Search Results (421)

Hyperspectral image (HSI) classification requires the extraction of discriminative features from high-dimensional spatial–spectral data. While the Mamba architecture has shown promise in long-sequence modeling with linear complexity, its application to HSI remains constrained by two major hurdles: the unidirectional causal scanning which fails to capture non-causal global dependencies, and the serialization-induced loss of two-dimensional spatial topology and local textures. To overcome these limitations, we propose HAMamba, a novel Hybrid Attention State Space Model. HAMamba facilitates deep representation learning through two core components: a Multi-Scale Dynamic Fusion (MSDF) module and a Hybrid Attention Mamba Encoder (HAME). Specifically, the MSDF module augments spatial perception through parallelized feature extraction and dynamically weighted integration. The HAME synergizes a Bidirectional Sequence Scan Mamba (BSSM) to establish global semantic context and a Spatial–Spectral Gated Attention (SSGA) module to refine local structural details. Comprehensive experiments on four public benchmark datasets demonstrate that the proposed HAMamba significantly outperforms state-of-the-art approaches, achieving a superior balance between classification accuracy and computational efficiency. Full article

Globally, Central Asian wild fruit forests are critical repositories of wild fruit germplasm resources and provide essential ecosystem services. However, their habitats are facing escalating degradation risks driven by climate warming, shifting precipitation regimes, and intensifying anthropogenic disturbances. Accurately quantifying climate-driven spatiotemporal variations in habitat suitability for keystone wild fruit tree species is therefore an essential prerequisite for formulating targeted conservation and management strategies in arid and semi-arid landscapes. In this study, we applied the maximum entropy (MaxEnt) model to simulate the current (2000–2020 baseline) and future (2030s, 2050s, 2070s) potential suitable habitats of two dominant wild fruit tree species, Malus sieversii (Ledeb.) M.Roem. and Prunus cerasifera Ehrh., in the Ili Valley, a core distribution area of Central Asian wild fruit forests in northwestern China. We integrated rigorously screened species occurrence records with key environmental predictors and characterized future climate conditions using three Shared Socioeconomic Pathways (SSPs; SSP126, SSP245, and SSP585) spanning low to high radiative forcing levels. The model exhibited excellent predictive performance (AUC > 0.85), confirming the robustness and reliability of our habitat suitability simulations. Elevation and annual precipitation were identified as the dominant environmental variables governing habitat suitability for both species, highlighting the critical role of terrain–hydroclimate interactions in maintaining viable dryland refugia for wild fruit forests. Under the baseline climate scenario, the total area of suitable habitats reached 24.014 × 10³ km² for Malus sieversii and 18.990 × 10³ km² for Prunus cerasifera. Future climate projections revealed a consistent and significant contraction trend in suitable habitats for both species, with the magnitude of habitat loss escalating with increasing radiative forcing and longer projection time horizons. Specifically, under the high-emission SSP585 scenario by the 2070s, the suitable habitat area is projected to decline by 7.579 × 10³ km² for Malus sieversii and 9.883 × 10³ km² for Prunus cerasifera relative to the baseline. Our findings delineate climate-vulnerable hotspots of wild fruit forests and provide a robust spatial scientific basis for prioritizing in situ conservation, targeted habitat restoration, and anthropogenic disturbance regulation to support the long-term persistence of these irreplaceable wild fruit germplasm resources under accelerating global climate change. Full article

