimization refinement. Our simulations show that CLBO yields the lowest residual error and requires the fewest evaluations, outperforming Bayesian optimization, simulated annealing, and local search. Mode composition beamforming (MCB) maps confirm that 6-DOF compensation restores spatial resolution and dynamic range to near-ideal levels, even under perturbed reference-source positions. Our experimental validation on a UAV rotor confirms practical feasibility, restoring focal peaks at blade tips. The proposed approach requires no external metrology, it is robust in practice, and it offers an efficient solution to array installation uncertainty. Full article

Spiking neural networks (SNNs) have emerged as a promising paradigm for energy-efficient neuromorphic computing, particularly when processing asynchronous event streams from dynamic vision sensors (DVSs). However, SNNs often suffer from limited representational capacity and suboptimal feature recalibration compared to their artificial counterparts. To address these challenges, we propose SE-SNN, a novel architecture that integrates Squeeze-and-Excitation (SE) blocks into deep residual SNNs, enabling channel-wise attention without spike generation. Furthermore, we introduce a Robust Parametric Leaky Integrate-and-Fire (RobustPLIF) neuron model with learnable membrane time constant ($\tau$) and firing threshold ($v_{\mathrm{th}}$), allowing adaptive temporal dynamics in each layer. Our model is trained on the CIFAR10-DVS dataset.The experimental results demonstrate that SE-SNN achieves an accuracy of $78.8$% on CIFAR10-DVS with 16 time steps, outperforming baseline SNNs while maintaining biological plausibility and hardware efficiency. Ablation studies confirm the individual contributions of the SE blocks and learnable neuron parameters to the performance gains. Full article

Sialic acids are a diverse class of widely distributed monosaccharides that are engaged in a wide range of biological processes. Sialin, a sialic acid/proton symporter, transports sialic acid across membranes between the lysosomal lumen and cytosol, playing a critical role in sialin metabolism. Taking advantage of recently published experimental structures of sialin, we report here the first computational study that probes the molecular mechanism of ligand transport through sialin, which is yet to be fully understood. In particular, we carry out steered molecular dynamics simulations of the transport of N-acetylneuraminic acid, the most widely spread natural derivative of sialic acids, through sialin with two key glutamate residues (E171 and E175) in various protonation states. The previously proposed model is refined with enriched atomistic details from this study for the cotransport of sialic acid and proton. With additional quantum calculations, our data suggest a possible explanation for why mutation R168A retains most of the transport activities, but R168K does not. Full article

Road disasters such as subsidence and bridge failures pose severe threats to traffic safety. Existing warning distance calculation methods typically assume homogeneous traffic flow and overlook the spatial heterogeneity of vehicle responses across different vehicle types, limiting their applicability for geospatial early warning systems. This paper proposes a dynamic warning distance model that integrates mixed-traffic flow composition—comprising human-driven vehicles (HDVs), Level 2 advanced driver-assistance system vehicles (ADASVs), and automated vehicles (AVs) of Level 3 and above—within a geospatial risk propagation framework. The model introduces vehicle-type weighting coefficients to quantify response differences, incorporates interaction delays calibrated through SUMO microsimulations, and accounts for cascading reaction delays caused by abrupt HDV braking. The methodology is illustrated using a counterfactual reconstruction of the 2024 Meizhou–Dapu Expressway collapse in China (52 fatalities). Based on reconstructed traffic conditions (80% HDVs, 15% ADASVs, 5% AVs; average speed 27.5 m/s; flow 1800 veh/h), the calculated dynamic warning distance is 153 m, which is 12% shorter than the speed-matched conventional stopping sight distance of 174 m (computed under consistent wet-pavement assumptions). Sensitivity analyses reveal that warning distance decreases substantially with increasing AV penetration (to 42 m in AV-dominated scenarios, a potential reduction of up to 74% compared with the HDV-dominated baseline, provided that residual HDVs are supported by V2X-based alerting) and varies monotonically with traffic flow, demonstrating the model’s adaptive capability. The proposed framework provides a theoretical foundation for adaptive geospatial disaster warning strategies and offers practical guidance for infrastructure development in the era of mixed-traffic automation. Full article

