Search Results (26,785)

Traditional crack monitoring methods, relying on discrete point sensors, cannot capture the full spatiotemporal evolution of cracks. To address this limitation, this paper presents a distributed online monitoring approach using ultra-weak Fiber Bragg Grating (UWFBG) array sensing technology. A 16 m full-scale π-beam was instrumented with a grating array strain sensing system and tested under progressive mid-span loading until failure. The array successfully detected crack initiation at 848.7 kN (0.9P1) and tracked the transformation from L-shaped to U-shaped cracks, yielding a final crack count of 90 with a maximum width of 1.21 mm and length of 246.5 cm at 1791.7 kN. The strain–load curves exhibited a clear linear-to-nonlinear transition and continuous slope increase, closely matching manual observations. Quantitative correlation analysis further established a strong linear relationship between UWFBG peak strains and manually measured crack widths, with the fitting equation ε = 7918 · w − 110 and a coefficient of determination R² = 0.971, providing a specimen-specific basis for strain-based crack severity estimation that requires in-situ calibration before field application. The UWFBG array maintained stable signal acquisition throughout the entire loading process, offering superior data continuity and measurement range compared to resistive strain gauges, which suffered progressive data loss after cracking. The results demonstrate that the proposed method can provide real-time, full-field strain mapping and quantitative crack evolution monitoring, offering a powerful tool for bridge health assessment. Full article

Aerial tiny objects naturally possess conjugate symmetry in the frequency domain, yet complex scenarios, heavy clutter, and frequent rotation/occlusion lead to severe detail loss, inaccurate contour modeling, and high false/miss rates in existing detectors. Current Fourier-based methods neither leverage object symmetry nor coordinate with Fourier analysis to jointly enhance contour representation and spatial-frequency feature learning, suffering from weak fusion, phase-sensitive coefficient regression, and poor discriminability. To fill this gap, we propose the Frequency-Aware Fourier Series Detection Network (FAFSDet), which explicitly exploits the inherent symmetry of tiny objects and their frequency-domain representations. Specifically, FAFC (Frequency-Aware Feature Fusion) employs conjugate-symmetry-guided dynamic low-pass filtering, similarity-based rearrangement, and adaptive high-frequency enhancement to recover degraded symmetric patterns. FSPRM (Fourier Series Profile Representation) utilizes the symmetric positive–negative frequency distribution to achieve compact parametric contour encoding and normalized centroid-shape description. FSDIM (Fourier Series Detection Inference) incorporates symmetric multi-scale branches, a rolling-optimization loss that eliminates phase interference while preserving coefficient-regression symmetry, and inverse Fourier transform for precise contour reconstruction and end-to-end detection. Extensive experiments on DOTA, AI-TOD, and UCAS-AOD demonstrate that our method achieves superior performance (mAP 82.18%, 51.2%, and 90.70%, respectively) and strong generalization, particularly in scenarios where symmetry is most severely compromised, confirming that exploiting these symmetry properties substantially boosts detection accuracy. Full article

Robust quadrotor trajectory tracking under wind disturbances is challenging because real outdoor wind is multimodal with time-varying and heavy-tailed characteristics, whereas existing solutions suffer from insufficient disturbance observability and poor out-of-distribution robustness. To this end, this paper presents a robust quadrotor trajectory-tracking method based on residual-aware deep reinforcement learning for multimodal wind disturbances. The proposed method augments a standard tracking policy with a compact online acceleration-residual feature, which provides disturbance-related information without requiring an explicit wind sensor, a full disturbance observer, or a long-history recurrent estimator. To reduce excessive dependence on wind-specific temporal patterns, a residual-input regularization term is introduced during policy optimization. In addition, a tail-risk-aware reward is designed to balance nominal tracking accuracy, control smoothness, and suppression of large tracking deviations. The proposed method is evaluated under in-distribution wind, held-out out-of-distribution wind, and measured real-wind disturbances. The results show that the proposed method achieves the most balanced robustness under multimodal wind conditions compared with baselines. Full article

