Search Results (418)

The suri, or lesser rhea (Rhea pennata), is a flightless Andean bird facing multiple anthropogenic threats. Despite these pressures, its reproductive ecology remains poorly documented near its distributional limits. This study provides the first comprehensive evaluation of the breeding ecology, nesting distribution, and threats for wild lesser rhea populations at their northern geographic limit in Moquegua, Peru. Across four consecutive breeding seasons (2021–2024), we combined systematic surveys, kernel density analysis, camera trapping, and micro-behavioral nest monitoring. We identified 14 nesting sites comprising 193 nests: 18 old with eggshells remains, 110 old without eggshells, 53 nests under construction, and 12 actives. Kernel density analysis indicated a total nesting area of 195.8 km², with a low density of 0.15 nests/km². Nests were primarily established on moderately sloping hillsides utilizing high-Andean desert vegetation for camouflage. The reproductive period extended from mid-July to mid-December, displaying marked interannual variation in breeding phenology. Camera trap records and field encounters confirmed nest predation and interactions by a native predator guild composed of mammals (Lycalopex culpaeus, Puma concolor) and raptors, while free-ranging domestic dogs act as localized anthropogenic disturbance factors and linear infrastructure represents potential movement constraints. These findings establish an essential ecological baseline, highlighting that the long-term persistence of this marginal population depends on standardized monitoring to detect subtle demographic shifts and on strategic connectivity conservation across high-Andean landscapes. Full article

In microcavity displays, different device structures can produce nearly identical normal-incidence electroluminescence spectra, making non-destructive identification difficult. This is especially relevant when narrow-band QLED emission is compared with cavity-narrowed OLED emission. Angle-resolved measurements can distinguish these cases, but the need for mechanical rotation limits measurement throughput. Here, we developed a digital twin-assisted method that uses a single normal-incidence spectrum for structural identification. The optical model was parameterized with measured material properties and checked against measured electroluminescence spectra. It was then used to generate spectra with ±1 nm electrode-thickness variations, and measured spectra were also included during training. Four machine-learning classifiers were compared for eight QLED/OLED device structures. The Tanh-activated multilayer perceptron gave the highest testing accuracy of 93.94%, compared with 84.85% for logistic regression. These results show that small differences in the full spectral shape can support structural identification when peak wavelength and linewidth alone are ambiguous. The method provides a practical basis for rotation-free optical screening of microcavity display structures. Full article

Skin pigmentation is influenced by multiple factors, with sun exposure being one of the most important. Therefore, sun protection plays a central role in the prevention and treatment of pigmentation disorders. This work analysed the recent literature (2015–2025) on the influence of sun protection in melasma, post-inflammatory hyperpigmentation, and vitiligo, highlighting therapeutic advances and the impact of sunscreen use in both the prevention and treatment of these conditions. A search was conducted in the PubMed and SCOPUS databases using MeSH terms, selecting original research studies in English and employing various methodologies. Conventional sun protection (UVB/UVA) may not be sufficient on its own to prevent skin pigmentation disorders, given the evidence supporting a role for UVA1 radiation and blue light (380–455 nm) in inducing hyperpigmentation, especially in individuals with phototypes IV-VI. Innovative, broad-spectrum sunscreens, e.g., iron oxide and methoxypropylaminocyclohexenylidene ethoxyethylcyanoacetate, have shown potential for enhancing protection against hyperpigmentation in visible light, while some tinted formulations may also provide cosmetic benefits by camouflaging existing dark spots. The discrepancy between the application doses used compromises observed efficacy, reinforcing the need for public education on photoprotection and for research conducted under real-use conditions. Personalised strategies that combine broad-spectrum and visible-light protection with antioxidant and anti-inflammatory agents may optimise the prevention and management of these disorders. Full article