To explore the molecular basis of hypoxia tolerance variation within euteleosts, we compared the hypoxia-inducible factor (HIF) pathways of the highly tolerant Nile tilapia (Oreochromis niloticus) and the hypoxia-sensitive rainbow trout (Oncorhynchus mykiss). Evolutionary analysis revealed that Nile tilapia possesses single copies of Hif-1α and prolyl hydroxylase domain protein 2 (Phd2), whereas rainbow trout retains two and three copies, respectively. The Leu-X-X-Leu-Ala-Pro (LXXLAP) motifs in the oxygen-dependent degradation (ODD) domain of Hif-1α and the interacting loop region of Phd2 are highly conserved, indicating a conserved core mechanism for regulating Hif-1α stability. However, differences in charged residue composition flanking the Phd2 loop (e.g., fewer positively charged residues in Nile tilapia) were identified. Molecular dynamics simulations revealed that the complex formed by Nile tilapia Phd2 and the Hif-1α LXXLAP motif was unstable across physiological temperatures, suggesting potential impairment of the catalytic geometry compatible with hydroxylation and elevated normoxic Hif-1α stability. In contrast, the corresponding complexes in rainbow trout were more stable, particularly at low temperatures. Expression profiling revealed that Nile tilapia tissues, including the heart, maintain higher basal expression of glycolytic genes, may help support energy production during hypoxia. Our findings indicate that a weakened protein interaction and high constitutive expression is predicted to enhance HIF pathway responsiveness, potentially priming vital tissues for glycolytic energy production and may contribute to this species’ hypoxia tolerance. Full article

Soil moisture content (SMC) is a key variable in hydrology, irrigation, and land-atmosphere interactions, yet continuous monitoring remains constrained by sensor limitations and site heterogeneity. This study evaluated Bayesian Additive Regression Trees (BART) for estimating daily SMC at 10, 30, and 50 cm depths in the Arta plain, northwestern Greece, using combinations of in situ soil moisture observations from other depths together with Sentinel-2-derived NDVI and NDMI. BART was trained with 2020–2021 data and evaluated using 2022 observations. Model performance was generally high, with Nash–Sutcliffe efficiency often exceeding 0.90 and RMSE remaining below nominal sensor uncertainty. The best results were obtained when soil moisture from two additional depths was used as predictor information, confirming the strong vertical dependence of profile moisture dynamics. NDVI and NDMI did not systematically improve point prediction accuracy but provided complementary information by improving the estimation of predictive uncertainty and generating more reliable credible intervals within the probabilistic formulation. Residuals were normally distributed and showed no evident systematic bias. Preliminary external validation at an independent site showed moderate skill, with most cases still producing errors below nominal sensor accuracy. Finally, a comparison between BART and Multiple Linear Regression (MLR) showed that BART outperformed MLR, particularly in cases where both machine learning models performed weakly. Overall, BART proved to be a robust framework for multi-depth soil moisture estimation. Full article

For nonlinear problems in robotic systems, such as parametric uncertainties and external disturbances, this paper proposes a control method based on bilateral dual-kernel fuzzy control. To address the issue that joint angular velocities cannot be directly measured, an extended state observer (ESO) is introduced to simultaneously estimate the joint positions, velocities, and system nonlinearities, thereby achieving effective reconstruction of the system states. In terms of controller design, a dual-kernel function is adopted instead of the conventional single-kernel function. By exploiting its enhanced feature representation capability and fast response characteristics, the proposed approach improves the system dynamic response speed and reduces the settling time. For nonlinear residuals, the bilateral parallel control strategy further improves the approximation accuracy of the control system. Multiple dual-kernel fuzzy sub-controllers are integrated in a bilateral parallel manner, and the weighting parameters of both the fuzzy system and the bilateral structure are updated in real time based on the approximation error. This enables accurate approximation and compensation of the residuals estimated by the extended state observer. The stability of the closed-loop system is rigorously proved based on Lyapunov theory. Finally, simulations on the MATLAB R2022b platform and experiments on a robotic experimental platform are conducted to verify that the proposed bilateral dual-kernel fuzzy controller achieves significantly improved control accuracy for a two-degree-of-freedom robotic manipulator system compared with conventional controllers, thereby demonstrating the effectiveness and superiority of the proposed algorithm. Full article

