Search Results (10,057)

The rapid proliferation of resident space objects has made space situational awareness critically dependent on accurate characterization of non-cooperative targets using photometric light curves. This review provides a comprehensive examination of data-driven approaches for space object photometric prediction, synthesizing research across optical scattering characterization, shape and attitude inversion methodologies, and intelligent analysis techniques based on machine learning and deep learning. The evolution from traditional physics-based models to contemporary data-driven paradigms is systematically analyzed, revealing fundamental trade-offs between physical interpretability, computational efficiency, and predictive accuracy. Key findings indicate that while physical bidirectional reflectance distribution function (BRDF) models provide rigorous foundations, their computational demands and prior knowledge requirements limit operational applicability; conversely, deep learning has demonstrated superior predictive accuracy in existing comparative studies, although this conclusion is qualified by the absence of standardized public benchmarks, and it also suffers from interpretability deficits and simulation-to-reality generalization gaps. Critical research gaps are identified, including the absence of public benchmark datasets, inadequate handling of temporal multi-scale phenomena, and the persistent challenge of bridging simulated and real-world observations. Future directions should pursue physics-guided machine learning frameworks that integrate domain knowledge with data-driven capabilities, develop explainable artificial intelligence techniques tailored for photometric analysis, and establish standardized evaluation protocols to advance next-generation space object characterization essential for collision avoidance and space traffic management. Full article

Wood coatings play a critical role in protecting wood substrates from environmental degradation, particularly ultraviolet (UV)-induced photodegradation. This review comprehensively examines the mechanisms of wood coating photodegradation, the factors influencing their durability, and current anti-aging strategies. Photodegradation arises from polymer chain scission, chemical structure reorganization, and photo-oxidation of lignin and cellulose, leading to coating chalking, cracking, gloss loss, and color changes, ultimately compromising wood mechanical properties and service life. Key anti-aging strategies include UV absorbers, which convert harmful UV radiation into heat; hindered amine light stabilizers (HALSs) that capture free radicals and quench excited-state molecules; barrier and shielding materials that form dense physical or nanostructured networks to block UV penetration and enhance mechanical and water resistance; and antioxidants that neutralize free radicals or decompose peroxides at the molecular level. Each approach can be employed individually or synergistically to enhance coating durability. Challenges remain in achieving long-term outdoor stability, balancing transparency and UV shielding, optimizing nanoparticle dispersion, and maintaining the activity of natural antioxidants. Future research should focus on multifunctional composite coatings integrating bio-based materials and nanotechnology, smart responsive systems, adaptive protection mechanisms, and standardized long-term evaluation protocols. These advancements will facilitate the development of high-performance, sustainable wood coatings and promote the value-added utilization of wood resources. Full article

Net surface shortwave radiation (NSSR) is a key parameter in the Earth’s energy cycle, greatly affecting global water and heat balance. Currently, a comprehensive comparative analysis regarding the accuracy of different models remains severely lacking, and there is also a notable deficiency in the systematic exploration of seasonal radiative drivers. Therefore, we developed a machine learning-based seasonal NSSR estimation model. By integrating in-situ observational data with multi-source remote sensing datasets, we achieved precise quantification of radiative fluxes. This proposed model framework employed three cutting-edge algorithms, namely Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), to capture the non-linear interactions among radiative drivers across the four seasons. Through mechanistic sensitivity analysis, we quantified the impacts of key variables on NSSR prediction. The results unequivocally demonstrated that the RF algorithm demonstrated the best performance. Its seasonal R² were 0.95 (spring), 0.89 (summer), 0.95 (autumn), and 0.96 (winter). The Solar Zenith Angle (SZA) dominated in spring and winter; its absence reduced R² by 0.23 and raised RMSE by 20.66–26.42 W/m². Meteorological factors mattered most in summer; excluding them cut R² by 0.17 and hiked RMSE by 23.82 W/m². This study provides actionable insights for terrestrial radiation budget research. Full article

