Search Results (612)

To address unstable localization and sparse mapping for autonomous vehicles operating in GPS-denied and low-visibility environments, this paper proposes GTS-SLAM, a tightly coupled dense visual SLAM framework integrating Generalized Iterative Closest Point (GICP) and 3D Gaussian Splatting (3DGS). The system is designed for intelligent driving platforms such as underground mining vehicles, inspection robots, and tunnel autonomous navigation systems. The front-end performs covariance-aware point-cloud registration using GICP to achieve robust pose estimation under low texture, dust interference, and dynamic disturbances. The back-end employs probabilistic dense mapping based on 3DGS, combined with scale regularization, scale alignment, and keyframe factor-graph optimization, enabling synchronized optimization of localization and mapping. A Compact-3DGS compression strategy further reduces memory usage while maintaining real-time performance. Experiments on public datasets and real underground-like scenarios demonstrate centimeter-level trajectory accuracy, high-quality dense reconstruction, and real-time rendering. The system provides reliable perception capability for vehicle autonomous navigation, obstacle avoidance, and path planning in confined and weak-light environments. Overall, the proposed framework offers a deployable solution for autonomous driving and mobile robots requiring accurate localization and dense environmental understanding in challenging conditions. Full article

In real-world machine learning scenarios, training data are frequently weakly annotated and distributionally misaligned with deployment environments. Specifically, label ambiguity may arise when each instance is associated with a set of candidate labels, and distribution shifts between training and testing are common in practice. Although Partial Label Learning (PLL) and Unsupervised Domain Adaptation (UDA) have been extensively studied individually, they frequently co-occur in practice. For instance, in cross-hospital medical image analysis, datasets may exhibit both inconsistent diagnostic labels due to variations in expert interpretation (label ambiguity) and significant differences in imaging equipment or patient demographics (distribution shift). However, Partial-Label Unsupervised Domain Adaptation (PLUDA) has received limited attention as a unified problem. In this paper, a unified generalization bound is established for Partial-Label Unsupervised Domain Adaptation (PLUDA) and three critical limitations causing existing approaches to fail: ambiguity degree, ideal joint error, and model complexity remain uncontrolled. Motivated by these theoretical insights, we propose Dual-Smoothing over Manifold and Parameter (DSMP) to control all three factors. DSMP employs manifold-based representation smoothing via Laplacian smoothing based on adaptive multi-kernel RKHS similarity and candidate set refinement to address the three critical limitations. Moreover, DSMP leverages sharpness-aware parameter smoothing to ensure stable optimization under weak supervision through loss landscape flattening. Extensive experiments demonstrate that DSMP outperforms existing baselines, achieving superior cross-domain generalization from weakly labeled sources. This work provides theoretical insights and a principled solution to the previously underexplored yet practically important PLUDA problem. Full article

Managing excessive water production in oil fields during primary, secondary, or enhanced recovery remains challenging. It increases costs and reduces hydrocarbon recovery, particularly in reservoirs with high-conductivity pathways such as high-permeability zones and fractures. Hydrogels are commonly used for water blocking and retention; however, their effectiveness diminishes at higher flow rates due to mechanical weaknesses and structural limitations. These problems are intensified under harsh environmental conditions, including high temperatures, salinity, and hardness. In this study, we investigate how altering the molecular suprastructure of preformed particle gel (PPG) can improve its effectiveness in shear-responsive water-blockage treatments, particularly when traditional PPGs cannot control rising flow rates. We enhance the shear-responsive mechanical properties of a composite PPG by increasing the density and diversity of intermolecular interactions. We use two different strategies: first, incorporating cationic groups into the polymer backbone to form a polyampholyte network with stronger electrostatic interactions; second, adding a linear anionic polymer to generate a secondary interpenetrating network that can undergo a coil–stretch transition under thermal and shear stimuli, thereby enhancing its own solvation and whole-network expansion. Molecular simulations provide an interpretation of the experimentally observed shear-thickening response and enhanced disproportionate permeability reduction at high flow rates. The water residual resistance factor of the improved PPGs deviates from the typical shear-thinning power-law behavior (n < 1) observed in conventional PPG, showing shear-thickening (n > 1). Tests reveal a strong ability to preferentially reduce water flow over oil, with Disproportionate Permeability Reduction increasing from 8 to 117 in the high-flow-rate zone. The enhanced strength and thermal stability also improve resistance to washout under high-pressure gradients. This research provides a novel approach to tailoring the microscopic architecture of PPGs to achieve selective, robust water blockage, offering a high-efficiency solution for complex reservoir environments. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

