Search Results (2,355)

Autonomous navigation in dynamic, multi-vessel maritime environments presents a formidable challenge, demanding strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). Conventional approaches often struggle with the dual imperatives of global path optimality and local reactive safety, and they frequently rely on simplistic state representations that fail to capture complex spatio-temporal interactions among vessels. We introduce a novel hybrid reinforcement learning framework, D* Lite + Transformer-Enhanced Soft Actor-Critic (TE-SAC), to overcome these limitations. This hierarchical framework synergizes the strengths of global and local planning. An enhanced D* Lite algorithm generates efficient, long-horizon reference paths at the global level. At the local level, the TE-SAC agent performs COLREGs-compliant tactical maneuvering. The core innovation resides in TE-SAC’s synergistic state encoder, which uniquely combines a Graph Neural Network (GNN) to model the instantaneous spatial topology of vessel encounters with a Transformer encoder to capture long-range temporal dependencies and infer vessel intent. Comprehensive simulations demonstrate the framework’s superior performance, validating the strengths of both planning layers. At the local level, our TE-SAC agent exhibits remarkable tactical intelligence, achieving an exceptional 98.7% COLREGs compliance rate and reducing energy consumption by 15–20% through smoother, more decisive maneuvers. This high-quality local control, guided by the efficient global paths from the enhanced D* Lite algorithm, culminates in a 10–32 percentage point improvement in overall task success rates compared to state-of-the-art baselines. This work presents a robust, verifiable, and efficient framework. By demonstrating superior performance and compliance with rules in high-fidelity simulations, it lays a crucial foundation for advancing the practical application of intelligent autonomous navigation systems. Full article

Development of forming methods for surface-coated metals is a current concern due to their economic and environmental advantages. For a successful forming operation, it is necessary that both components, the substrate and the coating, are able to withstand stress without damage until the final shape and dimensions are reached. This goal can be achieved through good knowledge of the elastic and plastic properties of the substrate and the coating, the compatibility between them, the appropriate surface treatment, and the rigorous control of technological forming parameters. Our study was carried out with flat specimens of pre-painted Al 99.0 sheet that were electromagnetically formed by bulging. Forming behavior was investigated as depending on the initial thickness of the substrate, on the aluminum sheet pretreatment, as well as on the plastic deformation path of the metal–paint structure. To verify the damage to the paint layer, tests with increasing strains were performed, and the interface between the metal and the coating layer was investigated by scanning electron microscopy. The obtained results indicate that electromagnetic forming of pre-painted sheets can be a feasible method for specific applications if the forming degree of the substrate is tightly correlated with the type of desired coating and with the pretreatment method used for the metal surface. Full article

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, enabling simultaneous downconversion without modification of the passive mixer topology. The receiver employs a 4-path passive mixer configuration to enhance harmonic selectivity and provide flexible frequency planning.The architecture is implemented on a printed circuit board (PCB) and validated through comprehensive simulation and experimental measurements under continuous wave and modulated signal conditions. Measured results demonstrate a sensitivity of $-55\mathrm{dBm}$ and a conversion gain varying from $-2.5\mathrm{dB}$ to $-9\mathrm{dB}$ depending on the selected harmonic pair. The receiver’s performance is further corroborated by concurrent (dual band) reception of real-world signals, including a GPS signal centered at 1575 MHz and an LTE signal at 1179 MHz, both downconverted using a single 393 MHz LO. Signal fidelity is assessed via Normalized Mean Square Error (NMSE) and Error Vector Magnitude (EVM), confirming the proposed architecture’s effectiveness in maintaining high-quality signal reception under concurrent multiband operation. The results highlight the potential of harmonic-selective clocking to simplify multiband receiver design for wireless communication and global navigation satellite system (GNSS) applications. Full article

Accurate and efficient cloud detection is critical for maintaining the usability of optical remote sensing imagery, particularly in large-scale Earth observation systems. In this study, we propose FEMNet, a lightweight dual-branch network that combines state space modeling with convolutional encoding for multi-class cloud segmentation. The Mamba-based encoder captures long-range semantic dependencies with linear complexity, while a parallel CNN path preserves spatial detail. To address the semantic inconsistency across feature hierarchies and limited context perception in decoding, we introduce the following two targeted modules: a cross-stage semantic enhancement (CSSE) block that adaptively aligns low- and high-level features, and a multi-scale context aggregation (MSCA) block that integrates contextual cues at multiple resolutions. Extensive experiments on five benchmark datasets demonstrate that FEMNet achieves state-of-the-art performance across both binary and multi-class settings, while requiring only 4.4M parameters and 1.3G multiply–accumulate operations. These results highlight FEMNet’s suitability for resource-efficient deployment in real-world remote sensing applications. Full article