Mountainous megacities face a distinctive form of pluvial waterlogging in which terrain-controlled flow convergence, accelerating imperviousness, and aging drainage interact to produce chronic, spatially clustered failures rather than stochastic events. Existing frameworks, such as hydrodynamic modeling, data-driven machine learning, and multi-criteria composite indexing, carry distinctive failure modes at the municipal scale. This study develops and externally validates a city-wide, grid-based assessment framework for Chongqing, China, through three integrated choices. First, resilience is reformulated as a stabilized adaptation-to-risk ratio and subjected to an explicit falsification test against independent waterlogging observations. Second, multi-source hydroclimatic, topographic–hydrologic, land-cover, and service-accessibility indicators are integrated on a 500 m fishnet (22,500 cells) through within-component CRITIC–Entropy weighting and TOPSIS, with robustness diagnosed by a 500-iteration Monte Carlo weight-perturbation analysis. Third, a spatially grouped LightGBM classifier with SHAP interpretation serves both as an independent validation layer and as a mechanistic lens on non-linear driver thresholds. The composite risk surface achieves ROC-AUC values of 0.834 and 0.873 against two independent waterlogging registries, is strongly spatially clustered (Moran’s I = 0.81, p < 0.001), and preserves its ranking under aggressive weight perturbation (Spearman ρ ≥ 0.95 in 95% of scenarios). A counterintuitive finding emerges from the falsification test as resilience yields ROC-AUC below 0.5 on both point sets, indicating that accessibility-based capacity proxies systematically capture urban centrality rather than drainage robustness, like a diagnosable measurement problem affecting the wider resilience-index literature. LightGBM concentrates 88.0% of waterlogging cells within the top 10% of scored grids, and SHAP-derived thresholds align with saturation-ponding, well-drained, and convergence–hotspot regimes of classical hydrology. Together, these results reframe waterlogging assessment in complex terrain from a cartographic exercise into a falsifiable, resource-aware prioritization framework, and clarify why capacity maps and risk maps should be published as complementary instruments of flood governance. Full article

Increasing planting density is a common strategy to raise soybean yield, yet it often intensifies light competition within the canopy, leading to diminishing returns at high densities. Optimizing row spacing to improve canopy light distribution and light use efficiency is therefore key to increasing yield under dense planting. This study examined the combined effects of planting density and row spacing on canopy light interception, distribution dynamics, and yield in a drip-irrigated soybean system. A two-year field experiment (2024–2025) was conducted in Shihezi, Xinjiang, using three density levels (D1: 210,000; D2: 330,000; D3: 450,000 plants ha⁻¹) and two row spacing patterns (RS1: alternating wide–narrow rows of 20 + 55 cm; RS2: uniform 38 cm rows). Results demonstrated that plant density establishes the baseline for yield, while row spacing modulates light utilization and unlocks yield potential under high-density conditions. The RS1 treatment increased SPAD values in upper leaves by 6.06% at the R6 growth stage compared to the RS2 treatment. At the R5 stage, the RS1 treatment increased radiation use efficiency (RUE) by an average of 6.44%. This planting pattern alleviated photosynthetic decline in dense canopies and conferred a distinct yield advantage. The highest yield was achieved under the D2 treatment, which was 8.44% and 6.71% higher than that under the D1 and D3 treatments, respectively. In conclusion, integrating moderate plant density with optimized wide–narrow row spacing improves canopy light capture and utilization, synergistically enhancing yield and resource use efficiency. This approach offers a practical strategy to overcome the yield plateau in high-density soybean production systems. Full article