This paper presents a comprehensive life-cycle analysis of distribution transformers, based on realized measurements of the increased power losses as a result of their long-term service under real-world conditions. The study is based on aggregated measured data from extensive fleets of oil-immersed distribution transformers characterized by diverse designs, manufacturing vintages, and service lives. The evolution of no-load losses and short-circuit losses is analyzed as a function of operational duration, structural characteristics, and the specific technologies employed for windings and magnetic core construction. Statistical models describing the variation in these losses are presented, highlighting the limitations of the static assumptions commonly utilized in power distribution network planning. On this basis, an approximation of the time evolution of the transformer’s total power and energy losses is proposed as appropriate for implementation in a life-cycle analysis model. Furthermore, the impacts of thermal loading and abnormal operating conditions—such as unbalanced loads, frequent short circuits, and repeated overheating of the transformer oil—are analyzed as drivers of accelerated transformer aging. These effects are integrated into a unified life-cycle framework, enabling the quantitative assessment of loss variations and their associated operational expenditures (OPEX). A numerical example is provided to evaluate the cost-effectiveness of “repair vs. replacement” scenarios, utilizing a discounted cash flow analysis that incorporates a carbon component. The findings establish a methodological foundation for a broader assessment of technical condition and energy performance, identifying the optimal intervention point for repair or replacement to support decision-making for Distribution System Operators (DSOs) amidst increasing requirements for efficiency and decarbonization. Full article

Drought severely limits maize production. Basic helix-loop-helix (bHLH) transcription factors act as key regulators of plant drought responses; however, the precise regulatory networks they coordinate in maize remain largely unclear. Here, we functionally characterized ZmbHLH81, a drought- and abscisic acid (ABA)-responsive bHLH transcription factor in maize. Subcellular localization confirmed that ZmbHLH81 is a nuclear protein. Overexpression of ZmbHLH81 in Arabidopsis enhanced drought tolerance, whereas CRISPR/Cas9-mediated targeted mutagenesis in maize significantly increased plant sensitivity to drought stress. Physiologically, these mutant lines exhibited accelerated water loss, delayed stomatal closure, compromised antioxidant enzyme activities and elevated malondialdehyde (MDA) accumulation under drought stress. DAP-seq analysis demonstrated that ZmbHLH81 specifically recognizes the conserved G-box motif (CACGTG). Furthermore, integrating DAP-seq and transcriptomic data successfully identified the key downstream targets governed by ZmbHLH81. Molecular assays confirmed that ZmbHLH81 directly targets and transactivates the core ABA signaling kinase gene ZmSnRK2.9 and stress-responsive transcription factor genes ZmNAC20 and ZmHDZ4. Taken together, ZmbHLH81 positively regulates maize drought tolerance by directly activating a specific regulatory module that orchestrates ABA-mediated stomatal closure and reactive oxygen species (ROS) scavenging, providing a promising genetic target for breeding climate-resilient crops. Full article

In disease control and precision management in aquaculture, rapid and accurate identification of common fish diseases is pivotal to mitigating economic losses and ensuring aquaculture profitability. However, fish diseases are characterized by subtle symptoms, polymorphic lesions, and high susceptibility to environmental perturbations such as water turbidity and illumination fluctuations. Existing detection models generally suffer from inadequate lightweight design, poor fine-grained lesion feature extraction, and deficient adaptability to class imbalance, failing to meet the stringent requirements of precise diagnosis in real-world aquaculture scenarios. To address these challenges, this study proposes FDR-Net: a fine-grained lesion detection model for tilapia via multi-scale feature enhancement and spatial attention fusion. Using image data of Nile tilapia (Oreochromis niloticus) covering 6 common diseases and healthy individuals (from the NTD-1 dataset), the model incorporates symmetry-aware design logic, leveraging the morphological and textural symmetry of healthy tilapia tissues to capture lesion-induced symmetry-breaking features, thereby improving fine-grained lesion detection accuracy. Through depth-width scaling coefficients, FDR-Net achieves lightweight optimization while integrating three core modules and a task-specific loss function for full-chain optimization: specifically, a Micro-lesion Feature Enhancement Module (MLFEM) is embedded in key feature layers of the backbone network to accurately extract edge and texture features of incipient fine-grained lesions via multi-scale frequency decomposition and residual fusion; subsequently, a Lightweight Multi-scale Position Attention Module (MS_PSA) and a Single-modal Intra-feature Contrastive Fusion Module (SMICFM) are collaboratively deployed—the former focusing on spatial localization of lesion features, and the latter enhancing lesion-background discriminability through channel-spatial feature recalibration and contrastive fusion; finally, a Class-Aware Weighted Hybrid Loss (CAWHL) function is combined with customized small-target anchor boxes to alleviate class imbalance and further improve localization and classification accuracy of fine-grained lesions. Empirical evaluations on the NTD-1 dataset demonstrate that compared with mainstream state-of-the-art baseline models, FDR-Net achieves a peak recognition accuracy of 90.1% with substantially enhanced mAP_50-95 performance. Retaining lightweight characteristics, it exhibits superior performance in identifying incipient fine-grained lesions and strong adaptability to simulated complex aquaculture scenarios. Collectively, this study provides an efficient technical backbone for the rapid and precise detection of tilapia fine-grained lesions, offering a potential solution for precise disease management in tilapia farming. Full article