Multistakeholder partnerships (MSPs) are central to the 2030 Agenda for Sustainable Development, yet the UN Partnership Platform suffers from an extreme validation asymmetry: fewer than 5% of registered projects undergo independent verification. This study examines whether discourse patterns distinguish validated from non-validated MSPs. Applying BERTopic neural topic modeling to 3807 project descriptions in the GENESIS WP4 Database, we identify three substantive thematic clusters, spanning climate, sanitation, and health; marine and fisheries; and sustainable textiles, alongside a combined language–artifact cluster excluded from thematic interpretation. The target topic count was fixed to ensure reproducibility after an initial automatic-selection step proved unstable across runs; a multi-seed check confirms stable topic counts with moderate assignment-level agreement (mean Adjusted Rand Index = 0.63). We introduce the Validated-Discourse Similarity Index (VDSI), a leave-one-out cosine similarity measure comparing each project’s textual embedding to a centroid of validated MSPs shown to be more homogeneous than random samples of non-validated projects (p = 0.002). VDSI analysis indicates that 3538 of the 3807 projects (92.9%) exhibit Inconsistent Non-Validated language resembling validated MSPs despite lacking independent verification, though raw semantic similarity alone only modestly discriminates validation status (AUC = 0.686), indicating a real but partial signal rather than a proxy for validation. Partner count is the strongest structural discriminator of validated MSPs (r = −0.389, p < 0.001), remaining significant after adjusting for SDG scope, description length, duration, topic, and language (adjusted OR = 1.38 per SD, p < 0.001), and consistent across six SDG-level subgroups. These findings extend SDG-washing scholarship to the UN multilateral voluntary commitment ecosystem and offer a provisional, discourse-informed basis for partnership evaluation. Full article

Motor Imagery (MI) decoding based on electroencephalogram (EEG) is promising for Brain–Computer Interface (BCI) applications, yet existing methods generally suffer from two major limitations. (1) Global EEG signal analysis overlooks region-specific neural activity patterns, causing biased feature extraction and poor inter-subject generalization. (2) Few MI-EEG decoding studies adopt frequency decomposition for multi-rhythm feature extraction. Even when adopted, conventional methods rely on predefined frequency bands and suffer from mode mixing, failing to preserve the inherent fractal self-similarity and nonlinear characteristics of EEG signals, restricting the extraction of fine-grained specific features. To address these issues, we propose RMF-Net, a novel model integrating brain region division and Multi-variable Variational Mode Decomposition (MVMD). The model partitions EEG into functional brain regions based on MI neural mechanisms, performs dynamic modal feature extraction for each region via MVMD, and enables efficient cross-regional spatiotemporal feature interaction through an adaptive fusion. On the BCI Competition IV 2a open EEG MI dataset, our model achieves 80.06% accuracy in cross-session tasks and 63.05% in cross-subject tasks, outperforming other mainstream methods. Further analysis verifies that the cross-regional feature weight distribution of RMF-Net conforms to neuroanatomical principles. This work demonstrates that the spatiotemporal feature fusion framework combining brain region segmentation and fractal-aware multimodal signal decomposition is effective for EEG MI decoding tasks. Full article

Federated learning enables collaborative model training without centralizing private data, yet its deployment on edge devices remains constrained by communication overhead, heterogeneous data distributions, and privacy leakage from shared model updates. Spiking neural networks offer an energy-efficient alternative to conventional artificial neural networks by transmitting sparse binary events rather than dense activations, but existing federated spiking learning methods still suffer from inefficient gradient exchange and insufficient privacy protection under non-independent and identically distributed data. This paper proposes PriSparK, a privacy-preserving and communication-efficient federated spiking neural learning framework that jointly exploits temporal event sparsity, adaptive client-side spike-gradient compression, and privacy-calibrated aggregation. The core of PriSparK is a novel Event-Sparse Differentially Private Federated Spiking Optimization algorithm, which converts local surrogate gradients into spike-saliency-aware sparse updates, dynamically allocates communication budgets across layers and time steps, and injects calibrated Gaussian noise after clipping in a low-dimensional event subspace. To mitigate accuracy degradation caused by aggressive compression and privacy perturbation, PriSparK further introduces a membrane-aware error-feedback mechanism and a heterogeneity-adaptive server aggregation rule that weights client updates according to spike activity stability and local distribution drift. Experiments on neuromorphic and vision benchmarks, including N-MNIST, DVS128 Gesture, CIFAR-10, and Fashion-MNIST, show that PriSparK achieves competitive or superior accuracy compared with federated artificial neural and spiking baselines while substantially reducing uplink communication. Under strong privacy constraints, PriSparK maintains stable convergence and improves the accuracy–communication–privacy trade-off, demonstrating its potential for privacy-sensitive edge intelligence with event-driven neural computation. Full article

