Search Results (110)

This study introduces a training-free, self-contained adaptive low-light image enhancement (LLIE) framework driven by a metaheuristic optimization algorithm (MOA) and a context-aware dual image quality assessment (IQA) engine. Although deep-learning-based methods exhibit rapid inference, their static parameters often suffer from severe performance degradation in out-of-distribution (OOD) scenarios—such as those involving unseen sensor noise or environmental shifts. To bridge this generalization gap, the proposed framework operates within a decoupled HSV color space, specifically targeting the luminance ($V$) channel to formulate image enhancement as an instance-specific optimization task. We introduce a novel hybrid Log-Gamma mapping function that mathematically unifies the localized dark-stretching capabilities of logarithmic compression with the global dynamic range regulation of power-law gamma curves, thereby substantially expanding the expressiveness of the transformation space. To govern parameter convergence without reference images, a multi-stage Low-Light Image Discrimination (LLID) engine classifies the input frame by computing context-specific trimmed skewness residuals and global intensity means, effectively mitigating highlight biases. Under normal-light conditions, the swarm intelligence engine optimizes the Log-Gamma coefficients via the Patch-based Contrast Quality Index (PCQI) to maximize structural fidelity; conversely, under severe low-light degradations, the framework leverages the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) to minimize spatial artifacts. Using the Marine Predators Algorithm (MPA), the framework iteratively searches the continuous bounding space to fine-tune a parameter matrix tailored exclusively to each image. Empirical evaluations across four benchmark datasets (Bicycle, DF1000, DICM, and VV) validate the effectiveness of the proposed paradigm. The proposed variant, $OLG\left(\mathrm{MPA}\right)$, secured the top average rank in internal algorithm ablation ($\overline{R}=3.10$) and achieved a competitive global average rank ($\overline{R}=2.95$) against four state-of-the-art deep networks, matching the performance of leading data-driven models. Although the evolutionary optimization loop incurs an average per-frame latency of $17.300 \mathrm{s}$, this instance-specific paradigm successfully trades instantaneous processing speed for absolute domain adaptability and predictable, artifact-free image restoration. Full article

Terahertz (THz) fingerprint spectroscopy enables label-free identification of molecular vibrational signatures, but trace biomolecular absorption is too weak to be reliably resolved in free-space measurements. To address this limitation, we propose a four-degree-of-freedom geometrically encoded THz metasurface for broadband multichannel fingerprint sensing. The substrate-free self-supporting aluminum structure incorporates four independently tunable geometric parameters: gap angle θ, outer ring radius R, scaling factor S, and ring width W. By regulating these parameters, multiple resonance-tuning pathways are established, enabling designable multiband spectral coverage over 0.6–1.4 THz and flexible matching with the fingerprint bands of L-hydroxyproline (L-HYP). Numerical simulations show that the metasurface achieves a refractive-index sensitivity of 512.66 GHz/RIU with a linear fitting coefficient of R² = 0.99708 and a mean Q factor of 4.72. For biomolecular fingerprint sensing, the encoded resonances overlap with the L-HYP absorption bands near 0.73 and 1.17 THz, producing AIT-like spectral modulation and envelope-derived attenuation enhancement. Compared with an unstructured analyte reference, the valid 0.73 THz readout gives enhancement factors of 5.76 and 7.09 for the R and S channels, respectively, while the four encoded channels provide enhancement factors of 3.53–4.45 at 1.17 THz. This design provides a compact strategy for broadband multichannel THz fingerprint enhancement, offering a promising route for monitoring collagen-metabolism-related biomarkers and advancing label-free biochemical sensing, fibrosis-related molecular screening, and integrated broadband THz detection. Full article

Accurately identifying influential nodes is essential for analyzing network structures and optimizing information propagation. Existing methods predominantly rely on single indicators such as degree, H-index, or k-shell, inherently limiting their ability to capture a node’s true influence. Recent hybrid centrality approaches attempt to address this by combining multiple local and global attributes; however, they typically integrate features through simple weighting or superposition, failing to characterize the intrinsic synergy among structural properties. Furthermore, they often quantify neighbor contributions too coarsely, overlook the regulatory role of edge strength, and some suffer from high computational complexity, limiting scalability. To overcome these deficiencies, we propose WKDH, a novel influential node identification method based on multi-attribute neighbor contributions. WKDH fuses local structural attributes (degree and H-index) with global structural attributes (k-shell) via a multiplicative weighted synergy model, simultaneously capturing local connection “quantity,” local connection “quality,” and global core-layer position. By transforming neighbors’ comprehensive characteristics into regulated contribution degrees, WKDH mitigates excessive self-attribute interference and accurately reflects the actual propagation potential of edges. Notably, the method achieves linear computational complexity of $O\left(m\right)$. Experimental results on nine real-world and six artificial networks demonstrate that WKDH outperforms nine established indicators in terms of node influence ranking, identification of high-influence nodes, and measuring propagation capability. Moreover, WKDH exhibits strong universality across diverse network structures, as it operates without parameter tuning. Full article