To address the progressive collapse of mountainous stilted RC frames induced by debris flows, this study establishes a three-dimensional refined solid model using ABAQUS. The alternate path method (element removal method) is employed to simulate the failure of ground-floor columns under impact, revealing the underlying damage evolution mechanism. The results indicate that the loss of an edge column compromises structural stability significantly more than that of a corner column. Sequential multi-column failure leads to a nonlinear accumulation of damage; specifically, the simultaneous failure of a ‘corner column and its adjacent edge column’ completely severs the outer load-transfer paths, triggering a drastic inward load redistribution. Furthermore, under extreme scenarios, the maximum structural displacement and nodal stress surge to 66.67 mm and 40 MPa, respectively, while the axial force of the core central column jumps by nearly 150% (reaching 2.67 × 10⁶ N). The crushing of internal central columns due to overloading is identified as the critical mechanism triggering global collapse. Based on these findings, design recommendations are proposed, emphasizing the reinforcement of upstream edge columns and the construction of a ‘component-joint-global’ hierarchical defense system. Full article

Dental tissue regeneration, particularly alveolar bone and gingival repair, remains a major challenge in regenerative medicine. 3D bioprinting offers patient-specific and anatomically precise constructs, representing an advanced alternative to conventional grafting. In this study, nanohydroxyapatite (nHA), chitosan (CS), and collagen (CoL) were combined to fabricate and characterize 3D bioprinted dental grafts. SEM revealed a highly porous, interconnected architecture favorable for cell infiltration and nutrient exchange. EDS confirmed Ca/P ratios of 2.06 for nHA/CoL and 1.83 for nHA/CS/CoL, both of which are above the stoichiometric 1.67, indicating the presence of additional mineral phases and ion substitutions. FTIR and XRD verified characteristic functional groups and crystalline phases, including B-type HA with carbonate substitution. Mechanical testing showed that pure nHA exhibited the lowest compressive strength, whereas CoL incorporation improved stiffness. The nHA/CS/CoL composite achieved the highest compressive strength, elastic modulus, and toughness, demonstrating superior mechanical resilience. DSC analysis indicated endothermic peaks at 106.49 °C and 351.91 °C, with enthalpy values (264.91 J/g and 15.09 J/g) surpassing those of nHA alone. TGA revealed ~28.8% weight loss across three degradation stages, confirming enhanced thermal stability. In vitro cytocompatibility testing using L929 fibroblasts validated the biocompatibility of the composites. Collectively, the synergy between bioceramics and biopolymers markedly improved both mechanical and thermal performance. These findings position the nHA/CS/CoL scaffold as a promising candidate for clinical applications in dental tissue regeneration. Unlike conventional grafting materials, this study introduces a synergistically optimized nHA/CS/CoL bio-ink formulation specifically designed for extrusion-based 3D bioprinting of patient-specific dental constructs. The core innovation lies in the precise integration of nHA within a dual-polymer matrix (CS/CoL), which bridges the gap between mechanical resilience and biological signaling, achieving a compressive strength that mimics native alveolar bone while maintaining high cytocompatibility. Full article