This paper presents a simulation-based cyber-quantum Internet of Things (IoT) security framework for modeling, securing, and detecting attacks in QKD-secured IoT communication environments. The proposed framework integrates heterogeneous IoT traffic generation, gateway-assisted routing, edge processing, QKD key-pool management, Quantum-Synchronized Ephemeral Encryption (Q-SEE), cross-layer adversarial attack injection, and AI-based multiclass detection. Unlike conventional IoT intrusion datasets that mainly capture packet- or flow-level abnormalities, the generated dataset represents the joint behavior of IoT sessions, network delay, queue pressure, QKD state, key consumption, encryption-mode transitions, ciphertext metadata, and cyber-quantum risk. A Python/SimPy/NetworkX simulation was developed using 80 IoT devices, 3 gateways, 2 edge servers, 4 cyber-quantum control-plane nodes, and 1 adversarial orchestrator. The final simulation produced 46,351 records with 76 features covering normal traffic, five traditional IoT attacks, and six novel cyber-quantum attacks, including QKD key-pool starvation, QBER camouflage, false QKD-health injection, encryption downgrade induction, queue–key coupling, and multi-vector cyber-quantum orchestration. Q-SEE adaptively selects among QKD-OTP, QKD-synchronized AES-256 ephemeral mode, PQC fallback, degraded mode, and blocked mode according to QBER, secret key rate, key availability, device criticality, downgrade pressure, and risk. A leakage-aware Quantum-Aware Kolmogorov–Arnold Network (QKAN) was then trained using deployable cyber-quantum evidence. The final nonrisk QKAN achieved 98.79% test accuracy, 98.61% macro-F1, 98.85% weighted-F1, and 99.78% macro-AUC, demonstrating effective detection of traditional and cyber-quantum IoT attacks. Full article

Smart-city energy platforms—peer-to-peer markets and tokenized renewable energy certificates—settle against metered generation, yet never check the physical plausibility of those claims. For wind, attainable energy is not measured but derived from anemometry through a vertical extrapolation whose exponent—the wind-shear coefficient—is a discretionary modeling choice. Using a five-height, 52,192-record campaign from Phangan Island, Thailand—reproduced here as a statistically anchored reconstruction, the raw archive not being redistributable—we show that the conventional $1/7$ rule understates attainable energy by $29.8\%$, and that an adversary asserting the exponent could inflate a resource claim by $87.4\%$. WindChain sits beneath the smart-city transactive layer rather than beside it: it does not mint certificates from wind data but bounds what a revenue meter may claim. It enforces boundary-layer, kinematic, and thermodynamic admissibility as a consensus predicate and commits wind statistics to a hierarchical Merkle–Weibull accumulator whose 104-byte root lets any verifier recompute the Weibull parameters, power density, and shear exponent in constant time. We prove that an epoch-level admissibility gate bounds over-issuance, and that attestation windows must be thirty-six times longer than independence assumes. On the reconstruction, WindChain detects six of eight manipulation classes—four of them with recall $\ge 0.99$—at a $1.75\%$ false-positive rate; we also report a camouflage regime defeating every per-record test, and a sustained bias at or below $2.6\%$ that the epoch detector does not see. Consensus performance is modeled, not deployed. WindChain narrows the trust boundary rather than removing it: the guarantee is conditional on an independently certified site reference and on physical calibration of the mast and is best read as an auditable plausibility layer beneath settlement rather than as a trustless one. Full article

Polarization–spectral imaging information has theoretical advantages for camouflage-net recognition. However, for specific observation targets, the selection of effective spectral bands and polarization parameters, which are critical issues in engineering applications, remains unclear. Using a newly developed hyperspectral polarization imaging prototype, we conducted systematic outdoor observations of a grassland camouflage net placed over green grass at multiple viewing angles and different times of day. After evaluating and confirming the net’s camouflage effectiveness, we analyzed polarization-parameter spectral images of both the net and background under varying observation and solar altitude angles. The Fisher criterion was used to identify discriminative polarization parameters and spectral ranges. Effective bands were determined as S₁ at 675–696 nm, S₂ at 673–677 nm, S₃ at 607–616 nm and 625–700 nm, DoP at 676–700 nm, and DoLP at 668–680 nm. Grayscale images in these bands further verified the separability of the camouflage net from grass. These results provide experimental evidence and wavelength-parameter references for engineering applications of polarization–spectral imaging in camouflage-net detection. Full article