Coordinating multiple unmanned aerial vehicles (UAVs) for cooperative missions requires agents that perceive their environment, reason about objectives, and generate joint actions. Vision–language–action (VLA) models unify these capabilities but lack a principled source of multi-agent training data and suffer from a training–inference discrepancy in closed-loop control. We propose MiniUAV-VLA, a compact centralized VLA controller for simulated multi-UAV search-and-elimination based on multi-agent reinforcement learning (MARL) expert distillation. A QMIX expert policy achieving 100% mission success generates multimodal demonstrations pairing rendered tactical map images with structured textual state prompts. A 158 M-parameter VLA model with approximately 65 M trainable parameters in the MiniMind-3V backbone and vision projection is fine-tuned with a multi-agent discrete action head that jointly predicts actions for all UAVs in a single forward pass. We identify a training–inference feature mismatch in behavior cloning and address it via prompt-end action pooling, which extracts action-relevant hidden states at the user–prompt boundary rather than after the generated response. In closed-loop evaluation with four drones and six mobile targets averaged over five evaluation seeds, MiniUAV-VLA reaches 74.4 ± 4.6% mission success against 9.4 ± 2.1% for a random policy and 16.2 ± 3.2% for an observation-limited greedy baseline. Across five independent training runs, prompt-end action pooling improves mean closed-loop success from 40.6% to 76.2% over the last-token alternative. These results support MARL expert distillation as a data-efficient route to compact multi-agent VLA control in this simulated setting. Full article

Soil salinization has become a major global abiotic threat restricting sustainable viticulture, especially in coastal and inland saline–alkali zones. Unlike cereal crops mainly suffering from sodium toxicity, grapevine (Vitis vinifera L.) is a typical chloride-sensitive woody perennial, subjected to superimposed damages of osmotic stress, ionic imbalance and secondary oxidative injury under saline conditions which severely suppress vegetative growth and degrade berry quality. This review systematically summarizes the multi-layered physiological adaptive mechanisms of grapevine against salt stress, including ion homeostasis maintained by salt overly sensitive (SOS), Na⁺/H⁺ exchanger (NHX) and chloride channel (CLC) transporter families, active accumulation of osmoprotectants, synergistic enzymatic and non-enzymatic antioxidant systems, and phytohormone crosstalk networks formed by endogenous phytohormones (abscisic acid, ABA; jasmonic acid, JA; salicylic acid, SA; brassinosteroid, BR) and small signaling molecules. We further elaborate comprehensive molecular regulatory cascades governing salt tolerance, covering core functional genes for ion transport, master transcription factor families WRKY, MYB, APETALA2/Ethylene Response Factor (AP2/ERF), NAC, basic helix–loop–helix (bHLH) and emerging epigenetic regulatory layers mediated by deoxyribonucleic acid (DNA) methylation, microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). In addition, we integrate four categories of field mitigation strategies for saline vineyards: germplasm improvement via salt-tolerant rootstock grafting, rhizosphere soil basal amendment, exogenous biostimulant regulation, and precision agronomic optimization. Current experimental systems do not fully recapitulate complex field combined-stress conditions, as most studies rely on laboratory single-salt stress simulation. Meanwhile, multi-omics, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) gene editing and high-throughput phenotyping tools provide promising approaches to deepen our understanding of grape salt tolerance. This review constructs a comprehensive theoretical framework linking physiological responses, molecular regulatory networks and practical field technologies, offering systematic theoretical references and technical guidance for salt-tolerant germplasm innovation and environmentally sustainable viticulture on saline soils. Full article

Automated fracture detection remains challenging because fracture regions often exhibit low contrast, blurred boundaries, large scale variations, and substantial morphological diversity. Although deep learning-based detectors show promising performance, they often suffer from limited category coverage and insufficient multi-scale feature representation. To address these issues, we propose LiteFracNet, a lightweight framework for accurate and efficient fracture detection. C3-CFormer enhances feature representation via residual aggregation, gated dynamic modeling, and long-range dependency extraction. C2Mona improves fine-grained fracture perception using multi-scale convolution and feature separation–reconstruction. The OmniKernel Fusion Pyramid Network (OFPN) promotes cross-level feature interaction and multi-scale information propagation, improving detection of subtle fractures. The Fusion-Enhanced Detection Head (FED-Head) employs channel alignment and shared convolutions to unify multi-scale features, reducing redundancy in conventional multi-branch heads. On the HBFMID, LiteFracNet achieves a mAP50 of 93.53% with 2.41 M parameters, 8.1 GFLOPs, a 4.9 MB model size, and 87.1 FPS inference speed. It also achieves a mAP50 of 61.43% on the pediatric GRAZPEDWRI-DX dataset, demonstrating competitive cross-dataset adaptability. These results indicate that LiteFracNet has the potential to efficiently assist computers in detecting fractures. Full article