Edge intelligence enables significant latency reduction and enhances the timeliness of model-based fault diagnosis. However, existing deep learning-driven bearing fault diagnosis models are ill-suited for deployment on edge devices, primarily due to three critical limitations: (1) Lightweight models typically exhibit inadequate anti-noise performance, failing to meet the reliability requirements of real-world engineering scenarios. (2) Models with superior anti-noise capabilities often demand high-performance hardware for operation, thereby restricting their deployment on resource-constrained edge devices. (3) These models adopt a fixed input length, which makes it difficult to guarantee diagnostic accuracy across diverse application scenarios—attributed to variations in sampling frequencies, bearing parameters, and other relevant factors. To address these challenges, this paper proposes a lightweight anti-noise diagnostic framework (LADF) for edge-intelligent bearing fault diagnosis in complex engineering environments. The LADF comprises three core modules: a dynamic input module (DIM), a lightweight network module (LNM), and a denoising branch. Specifically, the DIM is designed based on the envelope spectrum, leveraging its inherent demodulation characteristics to dynamically adapt to input signals across diverse scenarios. Group convolution and layer normalization are employed to construct the LNM, ensuring robust diagnostic performance while achieving efficient computation. The denoising branch constrains the feature extractor via a loss function, enabling it to learn generalized fault features under varying noise environments and thereby enhancing the anti-noise capability of the framework. Finally, the proposed LADF is validated through test rig experiments on two datasets of train axle box bearings. Comparative analysis with state-of-the-art models demonstrates that the LADF achieves superior diagnostic stability and anti-noise performance while maintaining a more lightweight architecture, making it well-suited for edge deployment in railway bearing fault diagnosis. Full article

The rapid progress of large language models (LLMs) has fostered the development of domain-specific variants in law, medicine, and finance. However, existing legal LLMs still struggle to generate contextually grounded and regulation-compliant responses in complex scenarios of fair competition review. To address this, we present FairAgent, a collaborative multi-agent framework that unifies data refinement and reinforcement learning for legal reasoning. FairAgent integrates two core modules: (1) EchoCourt, a closed-loop data generation and refinement pipeline that constructs high-quality question–answer pairs through generation, critique, and optimization guided by a hierarchical Fairness Knowledge Forest; and (2) a two-stage outcome-based reinforcement learning mechanism that progressively teaches the model to invoke and integrate external retrieval in reasoning. We further enhance learning stability through a RAG-based rollout and retrieval-mask loss. Extensive evaluations demonstrate that FairAgent significantly improves reasoning accuracy, interpretability, and compliance in fair competition review compared with state-of-the-art baselines, establishing a scalable framework for retrieval-augmented legal intelligence. Full article

Chronic hypoxia is a hallmark of aging and retinal diseases such as age-related macular degeneration (AMD), yet the molecular mechanisms that enable retinal pigment epithelium (RPE) cells to survive under sustained low-oxygen conditions remain poorly understood. To address this, we conducted transcriptomic profiling and a genome-wide CRISPR-Cas9 loss-of-function screen in ARPE-19 cells exposed to chronic hypoxia (1% and 5% O₂), mimicking the retinal disease environment. The CRISPR screen identified genes whose loss compromises RPE viability or fitness under hypoxia, while transcriptomic profiling revealed oxygen-dependent shifts in key functional modules. These findings converged on pathways related to mitochondrial function, extracellular matrix remodeling, vascular signaling, and cell cycle regulation, identifying unique functional nodes specific to RPE cells. These core processes are also implicated in retinal diseases, such as AMD. Together, these complementary approaches provide an integrated view of the molecular networks driving RPE adaptation to hypoxic stress and highlight novel gene candidates that may serve as therapeutic targets in retinal disease. Full article