Grassland fire is one of the major disasters threatening regional ecological security. Its occurrence, development, and spread are closely related to the spatial distribution and coverage of surface vegetation and snow cover across grassland areas. As the primary combustible fuel source, higher vegetation coverage increases fuel load and continuity, thereby directly determining grassland fire danger levels and accelerating fire spread velocity. In contrast, snow cover imposes an indirect regulatory effect on the spatiotemporal pattern of fire danger factors: it lowers surface temperature, raises near-surface humidity, and restricts the germination and growth of herbaceous vegetation in cold seasons, which effectively reduces available combustible materials and weakens regional fire hazard conditions. Therefore, accurately obtaining the coverage status of vegetation (direct combustible fuel factor) and snow cover (indirect fire-regulating factor) in complex grassland scenarios is the essential premise for reliable grassland fire danger monitoring, early warning, disaster prevention and control, and regional ecological management. Aiming at the practical problems in complex grassland scenarios (such as undulating terrain, uneven vegetation growth, large differences in snow depth, and complex lighting conditions), including difficulty in extracting vegetation and snow-covered areas, blurred and confusing boundaries, and low accuracy in coverage calculation, which seriously restrict the technical bottleneck of precise monitoring of grassland fire danger factors, this study takes near-ground images collected by grassland fire danger factor monitoring stations as the core data source, and proposes an improved UNet image segmentation model combined with image segmentation technology and deep learning methods to realize precise extraction of vegetation and snow-covered areas and efficient calculation of coverage in complex scenarios. To improve the model’s feature extraction ability, boundary localization accuracy, and reduce model parameters and computational overhead, the CBAM-ASPP (Convolutional Block Attention Module—Atrous Spatial Pyramid Pooling) module is integrated at the end of the encoding path. The attention mechanism is used to enhance the weight of key features, and the multi-scale receptive field of atrous spatial pyramid pooling is utilized to strengthen the model’s ability to fuse features of vegetation and snow areas of different scales. The residual attention mechanism is introduced in the upsampling stage to effectively alleviate the gradient disappearance problem, improve the model’s ability to accurately locate the boundaries of vegetation and snow areas, and reduce segmentation errors. In the training process, a dynamically weighted hybrid loss function is adopted to dynamically adjust the weights according to the segmentation difficulty of different types of samples during training, optimize the model training effect, and improve the segmentation accuracy and generalization ability. Experiments were conducted using near-ground images of typical complex grassland scenarios as the dataset, and the performance of the proposed model was verified through comparative experiments. The results show that in the vegetation segmentation task, the mean Intersection over Union (mIoU) of the model reaches 84.70%, and the accuracy rate is 91.28%, which are 1.48 and 1.58 percentage points higher than those of the standard UNet model, respectively. In the snow segmentation task, the mIoU of the model reaches 92.74%, and the accuracy rate is 94.19%, which are 2.39 and 2.36 percentage points higher than those of the standard UNet model, respectively. At the same time, the number of parameters of the model is reduced by 12.85% compared with the standard UNet. Also, its comprehensive performance is significantly better than that of mainstream image segmentation models such as FCN, SegNet, and DeepLabv3+. Based on the standardized time-series data retrieved by the optimized segmentation model, this study further constructs a Grassland Fire Risk Index (GFRI) using the Analytic Hierarchy Process (AHP). Pearson correlation verification confirms that the GFRI has an extremely significant positive correlation with historical fire frequency, accurately capturing the seasonal dynamic rhythm of regional grassland fire occurrence. This integrated framework of intelligent segmentation and fire risk quantification provides a reliable technical solution for grassland fire factor monitoring, dynamic fire risk assessment, early warning systems, and refined regional ecological management. Full article