Background and Objectives: Delayed contour irregularities may become apparent years after otherwise successful autologous rhinoplasty, particularly in patients with thin nasal skin. Thin-skinned rhinoplasty, autologous graft behavior, and nasal aging have been studied individually, but their potential interaction remains unclear. This review examines current evidence on age-related changes in the nasal soft-tissue envelope, thin nasal skin, autologous graft behavior, and their potential effects on long-term rhinoplasty outcomes. Materials and Methods: A structured narrative review was conducted using PubMed as the primary database, with supplementary searches of Google Scholar and reference lists. Four predefined search domains addressed thin nasal skin, nasal aging, autologous graft behavior, and preservation and camouflage techniques. The PubMed searches identified 463 records. After duplicate removal and title and abstract screening, 205 publications were retained for further assessment. Additional relevant publications were identified through supplementary searches. Evidence was synthesized qualitatively because of differences in study design, surgical technique, follow-up duration, and outcome assessment. Results: Thin nasal skin is associated with increased visibility of underlying structural features after rhinoplasty. Aging is associated with changes in skin thickness and elasticity, soft-tissue volume, and the underlying nasal framework. These findings provide a possible basis for changes in soft-tissue camouflage over time, but direct longitudinal evidence linking aging to delayed graft visibility is lacking. Late contour changes may also result from graft warping or resorption, scar remodeling, or changes in the native nasal framework. The ability of preservation and camouflage techniques to prevent aging-related delayed contour visibility has not been established. Conclusions: Age-related changes in the nasal soft-tissue envelope may contribute to delayed contour visibility after rhinoplasty. However, direct longitudinal evidence showing that these changes increase the visibility of an otherwise stable autologous graft is lacking. This relationship should therefore be considered a hypothesis rather than an established causal mechanism. Prospective longitudinal studies using objective measurements of soft-tissue thickness, graft morphology, and external nasal contour are needed to test this hypothesis. Full article

In natural scenes, the high resemblance between unripe tomatoes and foliage, combined with severe occlusion, challenges current semantic segmentation models, causing poor accuracy and indistinct boundaries. To overcome this, we propose CMAE-UNet, a camouflage suppression and edge enhancement model based on the UNet architecture. Specifically, a Global-Local Integrated Spatial Attention (GLISA) encoder merges dual-branch dilated convolutions, residual structures, and an Efficient Multi-scale Attention mechanism to expand receptive fields and highlight targets in complex backgrounds. Furthermore, a Frequency-Domain Feature Enhancement (FFE) module leverages the Fast Fourier Transform to separate and adaptively enhance distinct frequency components, effectively mitigating camouflage interference. Additionally, a Directional Edge Enhancement (DEE) module uses three-directional learnable convolutions and spatial attention to sharpen indistinct target contours. Evaluated on a custom Tomato dataset encompassing five complex scenarios, CMAE-UNet outperforms 12 prominent methods in mIoU, Dice, and Sen metrics, yielding smoother and more precise segmentation boundaries. The model robustly withstands field interference, providing strong technological support for automated tomato detection, intelligent harvesting, and growth monitoring. Full article

TDP-43 proteinopathies, encompassing amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD), and limbic-predominant age-related TDP-43 encephalopathy (LATE), represent a heterogeneous spectrum of devastating neurodegenerative disorders. For decades, the diverse clinical presentations of these diseases have complicated antemortem diagnosis and hindered the development of disease-modifying therapies. However, recent breakthroughs in basic science are beginning to address these clinical barriers, although substantial hurdles to practical clinical application remain. Structural elucidation via cryo-electron microscopy (Cryo-EM) has shattered the single-protein amyloid dogma by revealing that TDP-43 can form hetero-amyloid filaments with ANXA11, thereby providing a molecular basis for pathological strain diversity. Concurrently, the pathogenic focus has shifted toward nuclear loss of function, which triggers a systemic “RNA crisis” characterized by aberrant alternative polyadenylation (APA) and cryptic exon inclusion (e.g., STMN2, UNC13A). Crucially, this metabolic collapse is profoundly exacerbated by patient-specific genetic risk factors, acting synergistically in a “two-hit” model of neurodegeneration. To translate these findings to the clinic, next-generation diagnostic tools are emerging. Integrating neuron-derived extracellular vesicle (EV) isolation with Seed Amplification Assays (SAAs) holds promise to help overcome the structural camouflage that limits current PET imaging, potentially offering ultra-sensitive, functional strain identification in biofluids. While these structural and diagnostic milestones provide a strong foundation for precision medicine, major challenges in assay standardization and clinical validation must be addressed. Advanced therapeutic strategies—namely, splice-switching antisense oligonucleotides (ASOs) that directly restore RNA metabolism, combined with the targeted suppression of neuronal hyperexcitability—are now entering clinical trials. This review synthesizes how decoding the structural and RNA-metabolic complexities of TDP-43 is paving a promising pathway from bench to bedside, while critically discussing current translational limitations. Full article