Suffering and theodicy are two interconnected concepts: while the former indicates the embodied personal experience of pain, the latter represents the theological effort to explain the existence of suffering and evil by reconciling them with the idea of a world created by an omnipotent, omniscient, and infinitely good God. From a sociological perspective, the first scholar to address the problematic relationship between these two concepts was Max Weber. The problem of suffering in Weber was linked, first of all, to the process of social rationalization, that is, to the growing predominance of formal rationality in Western social life, the ultimate outcome of which he famously described through the metaphor of the “iron cage” as the existential suffering of modern Western humanity. Subsequently, in his Sociology of Religion, he adopted the concept of “disenchantment” to refer to the cultural rationalization of worldviews as a process characterizing most Asian religions within a broader universal perspective. According to Weber’s comparative investigation, at the origin of all attempts at religious rationalization lies the same universal problem of theodicy, namely the search for an explanation capable of justifying the unequal distribution of the goods of happiness among human beings and the resulting suffering, perceived as unjust. For Weber, the paradox of theodicy consists in the fact that the rationalization of thought (worldviews) and action (conducts of life), in an attempt to confer meaning on and alleviate suffering on the basis of one of the two conceptions of salvation (rebirth or redemption), instead produces the unintended consequence of making the human condition even more vulnerable to the experience of suffering, since it comes to be regarded as morally more outrageous and intellectually more unacceptable. Later, the founders of the Frankfurt School, Horkheimer and Adorno, essentially took up and expanded the Weberian thesis of the paradox of theodicy by showing that the second disenchantment brought about by modernity affected the rationalization of metaphysical–religious worldviews through the return of the irrational mythical–magical forces that had once been subjugated and turned into myth by the Enlightenment. As a consequence, the loss of individual freedom, reduced to a mere functional response within modern political and economic bureaucratic systems, comes to coincide with the loss of meaning of any autonomous cultural framework rationally grounded in value orientations. Habermas further developed Weber’s thesis of the loss of freedom by situating the process of social rationalization within a broader paradox that illustrates the contemporary inability of colonized lifeworlds, now completely bureaucratized and technicized, to confer meaning on the subjective experience of suffering. More recently, in the so-called Anthropocene, a non-linear and non-unidirectional dialectic of disenchantment and re-enchantment has emerged, in which the two coexist as constitutive processes of modernity, allowing the paradox of theodicy and suffering to be reconsidered in terms of a new anthropodicy. Full article

With the fundamental establishment of China’s territorial spatial planning system featuring the integration of multiple plans, transitioning from static blueprint-centric regulation to dynamic implementation governance against rising uncertainties stands as a core challenge in advancing spatial governance modernization. The current patch-based management suffers from excessive rigidity and insufficient adaptability amid the evolving complexity of territorial systems. To tackle this issue, this paper develops a holistic framework for the hierarchical adaptive dynamic management of territorial space based on Complex Adaptive System (CAS) theory. Drawing on the CAS perspective, this study proposes a Five-in-One evaluation criterion as the value benchmark for spatial adjustment, constructs a five-tier hierarchical governance logic aligned with China’s administrative system, and designs differentiated regulatory strategies based on stable and change-prone spatial zoning. This research provides a new paradigm that combines theoretical depth and practical operability to boost the scientificity and implementation effectiveness of territorial spatial planning and helps facilitate the transformation of the territorial spatial governance system toward greater resilience and adaptability. Full article

Conventional snapshot-type polarization devices often suffer from inherent ohmic losses caused by the metal structure, resulting in low utilization of system light energy. This research proposes a dual-layer sandwich architecture metasurface that integrates polarization control and high light transmittance for the mid-wave infrared 3~5 μm band. The top metal polarization-selective structures and the bottom dielectric hemispherical anti-reflection (AR) array are integrated monolithically on the same substrate. Specifically, the numerical simulations predict a peak transmittance of 95% at 4.4 and 4.8 μm, while maintaining extinction ratios ranging from 81.8 dB to 84.3 dB. This enables high extinction ratio polarization splitting while significantly broadening the transmittance flux of the device. It breaks the inherent trade-off between “high extinction ratio” and “high transmittance” in polarization devices, providing a high signal-to-noise ratio hardware foundation for high temporal resolution detection. Full article