Control-barrier-function-based safety filters are promising for autonomous driving, but most existing formulations treat obstacle perception as deterministic or account only for bounded ego state-estimation errors. This becomes limiting when obstacle existence, position, motion, and sensing quality vary online. We present a sensor-health- and belief-aware risk-adaptive high-order control barrier function (HOCBF) safety filter for dynamic obstacle avoidance. The method uses obstacle belief from a perception/tracking module, inflates residual obstacle uncertainty according to an object-wise sensor-health score, and converts upper-tail risk into adaptive HOCBF tightening through conditional value-at-risk (CVaR). Sensor health enters the controller through both covariance inflation and online CVaR confidence scheduling. The resulting quadratic program combines deterministic ego-error robustness with probabilistic perception uncertainty while minimally modifying the nominal control input. The zero-slack solution guarantees forward invariance of the risk-tightened safe set under the stated assumptions, whereas the slack-activated mode provides a quantified least-violation fallback rather than a strict safety guarantee. Simulations on a nonlinear 3-DOF bicycle model evaluate critical cut-in, sudden perception degradation, merge-bottleneck, fixed-CVaR, sensitivity, runtime-scaling, heterogeneous multi-obstacle, and heavy-tailed uncertainty cases. Full article

Hyperoxaluria Type 1 (PH1) is a rare autosomal recessive metabolic disorder caused by mutations in the AGXT gene, leading to impaired glyoxylate metabolism and excessive oxalate accumulation, resulting in nephrolithiasis, nephrocalcinosis, and end-stage renal disease. As a rare and often neglected disease, PH1 poses a significant challenge to modern healthcare systems due to its progressive nature and limited therapeutic options. In this study, an integrated in silico approach was employed to identify pathogenic single-nucleotide polymorphisms (SNPs) and evaluate potential therapeutic candidates. Computational analyses using ConSurf, Align-GVGD, INPS-MD, CUPSAT, and iStable identified G161C and Y260C as highly deleterious variants affecting protein stability. Virtual screening, followed by ADME and toxicity assessments, identified Tauroursodeoxycholic acid (TUDCA) as a promising candidate with favorable pharmacokinetic and safety profiles. Molecular docking revealed that TUDCA exhibited higher binding affinity than the reference drug pyridoxine across native and SNP variants of AGXT proteins. Molecular dynamics simulations (300 ns) demonstrated enhanced structural stability of TUDCA-bound complexes, indicated by reduced RMSD and RMSF, improved compactness, and sustained hydrogen bonding. Furthermore, free energy landscape (FEL) and dynamic cross-correlation matrix (DCCM) analyses confirmed improved conformational stability and coordinated residue motions in SNP variant structures. Overall, these findings suggest that TUDCA may effectively stabilize structural alterations induced by pathogenic AGXT variants, highlighting its potential as a precision medicine-based therapeutic strategy for PH1. Full article

Human pose estimation requires accurate localization of body keypoints under complex backgrounds, occlusion, and diverse human postures. Existing high-resolution pose-estimation networks preserve spatial details effectively, but their static information flow limits their adaptability to different image contexts. To address this limitation, this paper proposes a context-aware hierarchical information arbitration method that dynamically regulates feature interaction at both multi-resolution fusion and residual feature refinement levels. The proposed method achieves superior performance on COCO, reaching 77.0 average precision and improving the High-Resolution Network baseline by 3.6 percentage points, with only a minor increase in model parameters. These results demonstrate that adaptive information arbitration improves pose-estimation accuracy and robustness while maintaining computational efficiency. Full article

This article presents an analysis of the consequences of a road accident caused by a failure to notice an oncoming vehicle, caused by the left headlamp malfunction. Research was conducted using computer simulation enabling the reproduction of vehicle dynamics under night-time conditions, heavy snowfall and reduced pavement adhesion (α_p = 0.20; α_s = 0.15). An overtaking manoeuvre was performed in three speed scenarios of the overtaking vehicle: 60, 70 and 80 km/h. In the first phase of the collision (the overtaking vehicle–the oncoming vehicle), a consistent increase in deformation depth was observed with increasing speed, from 342 mm and 469 mm (60 km/h) to 400 mm and 518 mm (80 km/h). The corresponding equivalent energy speed (EES) reached maximum values of 57.4 km/h and 70.5 km/h, respectively. Contact was strongly inelastic in nature (the coefficient of restitution 0.06–0.07), and the transferred impulse initiated intensive rotational motion. The second phase of the collision involved secondary contact between the overtaken vehicle and the overtaking vehicle. Collision severity was directly dependent on residual energy after the first impact. In the 80 km/h scenario, the deformation depth in this phase reached 146 mm, with EES of approximately 10–11 km/h. The analysis demonstrated that the energy not dissipated during the first stage determined the course of the subsequent contact and resulted in a complete loss of directional stability of all vehicles, ultimately leading to a departure from the roadway. Full article