Plumage anomalies in owls range from common color polymorphisms to rare, extreme aberrations such as leucism, albinism, and melanism, yet systematic syntheses across taxa are scarce. In this review, we collate recent genomic, ecological, and environmental research (2017–2025) on owl plumage anomalies and combine it with new field observations from Israel to examine how depigmentation and hyperpigmentation arise and what their ecological consequences may be. We highlight the exceptional rarity of familial leucism by presenting a brood of Eurasian Eagle Owls (Bubo bubo) from Mount Gilboa containing two Ino and one normally colored fledgling—the first family-level record in this species and only the second wild case of extreme depigmentation in Eurasian Eagle Owls following a leucistic fledgling reported in Spain. We complement this with another sporadic color anomaly in a Little Owl (Athene noctua) from the Galilee and one from Shoham, Central Israel, both exhibiting unusually dark plumage with reduced pale spotting. Together, these records illustrate that owls may show pronounced reductions or alterations in melanin-based plumage coloration. However, the underlying mechanisms—including possible melanin-synthesis defects, altered melanin deposition, and external staining—cannot always be determined from photographs or opportunistic observations alone. By integrating these examples with broader evidence on climate, habitat, diet, and urbanization as drivers of plumage variation, we argue that rare pigment anomalies, although individually exceptional, provide disproportionate insight into camouflage, signaling, fitness, and potential bioindicator roles in owl populations facing rapid environmental change. Full article

Physical-domain adversarial attacks have been extensively studied in face recognition and object detection, yet the field of palmprint recognition remains largely unexplored. Existing methods generate grayscale patches constrained by the single-channel input of most palmprint models. When deployed on skin, these patches contrast sharply with the surrounding tissue and are readily noticeable to human observers, undermining the covertness required in practical attacks. To address this limitation, we propose a Color Adversarial Patch (CAP) generation algorithm that leverages style transfer principles to produce visually natural color patches while maintaining high attack success rates. The method initiates the patch with a style prior using a pre-trained Contrastive Arbitrary Style Transfer (CAST) model and jointly optimizes adversarial loss, style loss, and smoothness loss within a unified framework. A three-channel averaging strategy is adopted to ensure compatibility with single-channel recognition models during gradient backpropagation. Experiments on the Tongji palmprint dataset show that the generated color patches achieve average cosine similarity values above the decision threshold in physical-domain tests, with peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values significantly higher than those for their grayscale counterparts. Ablation studies validate the indispensable role of each loss component. CAP offers a practical balance between attack effectiveness and visual camouflage, demonstrating the feasibility of concealed physical-domain attacks on palmprint recognition systems. Full article

Land-based hyperspectral imaging provides high spatial and spectral resolution for detecting camouflaged targets, but practical deployment remains limited by strong target background spectral similarity, scarce annotated hyperspectral samples, and the computational cost of full-band processing. To address these issues, this paper proposes HCTDNet (Hyperspectral Camouflaged Target Detection Network), a land-based hyperspectral image analysis framework. The method first employs band extraction for data dimensionality reduction, compressing multi-channel hyperspectral images into 3-channel virtual RGB representations, which reduces spectral redundancy while preliminarily enhancing camouflaged target saliency. A pre-trained RGB camouflaged target detector is then adopted as the backbone model, with its parameters frozen to maintain stability, while trainable modality-specific prompts are learned to improve training efficiency. Finally, model fine-tuning is performed using a self-constructed camouflaged target dataset to enhance robustness in detecting camouflaged targets within virtual RGB images. During inference, preprocessed hyperspectral images are fed into the model to generate detection results for camouflaged target regions. The experiments performed on our self-collected land-based hyperspectral dataset with camouflaged targets reveal that HCTDNet achieves superior detection performance compared with seven classical hyperspectral target detection methods while maintaining an average inference speed of approximately 16 FPS. The proposed framework provides an efficient and near-real-time applicable solution for land-based hyperspectral camouflaged target detection, showing significant practical potential. Full article