In this paper, we study anomalous scattering and weak interactions of lumps for the $(2+1)$-dimensional generalized Kadomtsev-Petviashvili equation. Together with the long-wave-limit method, the normal scattering lump solutions are obtained. By adding perturbation terms to the normal scattering lump solutions, the anomalous scattering of two-lump and three-lump solutions is derived. The weak interactions among one soliton with two lumps, two solitons and two lumps as well as the interactions between one normal lump and two anomalous lumps are constructed. The dynamic properties of these anomalous scatterings and weak interactions are analyzed in detail. In these hybrid anomalous scattering solutions, from the long-time asymptotic behavior of the solutions, we find that the peak trajectories separate as $\sqrt{t}$ when $\left|t\right|\to \infty$. In particular, a head-on collision of two lumps leads to 135° scattering. Moreover, the anomalous lumps exhibit the same dynamical properties when they collide with solitons. The results described in this paper could also be generalized to the other (2 + 1)-dimensional integrable systems. Full article

Aiming at the problems of difficult hard-constraint enforcement and weak environmental generalization ability in the safe trajectory planning of manipulators in complex environments, a Policy-Guided Model Predictive Path Integral (PG-MPPI) planning framework is proposed. This framework integrates the advantages of reinforcement learning and model predictive control to construct a global prior guidance, local real-time optimization and hard-constraint safety assurance: a Constraint-Discounted Soft Actor–Critic (CD-SAC) offline learning policy is designed, which incorporates the configuration-space distance field as a safety guidance term to realize the learning of obstacle avoidance behavior; the offline policy is used to guide the online sampling and optimization of MPPI, improving sampling efficiency and planning quality; and a Control Barrier Function (CBF) safety filter is introduced to revise control commands in real time, ensuring the strict satisfaction of constraints. Taking the SIASUN T12B manipulator as the research object, simulation comparison experiments are carried out in multi-obstacle scenarios. The results show that the PG-MPPI algorithm outperforms the comparison algorithms in the success rate of collision-free target reaching, ensures the smoothness and feasibility of the trajectory, and has a good adaptive capacity to complex environments with unknown obstacle configurations, thus providing an efficient solution for the autonomous and safe operation of manipulators. Full article

To address the high redundancy and weak security inherent in satellite image transmission, this paper proposes an image encryption algorithm founded on a novel five-dimensional fractional-order cosine memristive Hopfield neural network (5D-FOCMHNN). The constructed hyperchaotic system exhibits long-term memory and multistability, capable of generating reconfigurable multi-scroll attractors. A multivariate bit-level scrambling strategy effectively disrupts pixel correlations using neuron state sequences. Furthermore, the system’s chaotic output dynamically governs DNA encoding rules, while a bidirectional diffusion mechanism ensures strong randomization and resistance to differential attacks. Comprehensive experiments demonstrate that the 5D-FOCMHNN-based scheme provides a key space of $2^{256}$, has an information entropy approaching the ideal value of 8, and exhibits robust resilience against cropping, noise, and statistical cryptanalysis, thereby providing a highly secure solution for satellite image transmission. Full article

We establish a general no-go theorem demonstrating that all traversable wormhole configurations in Unimodular Gravity necessarily require exotic matter. The proof relies solely on the geometric flaring-out condition, b′(r₀) ≤ 1, which directly implies that ρ(r₀) + p_r(r₀) ≤ 0 at the throat. This condition represents a violation of the Null Energy Condition and, consequently, of the Weak and Strong Energy Conditions, independently of the particular choice of shape function, redshift function, or equation of state. This result holds for both tidal and zero-tidal-force configurations, showing that the requirement of exotic matter is a fundamental geometric consequence of the traversability condition rather than an artifact of specific solution choices. Therefore, Unimodular Gravity shares this fundamental constraint with General Relativity. Full article