Rye regeneration in anther cultures is problematic and affected by albino plants. DNA methylation changes linked to Cu²⁺ ions in the induction medium affect reprogramming microspores from gametophytic to sporophytic path. Alternations in S-adenosyl-L-methionine (SAM), glutathione (GSH), or β-glucans and changes in DNA methylation in regenerants obtained under different in vitro culture conditions suggest a crucial role of biochemical pathways. Thus, understanding epigenetic and biochemical changes arising from the action of Cu²⁺ and Zn²⁺ that participate in enzymatic complexes may stimulate progress in rye doubled haploid plant regeneration. The Methylation-Sensitive Amplified Fragment Length Polymorphism approach was implemented to identify markers related to DNA methylation and sequence changes following the quantification of variation types, including symmetric and asymmetric sequence contexts. Reverse-Phase High-Pressure Liquid Chromatography (RP-HPLC) connected with mass spectrometry was utilized to determine SAM, GSH, and glutathione disulfide, as well as phytohormones, and RP-HPLC with a fluorescence detector to study polyamines changes originating in rye regenerants due to Cu²⁺ or Zn²⁺ presence in the induction medium. Multivariate and regression analysis revealed that regenerants derived from two lines treated with Cu²⁺ and those treated with Zn²⁺ formed distinct groups based on DNA sequence and methylation markers. Zn²⁺ treated and control samples formed separate groups. Also, Cu²⁺ discriminated between controls and treated samples, but the separation was less apparent. Principal coordinate analysis explained 85% of the total variance based on sequence variation and 69% of the variance based on DNA methylation changes. Significant differences in DNA methylation characteristics were confirmed, with demethylation in the CG context explaining up to 89% of the variance across genotypes. Biochemical profiles also demonstrated differences between controls and treated samples. The changes had different effects on green and albino plant regeneration efficiency, with cadaverine (Cad) and SAM affecting regeneration parameters the most. Analyses of the enzymes depend on the Cu²⁺ or Zn²⁺ ions and are implemented in the synthesis of Cad, or SAM, which showed that some of them could be candidates for genome editing. Alternatively, manipulating SAM, GSH, and Cad may improve green plant regeneration efficiency in rye. Full article

This research presents DuSAFNet, a lightweight deep neural network for fine-grained bird audio classification. DuSAFNet combines dual-path feature fusion, spectral–temporal attention, and a multi-band ArcMarginProduct classifier to enhance inter-class separability and capture both local and global spectro–temporal cues. Unlike single-feature approaches, DuSAFNet captures both local spectral textures and long-range temporal dependencies in Mel-spectrogram inputs and explicitly enhances inter-class separability across low, mid, and high frequency bands. On a curated dataset of 17,653 three-second recordings spanning 18 species, DuSAFNet achieves 96.88% accuracy and a 96.83% F1 score using only 6.77 M parameters and 2.275 GFLOPs. Cross-dataset evaluation on Birdsdata yields 93.74% accuracy, demonstrating robust generalization to new recording conditions. Its lightweight design and high performance make DuSAFNet well-suited for edge-device deployment and real-time alerts for rare or threatened species. This work lays the foundation for scalable, automated acoustic monitoring to inform biodiversity assessments and conservation planning. Full article