Industrial defect generation has long been challenged by the scarcity of real anomaly samples and the imbalance of defect categories, particularly in complex industrial scenarios involving transparent containers. Taking vials as an example, glass reflection, specular highlights, and fine-grained defects make continuous defect acquisition difficult, thereby making the realism and controllability of augmented samples critical to downstream detection performance. Although existing diffusion-based generation methods can improve synthetic image quality, they often require additional training or lightweight fine-tuning, which limits their efficiency in sample-limited industrial scenarios. To address this issue, this paper builds upon the TF-IDG framework and proposes a training-free industrial defect generation method based on multi-domain consistency and geometric-semantic constraints. To alleviate the unnatural texture details, boundary transitions, and background blending commonly observed in generated defects, a multi-domain consistency constraint is introduced to enhance generation realism from both frequency-domain structures and cross-domain contextual representations, thereby improving anomaly texture expression and overall visual coherence. To further mitigate unstable defect contours, spatial deviation, and structural mismatch with target objects, a geometric-semantic constraint is designed to regulate the generation process through elastic shape constraints and semantic region-anchored attention, enhancing the rationality of defect morphology evolution and spatial localization. Experimental results on both the MVTec AD dataset and a self-built vial defect dataset demonstrate that the proposed method outperforms comparative approaches. Specifically, when YOLOv11 is used as the downstream detector, the mAP@50 on the MVTec AD dataset and the self-built vial defect dataset is improved from 88.5% and 98.0% for the TF-IDG baseline to 89.6% and 98.8%, respectively. Full article

The Zona Pellucida (ZP) and its structural analogs are evolutionarily ancient extracellular matrix components. These are essential for oocyte protection, species-specific gamete recognition, and prevention of polyspermy across Metazoa. Defined by the conserved ZP-domain—comprising ZP-N and ZP-C subdomains—these glycoproteins self-assemble into fibrillar matrices through tightly regulated polymerization. Mechanisms of the regulated polymerization involve furin cleavage, disulfide bonding, and hydrophobic interactions. Once considered a vertebrate innovation, the canonical ZP-domain—defined by its bipartite ZP-N/ZP-C architecture, eight conserved cysteine residues, and capacity for matrix polymerization—is now recognized as an ancient metazoan extracellular module, with homologs identified in basal lineages including Porifera, Cnidaria, and Placozoa. While ZP-like sequences have been reported in choanoflagellates such as Salpingoeca rosetta, these lack the complete canonical features and are considered distant structural relatives rather than true ZP-modules. There they function in cell adhesion and tissue integrity, suggesting an origin predating the evolution of specialized reproductive coats. Previous phylogenetic analyses across 97 metazoan species have revealed that vertebrate ZP genes arose from ancestral duplications of the canonical ZP-module. Accordingly, they give rise to eight subfamilies (ZP1–ZP4, ZPD, ZPAX, ZPX, ZPY), with lineage-specific expansions, losses, and pseudogenization reflecting adaptations to diverse reproductive strategies. Positive selection in sperm-binding regions of ZP2 and ZP3 drives a rapid adaptive evolution. It underscores coevolutionary arms races with sperm ligands, contributing to reproductive isolation and speciation. In invertebrates such as abalone and insects, ZP-domain proteins mediate analogous functions through lineage-specific elaborations, including tandem repeats and domain shuffling. Post-translational modifications, particularly glycosylation, fine-tune sperm receptor specificity and matrix stability. The functional transition from a general protective barrier in early metazoans to a sophisticated gamete recognition interface in vertebrates exemplifies modular evolution. This synthesis highlights the domain-level deep homology of ZP-domain proteins as a foundational element of metazoan extracellular matrices, repurposed through gene duplication, neofunctionalization, and selection to meet the demands of evolving reproductive modes. These insights bridge evolutionary biology, reproductive medicine, and developmental genetics. However, major gaps remain, including unresolved orthology between vertebrate and invertebrate ZP genes, the relative contribution of glycans versus protein backbone in sperm recognition, and the lack of functional evidence for canonical ZP-domain proteins in insects. Future studies integrating glycoproteomics, single-cell transcriptomics, and CRISPR-based models are needed to resolve these questions. Full article