The transition from passive containment to active, responsive management is defining the next generation of intelligent packaging. This evolution creates a critical demand for materials that can be precisely engineered to monitor, regulate, and protect. Hydrogen-bonded organic frameworks (HOFs) have emerged as a uniquely versatile platform in this regard, owing to their synthetically tunable porosity, inherent biocompatibility, and exceptional solution processability derived from reversible supramolecular assembly. This review moves beyond cataloging applications to dissect the fundamental mechanisms by which HOFs enable smart packaging functions, including the following: (i) selective gas capture and atmosphere tailoring via molecular recognition within designed pores; (ii) high-sensitivity optical and electrochemical sensing for real-time quality and safety signaling; and (iii) stimuli-responsive release of active agents (e.g., antimicrobials). We further explore the frontier of integrating HOFs as functional fillers or coatings within polymeric matrices, a key step toward practical devices. Despite challenges such as structural stability and maintaining permanent porosity due to relatively weak hydrogen bonds, this work aims to provide a design blueprint for advancing HOFs from laboratory curiosities to core components of sustainable, multifunctional packaging systems. Full article

In underwater target recognition scenarios, challenges arise as a result of the limited representational capability of acoustic images with single time-frequency features and poor recognition performance due to class imbalances in sample numbers. To tackle these issues, this paper proposes an underwater target recognition method based on feature fusion and balanced ensemble transfer learning. A LiT-INN dual-branch auto-encoder network architecture is employed for time-frequency image feature fusion to solve the weak feature representation capability of single time–frequency features. The Restormer network serves as a shared feature encoder to extract fundamental features, enabling feature fusion of underwater target echo time–frequency image data and generating a fusion image dataset with richer feature information. In order to address class imbalance in sample sizes, a balanced ensemble transfer learning method is constructed using a two-stage decoupled fine-tuning learning method. The first stage employs a uniform sampler strategy to fine-tune the feature extraction module of a pre-trained transfer learning model. The second stage uses multiple balanced sampling optimization methods to fine-tune the classifier. Then, a weight averaging ensemble learning method performs decision-level fusion of multiple weak classifiers. Field test data from three target classes validated the performance of the algorithm, demonstrating a 3% improvement in average recognition accuracy compared to deep transfer learning methods under different imbalance ratios. This method effectively enhances recognition performance for classes with limited samples while significantly boosting overall recognition accuracy, offering a novel solution for underwater target recognition. Full article

In recent years, there has been a notable increase in commercial demand for natural fibers. Consequently, numerous studies have concentrated on formulating innovative industrial production methodologies for natural fibers, with a particular emphasis on the environmental sustainability of production processes. Among natural fiber sources, bamboo has emerged as a leading candidate, attracting considerable interest due to its exceptional renewability, rapid growth, and low cultivation requirements. The contemporary industrial methodologies employed in the extraction of cellulose from bamboo frequently entail the utilization of concentrated solutions of strong acids and bases, often at elevated temperatures and with extended treatment durations. These processes generate highly polluting waste from mineral acids and bases, posing significant environmental challenges and ecosystem damage. In response to the prevailing concerns, there has been a marked increase in the focus on environmentally friendly techniques that combine enzymatic treatments, selective chemical reagents, and optimized mechanical processes. These processes facilitate the extraction of high-quality bamboo fibers, which are suitable for utilization in the textile industry and have the potential to replace synthetic fibers. This work demonstrates the efficacy of methodologies employing more diluted solutions than conventional approaches. Specifically, this study utilizes a weak base, such as NH₄OH, in conjunction with hydrothermal extraction. It is therefore possible for dilute weak base solutions to yield natural fibers after a relatively brief period of processing, typically just a few hours. Full article

The purpose of this work is to investigate the binding state of inorganic elements to flavonoid components in aqueous extract of Silybum marianum (SM) seeds, as well as the antioxidant activity of the extract. This study employed reversed-phase high-performance liquid chromatography (RP-HPLC) to separate silymarin flavonoids in boiling water decoction of SM seeds, and collected the post-column effluent in the segments according to the retention time of seven main silymarin flavonoid components. Inductively coupled plasma mass spectrometry (ICP-MS) was subsequently utilized to quantify nine inorganic elements (As, Cd, Co, Cr, Cu, Fe, Mn, Mo, Zn) in the collected HPLC fractions of the decoction. Meanwhile, electron paramagnetic resonance spectroscopy (EPR) was employed to assess the free radical scavenging activity of aqueous extract of SM seeds, using the signal intensity changes of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and DMPO-OH• adducts as quantitative metrics. The results showed that essential trace elements (Cu, Fe, Mn, Zn) mainly existed as inorganic ions or strong polar forms in the tea-like infusion, with weak binding to flavonoid compounds. On the other hand, the aqueous extract exhibited significant •OH scavenging capacity, with a scavenging rate of 95% against •OH generated by continuous 5 min ultraviolet irradiation of H₂O₂ aqueous solution. This study provides experimental evidence for the development of SM as a food–medicine dual-purpose resource, proposing that consumption of SM seed tea represents a facile and effective approach to supplement trace elements and intake silymarin for enhancing endogenous antioxidant defense. Full article