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have transformed the management of type 2 diabetes and obesity, yet their actions extend beyond glycemic control and weight loss. This narrative review synthesizes current preclinical and clinical evidence examining the bidirectional relationship between glucagon-like peptide-1 (GLP-1) receptor agonists and circadian biology. A structured literature search was conducted in PubMed using combinations of the terms ‘GLP-1,’ ‘circadian,’ ‘chronobiology,’ ‘sleep,’ ‘obesity,’ and ‘type 2 diabetes’ through January 2026. Accumulating evidence indicates that GLP-1 physiology is closely coupled to circadian timing systems and sleep–wake regulation. In this narrative review, we synthesize emerging data that reframe GLP-1RAs as chronometabolic modulators, acting at the intersection of metabolism, circadian biology, and sleep. We review circadian control of GLP-1 secretion by intestinal L-cells, emphasizing the role of core clock genes and the vulnerability of incretin rhythms to circadian misalignment from shift work, nocturnal light exposure, and sleep loss. We then examine GLP-1 receptor signaling within central and peripheral clock networks, including feedback effects on hypothalamic and hepatic circadian regulation. Emerging data suggest that GLP-1 signaling is under circadian regulation and may, in turn, influence central and peripheral clock systems. Comparative discussion of semaglutide, liraglutide, and tirzepatide highlights agent-specific pharmacokinetics and emerging clinical data linking GLP-1RA therapy to sleep outcomes, particularly obstructive sleep apnea. Finally, we outline translational opportunities for chronotherapy and precision medicine, positioning GLP-1RAs as integrative tools for metabolic and sleep-related disease rather than purely weight-centric therapies. We propose that GLP-1 receptor agonists may function as chronometabolic modulators, with potential implications for personalized chronopharmacological strategies in metabolic disease. Full article

As one of the world’s leading cotton-producing countries, China frequently experiences severe yield reductions due to crop diseases and pest infestations, with losses often exceeding 20%. Although computer vision models can identify diseased plants, they currently fail to connect visual symptoms to the diagnostic reasoning process used by agronomists. This leads to text descriptions that ignore the biological causes of the damage. To fix this, we built Multimodal Knowledge Graph-Enhanced Cross Vision Transformer-18-Dagger-408 and Text-to-Text Transfer Transformer for Cotton Disease and Pest Image Captioning (MKG-CottonCapT6), a model that uses a local knowledge database to generate professional diagnostic reports from field images. The technical core consists of a Multimodal Knowledge Graph (MMKG) containing 14 types of entities (such as Pathogens and Control Agents) and 12 types of relations. We use a Cross Vision-Transformer-18-Dagger-408 (CrossViT) encoder to capture both the overall leaf shape and microscopic details of pests. Through a Visual Entity Grounding (VEG) module, the model maps visual features directly to specific triplets in the graph. These triplets are then turned into text sequences and fused with image data in a Text-to-Text-Transfer-Transformer (T5) decoder. To train the model, we collected a dataset of cotton images paired with expert descriptions of lesions, colors, and affected plant parts. Tests show that MKG-CottonCapT6 performs better than standard models, reaching an Information-based Metric for Image Captioning (InfoMetIC) score of 72.6%. Results prove that by using a specific alignment loss (${\mathcal{L}}_{align}$), the model generates reports that correctly name the disease stage and recommend specific chemicals, such as Carbendazim or Triadimefon. This framework provides a practical tool for farmers to record and treat cotton diseases with high precision. Full article

Background: The global spread of monkeypox virus (MPXV) highlights an urgent need for thermostable and easily administrable vaccines. Current orthopoxvirus vaccines are limited by cold-chain dependence and inconvenient injection-based delivery. Objectives: This study aimed to develop a dissolvable microneedle (DMN) vaccine against monkeypox based on a replication-deficient orthopoxvirus platform, through systematic formulation screening, stabilization mechanism exploration, and rigorous in vivo immunogenicity evaluation. Methods: A film-based approach was adopted for efficient, high-throughput formulation screening and thermostability assessment. NTV was mixed with excipients and dried into solid films. Stability was monitored via RT-qPCR after storage at 4 °C to 40 °C. The lead formulation was physically characterized, then used to fabricate MVA-BN-loaded DMN patches, which were further evaluated for in vivo immunogenicity via immunization in BALB/c mice. Results: The optimal formulation F2 (containing dextran, L-threonine, and BSA/HSA) showed a potency loss of only ~1 log₁₀ after 2 months at 25 °C, and <1 log₁₀ loss after 1 week at 37 °C. SEM revealed a porous virus-entrapment morphology, and FTIR indicated enhanced hydrogen bonding between the virus and the dextran matrix. The formulation was successfully manufactured into DMNs that dissolved within 5 min. In mice, these DMNs elicited robust MPXV-specific IgG and neutralizing antibody responses, with immunogenicity comparable to that induced by conventional intramuscular injection. Conclusions: This study successfully established a thermostable formulation and dissolvable microneedle delivery platform for replication-deficient orthopoxvirus vaccines against monkeypox. The optimized DMN vaccine induced robust MPXV-specific immune responses in mice with immunogenicity comparable to intramuscular injection, addressing the core limitations of current vaccines and providing a promising solution for monkeypox prevention. Full article