The interaction between airborne allergens and environmental microplastics is an emerging concern in the context of increasing plastic pollution and allergic disease prevalence. In this study, we investigated the molecular interaction between Cry j 1, the major allergen of Japanese cedar (Cryptomeria japonica) pollen, and polyethylene terephthalate (PET) microplastic surfaces using all-atom molecular dynamics simulations integrated with computational epitope selection analyses. The simulations showed that Cry j 1 adsorbs onto PET primarily through hydrophobic and van der Waals interactions, with residues Pro165, Ala227, Tyr228, and Val163 contributing prominently to surface association. Mapping of selected epitope regions indicated that several linear B-cell epitopes remained solvent exposed following adsorption, whereas two CD4⁺ T-cell epitope regions (T5 and T6) contributed more directly to PET interaction. PET adsorption was accompanied by moderate changes in conformational dynamics, including reduced residue-level flexibility and localized secondary-structure adjustments, while the overall protein fold remained structurally stable throughout the simulation. Small decreases in radius of gyration and solvent-accessible surface area suggested mild adsorption-associated compaction rather than major unfolding. These findings indicate that PET association can influence the structural dynamics and interfacial behavior of Cry j 1 without extensive disruption of its global architecture. Because the study is entirely computational, the immunological implications remain hypothetical and require experimental validation. Nevertheless, this work provides a molecular-level framework for understanding how airborne microplastics may influence allergen behavior and protein-surface interactions in polluted atmospheric environments. Full article

Gearbox fault diagnosis under controlled bench-test conditions with known speed variations and noise interference remains challenging because nonstationarity, background noise, and operating-condition fluctuations can easily submerge weak localized fault features. To address this issue, this study proposes a fault diagnosis method based on a fractional-order-enhanced dual-view representation and VibrMamba–VMamba collaborative modeling. First, this study introduces a Grünwald–Letnikov fractional-order differential enhancement module with a fractional order of α = 0.6 to strengthen fault-sensitive impulsive components and improve the representation of nonstationary vibration signals. The framework then uses the enhanced signal to construct dual-view inputs: a fractional-order-enhanced one-dimensional vibration sequence and a fractional-order-enhanced synchrosqueezing transform (SST) time–frequency image. Subsequently, the framework constructs a VibrMamba temporal branch and a VMamba visual branch to extract dynamic temporal features and global structural features, respectively. Instead of using simple feature concatenation, this study designs a sample-adaptive collaborative fusion mechanism with gated weighting and cross-branch residual enhancement to integrate complementary temporal–visual representations. Bench-level experiments show that the proposed method achieves 98.90% diagnostic accuracy under clean test conditions and maintains 91.52% accuracy at −5 dB signal-to-noise ratio (SNR). These results should be interpreted as bench-level validation under controlled laboratory conditions rather than as direct evidence of field-level generalization. This framework provides a methodological solution that integrates fractional-order signal enhancement, dual-view representation, and Mamba-style collaborative state-space modeling for gearbox fault classification under controlled laboratory conditions with known speed variations and noise disturbances. Full article

Phosphodiesterase-1 (PDE1) represents an attractive target for the treatment of idiopathic pulmonary fibrosis (IPF). However, the limited chemical diversity of current PDE1 inhibitors has hindered the development of potential anti-IPF drugs, primarily due to an ambiguous understanding of interactions between inhibitors and PDE1. Herein, we report an integrated virtual screening strategy containing pharmacophore modeling, molecular docking, and molecular dynamics simulations, which markedly accelerated the discovery of novel PDE1 inhibitors. Enzymatic assays identified eleven active compounds with moderate inhibition from twenty-six purchased candidates, encompassing nine distinct scaffold types. Notably, 6484-0008 and 6484-0032 exhibited more than 50% inhibition at a concentration of 1 μM. Hydrogen bond analysis and residue-based energy decompositions revealed key recognition mechanisms involving crucial residues Gln421, His373, and Phe424, as well as the unique Thr271 in the flexible H-loop region, providing insights for the rational design of inhibitors with enhanced potency. Full article