Pancreatic ductal adenocarcinoma is highly malignant with poor prognosis. Its dense tumor microenvironment severely limits the efficacy of conventional chemotherapy and causes severe side-effects. Herein, we adopt the established Schiff-base crosslinked thermoresponsive injectable self-healing poly(2-(2-methoxyethoxy)ethyl methacrylate-co-oligo(ethylene glycol) methyl ether methacrylate-co-aldehyde 2-hydroxyethyl methacrylate)/carboxymethyl chitosan (APMOH/CMCS) hydrogel as the delivery scaffold. By regulating monomer composition, the volume phase transition temperature (T_VPT) of the hydrogel was tuned to around 43 °C to match the therapeutic temperature requirement. Subsequently, copper–metal organic framework (Cu-MOF) nanoparticles co-loaded with 2,2′-azobis(2-methylimidazoline) dihydrochloride (AIPH) and 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) cationic radicals (ABTS·⁺) (denoted as AB@Cu-MOF) were uniformly incorporated into the hydrogel network. Under near-infrared (NIR) irradiation, ABTS·⁺ acts as a photothermal agent to generate hyperthermia for tumor ablation; the elevated temperature further activates AIPH to produce alkyl radicals, which can oxidize inactivated ABTS back to ABTS·⁺ and construct a sustainable photothermal therapy–thermodynamic therapy (PTT-TDT) circulation. Meanwhile, Cu-MOF can consume intracellular glutathione (GSH) to protect active components from deactivation and initiate chemodynamic therapy (CDT) via Fenton-like reactions to produce toxic reactive oxygen species. Benefiting from the thermoresponsive characteristic, the hydrogel undergoes volume shrinkage upon heating, achieving NIR-triggered on-demand drug release with a cumulative release rate of 81.1%. In vitro and in vivo experiments verified that this integrated platform realizes remarkable triple synergistic efficacy of PTT, TDT, and CDT. The tumor volume of the treatment group was merely 13.3% of the control group, and the system also exhibited excellent biocompatibility. Collectively, it offers a feasible and promising intelligent platform for precise local treatment of pancreatic cancer. Full article

High penetration of distributed photovoltaic (PV) generation has intensified voltage violations and stochastic voltage fluctuations in distribution networks, while existing voltage–var control methods still have limitations in terms of communication dependence, scalability, and edge deployment. To address these issues, this paper proposes a stability-constrained voltage–var self-optimizing control method for distribution networks based on the Bandit-Guided Online Self-Tuning Group Relative Policy Optimization (BOST-GRPO) algorithm. First, based on the LinDistFlow linearized power-flow model, a communication-free, decentralized, and locally observable reinforcement learning control environment is constructed, enabling each node to independently generate reactive power regulation commands using only local voltage measurements. Second, a contraction-mapping-based stability constraint is embedded into the policy output layer, theoretically guaranteeing the local exponential convergence of nodal voltage deviations around the equilibrium point and reducing the risk of voltage instability caused by overly aggressive policy actions. Meanwhile, device capacity constraints are incorporated into the policy output through a tanh-based action mapping, ensuring the physical feasibility of control commands. On this basis, BOST-GRPO realizes the online self-tuning of key hyperparameters within a single training process through a Bandit-guided mechanism, thereby avoiding the repeated training overhead caused by traditional offline hyperparameter tuning. Simulation results on the IEEE 33-bus system show that the proposed method outperforms benchmark reinforcement learning algorithms in final test cost, voltage deviation suppression, steady-state error, and regulation speed. Further tests under sensitivity matrix mismatch, different initial voltage disturbance intensities, and the extended IEEE 69-bus system demonstrate that the proposed method achieves good robustness and scalability. Full article

Building fire safety inspection is a knowledge-intensive engineering task that requires reliable hazard recognition under complex visual conditions, limited labeled data, and strict regulatory accountability. To address these challenges, this paper proposes a coarse-to-fine intelligent inspection framework for building fire hazard recognition and regulation-grounded reporting. The framework first performs binary hazard screening and then refines positive or uncertain cases into specific hazard categories, thereby aligning the inference process with practical inspection workflows. A self-supervised DINOv2 Vision Transformer is adopted as the visual backbone, and a small-sample adaptation strategy is developed by combining staged fine-tuning, a lightweight SE-based classification head, and task-aligned knowledge distillation. In addition, an Agentic RAG compliance layer is introduced to retrieve, verify, and present clause-level regulatory evidence while suppressing hallucinated or unverifiable citations. Experiments on a real-world building fire hazard image dataset show that the proposed framework achieves stable recognition performance, outperforms representative CNN-, supervised Transformer-, and self-supervised Transformer-based baselines, and improves the faithfulness of regulation-grounded reporting. The results suggest that the proposed framework provides a feasible prototype-level pathway toward intelligent and auditable fire safety inspection, while broader multi-site validation and robustness evaluation remain necessary for future deployment. Full article