The development of deep coalbed methane (buried depth > 2000 m) in the Yan’an block of Ordos Basin is limited by low permeability, the pore structure of the coal reservoir, and the gas–water occurrence relationship. It is urgent to clarify the key control mechanism of pore structure on gas migration. In this study, based on high-pressure mercury intrusion (pore size > 50 nm), low-temperature N₂/CO₂ adsorption (0.38–50 nm), low-field nuclear magnetic resonance technology, fractal theory and Pearson correlation coefficient analysis, quantitative characterization of multi-scale pore–fluid system was carried out. The results show that the multi-scale pore network in the study area jointly regulates the occurrence and migration process of deep coalbed methane in Yan’an through the ternary hierarchical gas control mechanism of ‘micropore adsorption dominant, mesopore diffusion connection and macroporous seepage bottleneck’. The fractal dimensions of micropores and seepage are between 2.17–2.29 and 2.46–2.58, respectively. The shape of micropores is relatively regular, the complexity of micropore structure is low, and the confined space is mainly slit-like or ink bottle-like. The pore-throat network structure is relatively homogeneous, the difference in pore throat size is reduced, and the seepage pore shape is simple. The bimodal structure of low-field nuclear magnetic resonance shows that the bound fluid is related to the development of micropores, and the fluid mobility mainly depends on the seepage pores. Pearson’s correlation coefficient showed that the specific surface area of micropores was strongly positively correlated with methane adsorption capacity, and the nanoscale pore-size dominated gas occurrence through van der Waals force physical adsorption. The specific surface area of mesopores is significantly positively correlated with the tortuosity. The roughness and branch structure of the inner surface of the channel lead to the extension of the migration path and the inhibition of methane diffusion efficiency. Seepage porosity is linearly correlated with gas permeability, and the scale of connected seepage pores dominates the seepage capacity of reservoirs. This study reveals the pore structure and ternary grading synergistic gas control mechanism of deep coal reservoirs in the Yan’an Block, which provides a theoretical basis for the development of deep coalbed methane. Full article

Localized ionization enhancements (LIEs) in altitude range corresponding to the D-region ionosphere, disrupting Very-Low-Frequency (VLF) signal propagation. This case study focuses on Lightning-induced Electron Precipitation (LEP), analyzing amplitude and phase variations in VLF signals recorded in Belgrade, Serbia, from worldwide transmitters. Due to the localized, transient nature of Energetic Electron Precipitation (EEP) events and the path-dependence of VLF responses, research relies on event-specific case studies to model reflection height and sharpness via numerical simulations. Findings show LIEs are typically under 1000 × 500 km, with varying internal structure. Accumulated case studies and corresponding data across diverse conditions contribute to a broader understanding of ionospheric dynamics and space weather effects. These findings enhance regional modeling, support aerosol–electricity climate research, and underscore the value of VLF-based ionospheric monitoring and collaboration in Europe. Full article

In this note, we discuss interesting aspects of the interaction of electromagnetic waves (EMW) with gravitational waves (GWs) and how we can use them for GW detection. We show that there is (i) a deviation from the original path of the EMW, as measured by an angle of the scattered EMW, as well as (ii) a change in frequency. We show that the angular deviation is dependent on the frequency of the initial EMW and GW and suggest the use of MASERS/RASERS instead of LASERS for GW detection. We also briefly examine the influence of the Earth’s rotation and revolution, which can be sources of noise in the measurement of the angular deviation of EMW. Full article

This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach to route analysis, issues related to insufficient street view images, and the lack of proper image mapping for desired roads remain unaddressed by current applications, which are predominantly client-based. In response, we propose an innovative automatic system designed to generate videos depicting road routes between two geographic locations. The system calculates and presents the route conventionally, emphasizing the path on a two-dimensional representation, and in a multimedia format. A prototype is developed based on a cloud-based client–server architecture, featuring three core modules: frames acquisition, frames analysis and elaboration, and the persistence of metadata information and computed videos. The tests, encompassing both real-world and synthetic scenarios, have produced promising results, showcasing the efficiency of our system. By providing users with a real and immersive understanding of requested routes, our approach fills a crucial gap in existing navigation solutions. This research contributes to the advancement of route planning technologies, offering a comprehensive and user-friendly system that leverages cloud computing and multimedia visualization for an enhanced navigation experience. Full article

This paper develops a novel modeling framework that integrates time-varying quantile-based spillover effects into a regime-switching realized volatility model. A dynamic spillover factor is constructed by identifying the most influential contributors to Bitcoin’s realized volatility across different quantile levels. This quantile-layered structure enables the model to capture heterogeneous spillover paths under varying market conditions at a macro level while also enhancing the sensitivity of volatility regime identification via its incorporation into a time-varying transition probability (TVTP) Markov-switching mechanism at a micro level. Empirical results based on the cryptocurrency market demonstrate the superior forecasting performance of the proposed TVTP-MS-HAR model relative to standard benchmark models. The model exhibits strong capability in identifying state-dependent spillovers and capturing nonlinear market dynamics. The findings further reveal an asymmetric dual-tail amplification and time-varying interconnectedness in the spillover effects, along with a pronounced asymmetry between market capitalization and systemic importance. Compared to decomposition-based approaches, the X-RV type of models—especially when combined with the proposed quantile-driven factor—offers improved robustness and predictive accuracy in the presence of extreme market behavior. This paper offers a coherent approach that bridges phenomenon identification, source localization, and predictive mechanism construction, contributing to both the academic understanding and practical risk assessment of cryptocurrency markets. Full article