Background: Hepatocellular carcinoma (HCC) remains challenging to treat because of the limited therapeutic efficacy and insufficient selectivity of conventional therapies. To overcome these limitations, multifunctional nanoplatforms integrating biomimetic strategies and combination therapy have attracted increasing attention. Single-modality therapies are often limited by insufficient therapeutic efficacy and restricted mechanisms of action, highlighting the need for biomimetic nanoplatforms that integrate combination therapeutic strategies for enhanced antitumor performance. Methods: Herein, a biomimetic strategy-based nanoplatform (DI-ZM) was constructed via a combination of ZIF-8 biomineralization, physical adsorption of dihydroartemisinin (DHA) and indocyanine green (ICG), followed by HepG2 cell membrane coating to achieve homologous interaction. This design enables integrated chemotherapy, photothermal therapy (PTT), and photodynamic therapy (PDT) within a single system. Results: The resulting DI-ZM nanoparticles exhibited a hydrodynamic diameter of approximately ~200 nm with good colloidal stability and high drug-loading capacity. Under 808 nm laser irradiation, DI-ZM achieved a temperature elevation to ~66 °C within 5 min, together with efficient ROS generation. Compared with uncoated nanoparticles, the biomimetic membrane coating significantly enhanced cellular uptake and homologous targeting ability, as confirmed by CLSM and flow cytometry analysis. Benefiting from the biomimetic membrane coating, DI-ZM further exhibited improved homologous targeting and cellular uptake, which contributed to enhanced intracellular ROS generation. This was accompanied by significant mitochondrial membrane depolarization and apoptosis rates exceeding 80% in HepG2 cells under laser irradiation, ultimately resulting in markedly enhanced cytotoxicity. In addition, the biomimetic membrane coating also enabled efficient penetration of DI-ZM into multicellular tumor spheroids, indicating its improved tumor-penetration capability. In vivo antitumor studies further revealed effective tumor suppression with a tumor inhibition rate of approximately 97%, along with acceptable systemic tolerance in HepG2 tumor-bearing mice. Conclusion: The biomimetic membrane-coated ZIF-8 nanoplatform integrating chemotherapy with ICG-mediated phototherapy (photothermal and photodynamic therapy) provides an effective strategy for the combination therapy against HCC. Full article

Musk deer are forest-dwelling artiodactyls of high conservation value, but their wild populations remain under severe conservation pressure due to poaching driven by the demand for natural musk, together with habitat fragmentation and habitat loss. Efficient non-invasive image-based monitoring is therefore important for musk deer conservation; however, fine-grained detection of musk deer and visually similar artiodactyls in ecological images remains difficult because of background camouflage, vegetation occlusion, and high inter-class similarity. In this study, a fine-grained wildlife image dataset was constructed, and an RT-DETR-based framework, termed DSCF-DET, was proposed for automated detection in complex natural scenes. DSCF-DET integrates three task-oriented modules: DRPBlock for receptive-field-aware feature extraction, SASTE for sparse spatial encoding, and CBAFusion for cross-level feature fusion. On the constructed dataset, DSCF-DET achieved 91.4% precision, 86.2% recall, 88.7% F1-score, and 86.4% mAP50. Compared with RT-DETR-r18, it improved these metrics by 8.6, 12.1, 10.5, and 12.4 percentage points, respectively, while maintaining moderate model complexity. Visualization results showed more target-focused feature responses and reduced background-related activations. Cross-dataset experiments on an independent public wildlife dataset further suggested potential applicability to broader wildlife detection scenarios. These results indicate that DSCF-DET provides a computationally balanced approach for ecological image screening and intelligent musk deer monitoring. Full article

The detection of cracks in concrete bridges is essential for evaluating structural durability and load-carrying capacity. Although vision-based crack detection methods have been widely studied, the detection of fine cracks remains challenging. This study proposes a method for detecting fine surface cracks in bridges based on close-range unmanned aerial vehicle (UAV) photography and a camouflaged object detection network. The main contributions are as follows: 1. The feasibility of using a UAV equipped with a telephoto camera to capture fine cracks was investigated, and close-range imaging was shown to enable the acquisition of fine cracks as narrow as 0.08 mm. 2. To address the difficulty of accurately identifying cracks on concrete bridge surfaces due to stain interference and the weak texture features of fine cracks, a crack segmentation method based on a boundary-guided camouflaged object detection network was applied and validated. By improving the contextual aggregation module, the method achieved accurate identification of fine cracks under stain interference. 3. To overcome the difficulty of accurately measuring crack width when fine cracks occupy only a small number of pixels, a deep learning-based super-resolution method was applied to achieve sub-pixel-level crack width measurement. The proposed method was tested and validated on an in-service concrete bridge. The test results show that the proposed method achieved a mean absolute error of 0.7% in crack segmentation and a width measurement error of less than 0.06 mm, demonstrating its practical applicability. Full article