Ensuring fairness in machine learning while maintaining predictive performance remains a fundamental challenge in data science. Most fairness-aware learning approaches rely on fixed penalty scalarization or static multi-objective formulations, which often lead to unstable trade-offs and sensitivity to manually tuned hyperparameters. In this paper, we propose SAFEA (Self-Adaptive Fairness Entropy Algorithm), a novel evolutionary optimization framework that dynamically regulates the fairness–accuracy trade-off using inequality-aware feedback mechanisms. SAFEA introduces two complementary measures: the Fairness Entropy Index (FEI), which captures the dispersion of group-level fairness violations, and the Gini Fairness Index, which quantifies disparity in prediction errors across protected groups. These measures guide an adaptive penalty update rule that autonomously adjusts the fairness coefficient during the evolutionary search process, eliminating the need for manual tuning. Theoretical analysis establishes boundedness and stability of the adaptive penalty under mild assumptions and discusses convergence properties under Lipschitz-continuous objectives. Experimental evaluation on benchmark datasets (Adult Income, COMPAS, and German Credit) demonstrates that SAFEA improves hypervolume by up to 12.4% compared to NSGA-II fairness formulations, reduces demographic parity difference by 18–25% relative to static penalty evolutionary methods, and achieves up to 3.1% higher F1-score than adversarial debiasing approaches while maintaining competitive accuracy. These results indicate that entropy-guided adaptive regulation leads to smoother fairness convergence and better Pareto front coverage. The proposed framework bridges inequality theory and evolutionary multi-objective optimization, providing a scalable and effective solution for fairness-aware learning in high-stakes applications. Full article

Light-responsive hydrogel-based oscillators typically exhibit small oscillation amplitudes because solvent diffusion is intrinsically slow, and their dependence on external periodic light modulation further results in limited amplitude, poor stability, and insufficient autonomy. Inspired by the trigger and sliding mechanism of the ancient crossbow, this study introduces an innovative system that integrates a sliding-block mechanism with time-delay feedback, breaking from conventional approaches that rely on hydrogel inertia or external modulation, within a purely theoretical and simulation-based framework. By establishing a nonlinear dynamic model coupling solvent diffusion, photoisomerization, and optical attenuation, this research shows through numerical simulations that the system can exhibit two distinct modes under constant illumination: a stable state and a self-sustained oscillatory state. The model predicts that the oscillation frequency can be flexibly tuned by varying key parameters, including the crosslinking density, Flory–Huggins interaction parameters of the spiropyran and hydrophilic polymer, ring-opening reaction rate, light intensity, fraction of light-sensitive molecules, and sliding displacement, whereas the initial absorption coefficient has only a minor influence. The slider displacement is also identified as an effective means to regulate the oscillation amplitude. Furthermore, the expansion force at the container bottom is predicted to oscillate synchronously with the hydrogel’s volume change. This theoretical framework represents a paradigm shift from “static small deformation” to “dynamic large-amplitude oscillation”, significantly enhancing the mechanical responsiveness of the material. This work provides a novel and controllable strategy for the conceptual design of autonomous light-driven micromechanical systems. Full article

Symbiotic nitrogen fixation (SNF) can provide a sustainable and self-sufficient nitrogen (N) source for plants. Since its discovery, SNF has remained a central focus of both breeders and fundamental researchers. For decades, extending the utility of SNF to broader agricultural systems has been considered a promising strategy to reduce reliance on synthetic N fertilizers, thereby lowering production costs and mitigating environmental pollution caused by N overuse. This review summarizes recent advances in understanding the molecular and regulatory mechanisms governing SNF in legume plants and highlights emerging strategies to optimize and extend its application in agricultural systems. Particular emphasis is placed on approaches that aim to achieve dominant, fine-tuned, and controllable regulation of N fixation to support sustainable crop production. Full article