Three new Mn(II) complexes with imidazo[1,2-a]pyridine derivatives were synthesized and structurally characterized in a solid state by single crystal X-ray diffraction, FT-IR and Raman spectroscopy, and thermal analyses. The investigated compounds include [Mn(3-Climpy)₂Cl₂(MeOH)₂] (1), [Mn(3-Brimpy)₂Cl₂(MeOH)₂] (2), and a rare double chloro-bridged coordination polymer [Mn(impy)₂Cl₂]_n (3). Spectroscopic studies were used to assess their potential stability in DMEM (Dulbecco’s Modified Eagle Medium), and encapsulation in Pluronic P-123 micelles improved their solubility in aqueous solution, as well as cellular uptake and selectivity. Biological evaluation revealed negligible cytotoxicity against most cancer and control cell lines, but unexpectedly high activity against pancreatic adenocarcinoma (PANC-1), exceeding that of cisplatin. Complex 2, bearing a bromine substituent in the imidazole ring, showed the strongest effects, correlating with enhanced intracellular accumulation, reactive oxygen species (ROS) generation, and mitochondrial membrane potential disruption. Molecular docking and protein binding assays demonstrated moderate affinity toward human serum albumin (HSA) and transferrin, whereas DNA interaction was weak and non-damaging. These results highlight the structure–activity relationship of Mn(II) imidazo[1,2-a]pyridine complexes and support their potential as targeted redox-active agents against pancreatic cancer, with polymeric encapsulation providing an effective strategy to enhance biological performance. Full article

Ensuring space environment security through the detection of space debris and non-cooperative threat objects has become a critical mission for next-generation spaceborne surveillance systems. Frequency diversity array (FDA) radar, with its unique range angle-dependent beampattern, offers a transformative capability to distinguish closely-spaced space threats from intense background clutter. However, the operational deployment of spaceborne FDA is inherently hindered by stringent platform resource constraints, including limited power supply, high hardware complexity, and restricted data transmission bandwidth. These physical limitations inevitably lead to incomplete signal observations, resulting in elevated sidelobes that can obscure small, high-speed space debris. To bridge the gap between hardware constraints and high-fidelity surveillance, this paper proposes an AI-enabled data recovery framework based on deep matrix factorization. Specifically designed to process the complex-valued nature of radar echoes, the proposed framework introduces two specialized architectures: a real-valued representation-based method (DMF-Rr) and a native complex-valued deep matrix factorization (CDMF) network that preserves vital phase coherence. By leveraging deep learning to “enable” sparse-sampled systems, the proposed method effectively reconstructs missing observations without requiring prior knowledge of the signal rank. Numerical results demonstrate that the AI-powered CDMF significantly suppresses the high sidelobes induced by resource-limited sampling, enabling the reliable identification and localization of weak threat objects. This study demonstrates the power of AI in overcoming the physical bottlenecks of spaceborne hardware, providing a robust solution for enhancing space situational awareness in an increasingly crowded orbital environment. Full article

A four-quadrant low-coherence envelope detection method was proposed for measuring the surface spacing of optical components, eliminating the requirement for precise control of the delay line scanning step to generate a π/2 phase shift. The method employs an orthogonal polarization Mach–Zehnder (MZ) fiber interferometer, illuminated by a broadband superluminescent diode (SLD), and a four-quadrant polarization-resolved detector to simultaneously acquire spatially phase-shifted interference signals carrying surface spacing information. The interference envelope is directly demodulated to extract surface spacing, thereby decoupling measurement accuracy from mechanical stepping constraints. To enable real-time, high-precision calibration of the delay line, two complementary schemes were implemented: wavelength division multiplexing (WDM)-based calibration and dual optical path calibration. Experimental results confirm that the dual-path scheme exhibits weak dependence on scanning velocity and remains stable across a wide speed range. Repeat measurements of the surface spacing of a 1 mm thick sapphire plate yielded a standard deviation (STD) of 1.3 μm. By relaxing the strict π/2 phase shift condition traditionally imposed on scanning step size, this method improves operational efficiency while maintaining measurement reliability—providing a robust and broadly applicable solution for metrology, including lens surface spacing and transparent plate thickness characterization. Full article