Ensuring the structural integrity of reinforced concrete (RC) components in nuclear facilities exposed to extreme conditions is essential for safe decommissioning. This study investigates the impact of high-temperature exposure on RC pedestal structures supported by steel ring-shaped shear connectors—critical elements for maintaining vertical and lateral load paths in containment systems. Scaled-down cyclic loading tests were performed on pedestal specimens with and without prior thermal exposure, simulating post-accident conditions observed at a damaged nuclear power plant. Experimental results show that thermal degradation significantly reduces lateral stiffness, with failure mechanisms concentrating at the interface between the concrete and the embedded steel skirt. Complementary finite element analyses, incorporating temperature-dependent material degradation, highlight the crucial role of load redistribution to steel components when concrete strength is compromised. Parametric studies reveal that while geometric variations in the inner skirt have limited influence, thermal history is the dominant factor affecting vertical capacity. Notably, even with substantial section loss in the concrete, the steel inner skirt maintained considerable load-bearing capacity. This study establishes a validated analytical framework for assessing structural performance under extreme conditions, offering critical insights for risk evaluation and retrofit strategies in the context of nuclear facility decommissioning. Full article

The performance of the UAVs while executing various mission profiles greatly depends on the selection of planning algorithms. Reinforcement learning (RL) algorithms can effectively be utilized for robot path planning. Due to random action selection in case of action ties, the traditional Q-learning algorithm and its other variants face the issues of slow convergence and suboptimal path planning in high-dimensional navigational environments. To solve these problems, we propose an improved deep Q-network (DQN), incorporating an efficient tie-breaking mechanism, prioritized experience replay (PER), and L2-regularization. The adopted tie-breaking mechanism improves the action selection and ultimately helps in generating an optimal trajectory for the UAV in a 3D cluttered environment. To improve the convergence speed of the traditional Q-algorithm, prioritized experience replay is used, which learns from experiences with high temporal difference (TD) error and avoids uniform sampling of stored transitions during training. This also allows the prioritization of high-reward experiences (e.g., reaching a goal), which helps the agent to rediscover these valuable states and improve learning. Moreover, L2-regularization is adopted that encourages smaller weights for more stable and smoother Q-values to reduce the erratic action selections and promote smoother UAV flight paths. Finally, the performance of the proposed method is presented and thoroughly compared against the traditional DQN, demonstrating its superior effectiveness. Full article

Background/Objectives: Truncating mutations in PALB2, a critical component of the BRCA1-PALB2-BRCA2 homologous recombination repair complex, are associated with increased risk and aggressiveness of breast cancer. The consequences of PALB2 truncation on the expression, localization, and functional dynamics of the scaffold protein IQGAP1 were investigated in this study based on two cases of truncated PALB2 human breast invasive ductal carcinoma (IDC), specifically, c.1240C>T (p.Arg414*) and c.2257C>T (p.Arg753*). Methods: Using confocal microscopy, we examined co-expression patterns of IQGAP1 with PALB2, PCNA, CK7, and β-tubulin in tumor tissues from both control cancer and PALB2-mutated cases. Results: In PALB2-truncated tumors, IQGAP1 exhibited enhanced peripheral and plasma membrane localization with elevated co-localization levels compared to controls, suggesting altered cytoskeletal organization. PALB2 truncation increased nuclear and cytoplasmic N-terminal PALB2 immunoreactivity, indicating the presence of truncated isoforms disrupting the homologous recombination repair system. Co-expression analyses with PCNA revealed an inverse expression pattern between IQGAP1 and proliferation markers, suggesting S-phase cell cycle-dependent heterogeneity. Furthermore, the loss of IQGAP1 dominance over CK7 and β-tubulin in mutant tumors, along with persistent intercellular spacing, implied a loss of cell–cell cohesion and the acquisition of invasive traits. Conclusions: These data support a model where PALB2 truncation triggers a reorganization of IQGAP1 that disrupts its canonical structural functions and facilitates tumor progression via enhanced motility and impaired cell–cell interaction. IQGAP1 thus serves as both a functional effector and potential biomarker in PALB2-mutated IDC, opening novel paths for diagnosis and targeted therapeutic intervention. Full article