Cone-beam computed tomography (CBCT) offers advantages of low radiation dose and rapid acquisition but suffers from scatter-induced shading artifacts that limit diagnostic value compared to multi-detector CT (MDCT). While CycleGAN enables unpaired image translation, its uniform loss application struggles with localized artifact removal. We propose a two-stage learning framework with YOLO-based region correction loss. Stage 1 trains a standard CycleGAN to establish stable CBCT-MDCT domain mapping. Stage 2 fine-tunes the model by applying gradient magnitude minimization loss selectively to artifact regions detected by a pretrained YOLO detector, enabling focused correction while preserving anatomical structures. Using 11,000 2D CBCT slices from 17 patients (14 training, 3 testing) and 23,500 2D MDCT slices from 50 patients, our method achieves a 14.0% reduction in artifact score compared to baseline CycleGAN while maintaining high structural similarity (SSIM > 0.96). Independent evaluation using integral nonuniformity (INU) and shading index (SI) confirms consistent improvement across physics-based metrics. The self-regulating mechanism, where YOLO detection confidence naturally decreases as artifacts diminish, provides automatic adjustment without manual intervention. This work demonstrates that combining staged learning with object detection offers an effective solution for localized artifact removal in medical image translation, potentially improving diagnostic accuracy while preserving the low-dose benefits of CBCT. Full article

Experimental results suggest a feasible strategy for tuning the superconducting properties of MgB₂ through the incorporation of an electroluminescent inhomogeneous phase. By introducing GaP electroluminescent inhomogeneous phases into MgB₂, the effects of emission intensity variation on the sample structure, superconducting transition temperature, electrical transport behavior, and magnetic properties were systematically investigated. The results show that, at a fixed GaP addition level, the superconducting transition temperature T_c increases steadily from 38.2 K to 39.6 K with increasing emission intensity of the inhomogeneous phase, corresponding to a maximum enhancement of approximately 1.4 K. Meanwhile, the zero-resistance temperature shifts upward synchronously, indicating that the entire superconducting transition region moves toward higher temperatures. Raman measurements show that the peak position and linewidth of the E_2g phonon mode evolve systematically with emission intensity, while the electron–phonon coupling parameter λ exhibits a trend consistent with that of T_c. In addition, the nanoscale dispersed distribution of the GaP inhomogeneous phase, together with the interface/defect structures it introduces, appears to promote sample densification and enhance flux pinning, resulting in an increase in the critical current density J_c by approximately 69% at 20 K in self-field and an enhancement of the irreversibility field H_irr by about 31.5%. These results suggest that, beyond the effect of static inhomogeneous-phase incorporation, the luminescence-activated state under bias excitation is likely involved in modulating the superconducting response of MgB₂. This work provides a new experimental perspective for synergistically regulating the properties of conventional superconductors through the combined effects of inhomogeneous phases and excited states. Full article

Active rotors with trailing-edge flaps (TEFs) are a promising method applied to the main-rotor blades of the helicopter for vibration suppression. For active rotors, both the TEF actuator and the corresponding deflection control law determine their overall performance and effectiveness for vibration reduction. In this study, a double-acting piezoelectric actuator is designed to actuate the TEFs, where bidirectional push/pull output is achieved by two groups of piezoelectric stacks operating in opposite directions. Benchtop tests indicate that the TEF deflection angle of the active rotor equipped with this actuator can reach ±4.3°. Subsequently, based on the controlled autoregressive moving average (CARMA) model, an adaptive controller is developed to reduce vibrations in the active rotor by using a minimum variance direct self-tuning regulator (MVSTDR). Finally, an unmanned helicopter is retrofitted with the active rotor, and vibration control experiments are performed under tethered hover conditions with vertical cabin vibration as the control target. Experimental results demonstrate the effectiveness of the designed actuator and the MVSTDR for vibration reduction on the helicopter equipped with an active rotor, which also validates the feasibility of active rotors for practical engineering applications in helicopter vibration control. Full article

This paper presents a hybrid control strategy for robust voltage regulation of a DC–DC boost converter used in a renewable-rich DC microgrid. The DC microgrid may comprise batteries, photovoltaic, and wind energy sources connected to a common DC bus, where voltage fluctuations arise due to variable generation and dynamic load profiles. To ensure optimal and efficient output voltage regulation under these conditions, a novel Linear Quadratic Regulator (LQR) driven self-regulating Sliding Mode Control (SMC) approach is developed. The proposed scheme is realized by combining the optimal performance of an LQR voltage-reference tracking controller with the robustness of a tangent-hyperbolic-based-sliding-mode reaching law defined over an LQR-driven sliding surface. To reduce chattering and improve adaptability to bounded disturbances, the waveform of the hyperbolic switching function in the reaching law is adaptively modulated via an online indirect supervised learning law. The control parameters are tuned offline using numerical optimization. Simulation results under different scenarios, including input voltage disturbances, load variations, and model uncertainties, show that the proposed method achieves superior voltage regulation, reduced chattering, and enhanced dynamic response compared to conventional controllers. The framework ensures reliable EV integration into intelligent DC microgrids. Full article