Vehicle-mounted sensing enables high-resolution urban monitoring but remains constrained by heterogeneous multimodal integration, intermittent connectivity, privacy-sensitive visual data, and the absence of enforceable multi-provider governance. This paper introduces a governance-aware private cloud architecture that treats provider isolation, role-based access control, and privacy-by-design as core architectural properties rather than application-layer add-ons. The layered, containerised microservice design supports asynchronous store-and-forward ingestion, modality-specific processing pipelines, and GPU-accelerated object detection for structured metadata extraction. A key innovation is ingestion-time visual abstraction, which structurally separates raw imagery from derived observations and enforces lifecycle-based retention policies, embedding data minimisation directly into the data flow. The fully open-source implementation is validated through a two-month multi-provider pilot with continuous multimodal collection. Results demonstrate stable ingestion without data loss, real-time visual inference (~200 ms per frame), strict provider-level isolation under concurrent access, and up to 95% storage reduction via metadata abstraction. The findings establish a replicable architectural paradigm for scalable, privacy-aware, multi-actor mobile sensing infrastructures suitable for metropolitan-scale smart city deployment. Full article

Accurate urban green space (UGS) mapping is essential for assessing urban ecosystem health and supporting sustainable development planning. However, deep learning-based UGS segmentation from Red–Green–Blue (RGB) remote sensing imagery faces two major challenges. First, the absence of near-infrared (NIR) information in RGB imagery hinders the ability to discriminate spectrally similar classes, such as vegetation and non-vegetation. Second, conventional convolutions with fixed receptive fields struggle to model the complex and irregular boundaries characteristic of UGS. To address these challenges, this study combined the Normalized Green–Red Difference Index with the Deformable Convolutional Network Lab (NGRDI-DCNLab) model, a semantic segmentation model tailored specifically for RGB-only imagery. Based on the DeepLabV3+ framework, the model introduced three core improvements: (1) The Normalized Green–Red Difference Index (NGRDI) was incorporated to compensate for the absence of NIR information, enhancing the spectral separability of vegetation pixels. (2) Standard convolutions in the decoder were replaced with deformable convolutions, enabling the network to more effectively adapt to irregular boundaries of UGS. (3) An NGRDI-weighted loss function was designed to assign higher weights to challenging samples and uncertain boundary regions, guiding the model toward more accurate edge delineation. Comprehensive evaluations on two public high-resolution datasets—the Wuhan Dense Labeling Dataset (WHDLD) and the Beijing subset of the Urban Green Space-1m dataset (UGS-1m_Beijing)—demonstrated that the NGRDI-DCNLab model outperformed existing popular deep learning models (like Unet++, etc.). Specifically, the deformable convolution effectively enhances the feature modeling capability for irregular boundaries; incorporating the NGRDI vegetation index as a fourth channel strengthens spectral feature representation and improves the distinction between vegetation and non-vegetation; and adding the dynamic NGRDI-weighted loss enables targeted learning for challenging samples. Through the synergistic effect of these three modules, the model achieves mean Intersection over Union (MIoU) scores of 84.77% and 77.66%, as well as F1-scores of 91.75% and 87.27%, on the WHDLD and UGS-1m_Beijing datasets, respectively. Furthermore, the model exhibited certain generalization capability on the unmanned aerial vehicle (UAV) dataset, the Urban Drone Dataset 6 (UDD6), attaining an MIoU of 87.43%. Our results confirm that high-precision UGS extraction is achievable using only RGB remote sensing imagery, providing a cost-effective and practical technical solution for refined urban governance and ecological monitoring. Full article