Indoor mobile robots equipped with low-cost and sparse sensors often suffer from limited vertical perception and dynamic residual artifacts in the final map. This paper presents a lightweight 2.5D simultaneous localization and mapping (SLAM) framework using a single-line laser distance sensor (LDS), time-of-flight (ToF) sensing, wheel odometry, and an inertial measurement unit (IMU). In this work, 2.5D refers to a 2D grid map with discretized vertical occupancy bins for each grid cell, rather than a full continuous 3D reconstruction. The system integrates multi-sensor synchronization, motion correction, error-state Kalman filter (ESKF)-based state estimation, normal distributions transform (NDT) registration, and pose graph optimization to reconstruct a pose-consistent global map. Based on this map, an offline dynamic refinement module estimates temporal voxel support across keyframes, extracts low-support candidate regions, and applies geometric clustering and isolated-point filtering to suppress transient residual artifacts while preserving stable structures. A 24-bit RGB occupancy encoding is further proposed to store the discretized vertical occupancy state in a compact three-channel image format. The proposed framework emphasizes system-level deployment value by combining sparse multi-sensor mapping, conservative offline refinement, and compact height-aware map export on a low-cost indoor robot platform. Experiments on public datasets, embedded hardware, and self-collected indoor sequences evaluate odometry reference performance, resource usage, platform-specific 2.5D mapping, dynamic refinement, and height-aware encoding. Full article

Satellite glial cells (SGCs) play a critical role in the development and maintenance of chronic pain through complex interactions with inflammatory mediators and the opioid pathway. This systematic review synthesizes recent advances in the molecular mechanisms underlying SGC activation and implications for chronic pain management, particularly in conditions associated with the dorsal root ganglia (DRG) and trigeminal ganglia (TG). A systematic search was conducted in four databases (PubMed, Embase, Scopus, and Web of Science) covering studies published between January 2004 and May 2026. After screening 111 records, 19 studies were included. This review highlights how pro-inflammatory cytokines such as IL-1β, IL-1α, and TNF-α, as well as neurotransmitters like ATP and glutamate, contribute to SGC activation, neuroinflammation, and pain modulation. It also explores the role of receptors like CXCR4, TLR4, and P2X7 in SGCs in enhancing analgesic effects and their contributions to opioid tolerance and hyperalgesia. The findings underscore the potential of targeting SGCs to improve pain management outcomes across various pain models, including neuropathic, cancer-related, and visceral pain. Despite promising insights, variability in study methodologies and the complexity of glial–neuronal interactions present challenges. Future research should focus on standardizing experimental protocols and developing targeted therapies to modulate SGC activity to offer hope for patients suffering from chronic pain, particularly in managing opioid tolerance. Full article

Flexible capacitive sensors for electronic skins and soft robotic systems are expected to provide not only high-pressure sensitivity but also multifunctional sensing capabilities. However, conventional dielectric layer designs often suffer from a trade-off among multiple functionalities. To address this challenge, we developed a flexible sensor featuring a hybrid microstructured functional layer for pressure sensing, distance monitoring, and material identification. The functional layer was a polydimethylsiloxane (PDMS) film embedded with micro-sized sugar particles and patterned with microstructures on its surface. The pressure-sensing performance, such as pressing sensitivity, response time, and hysteresis, was first evaluated. The pressure sensitivity reached 3.55 × 10⁻² kPa⁻¹ at an applied force of 1 N, which is significantly greater than that of the sensor using either a flat PDMS layer or a PDMS film embedded solely with sugar particles. The hybrid microstructured sensor also exhibited superior performance in terms of hysteresis and repeatability. Moreover, the sensor was shown to measure the distance to an object with a sensitivity of 0.023 mm⁻¹. Furthermore, the robust identification of materials with different permittivities was demonstrated using the flexible sensor. Given its multifunctional, non-contact, and high-sensitivity capabilities, this flexible sensor holds significant potential for integration into advanced electronic skins, intelligent soft robotics for tactile object sorting, and human–-machine interfaces. Full article