Cultivated strawberry (Fragaria × ananassa) is an allo-octoploid for which the genetic basis of fruit appearance traits has not been comprehensively elucidated. This study investigated the genetic architecture of fruit color and morphological traits using integrated digital phenotyping and genome-wide association analysis of a core collection of diverse strawberry germplasm maintained for Korean breeding programs. A 108-accession core collection was assembled, genotyped, and phenotyped for 12 fruit quality traits. Population structure analysis identified K = 10 genetic clusters, and a Mantel test confirmed significant genotype–phenotype correspondence (r = 0.38, p < 0.001). Genome-wide association studies (GWAS) using BLINK and MLMM identified 15 significant marker–trait associations across six traits. Pleiotropic loci on chromosomes 15 (4C) and 22 (6B) were consistently associated with fruit lightness (L*) and red channel intensity (R) in both models, and the 6B locus explained approximately 18% of the phenotypic variance for each trait. Gene Ontology enrichment implicated transcriptional regulation, SUMOylation, and plastid-to-chromoplast transition, suggesting that the identified loci influenced fruit coloration through cellular regulatory mechanisms rather than direct pigment biosynthesis. These findings provide a genomic foundation for dual-trait marker-assisted selection targeting light and vividly red fruits for strawberry breeding. Full article

We demonstrate efficient and thermally stable second-harmonic generation (SHG) in x-cut thin-film lithium tantalate (TFLT) ridge waveguides via modal phase matching (MPM). The experimental characterizations reveal a normalized conversion efficiency (NCE) of 17.2% W⁻¹cm⁻² in a 4 mm long waveguide. Notably, the device exhibits a temperature-dependent phase-matching wavelength slope of 0.007 nm/°C, which shows a two-orders-of-magnitude improvement in thermal stability over conventional periodically poled lithium niobate/lithium tantalate optical devices. Our work indicates that MPM in TFLT is an attractive strategy for integrated nonlinear optical applications, particularly for the on-chip frequency conversion of both classical and quantum light signals without on-chip domain-poling processes. Full article

The enrichment of chocolate with healthy beneficial ingredients represents an effective strategy to create functional food with high nutritional and bioactive potential. Comparisons were made between eight treatments derived by the factorial combination of 2 types of butter (milk and cocoa) and 4 concentrations of green barley powder addition (1%, 3%; 5%; and 7%), plus 2 untreated controls (milk butter and cocoa butter with no green barley powder addition), in terms of chemical, colorimetric, physical, antioxidant, mineral and sensory characteristics of white chocolate. Increasing addition of green barley to both milk and cocoa butter led to the decrease in dry matter, soluble solids, pH and fat in the produced chocolate, with the untreated controls always showing the highest values. Opposite trends were recorded for proteins, fiber, ash and mineral substances. The ‘L’, ‘a’ and ‘b’ color components gradually decreased from the untreated control to the highest concentration of barley powder addition both to milk and cocoa butter. The increasing integration of barley powder either into milk or cocoa butter resulted in the gradual decrease in F max compression and F max cutting of the chocolate manufactured, compared to the untreated control. The addition of barley powder to milk and cocoa butter elicited a gradual increase in all the antioxidants analyzed, i.e., vitamin C, carotenes, lycopene and xanthophylls, and of chlorophyll a and b, compared to the untreated control. Vegetal flavor attributes were enhanced by the increasing addition of green barley powder. The latter incorporation into milk and cocoa butter sheds light on the interesting topic of conceiving and applying the manufacture of innovative functional chocolate with high content of fiber, nutrients and antioxidants. Full article

This paper presents a proposed architecture for an artificial intelligence-driven unmanned aerial vehicle (UAV) system intended for tactical intelligence, surveillance, and reconnaissance (ISR) missions. The architecture brings together electro-optical imaging, long-wave infrared sensing, two-dimensional light detection and ranging (LiDAR), inertial navigation support, onboard edge computing, and resilient communication links within a unified system-level framework. Unlike many existing approaches that treat perception, autonomy, communication, and safety as loosely coupled functions, the proposed architecture combines multi-modal sensing, operator-supervised autonomy, and a safety-oriented decision validation layer intended for future integration with Ansys SCADE. The system is structured around operational and sensor-performance requirements used to justify the selection and interaction of the main onboard subsystems. At the architectural level, the proposed framework is intended to support target detection, tracking, environment awareness, and mission-level decision support under degraded visibility, constrained communication, and contested operating conditions. The paper therefore contributes a requirement-driven and safety-aware ISR UAV architecture that provides a scalable basis for future implementation, validation, and multi-UAV extension. Full article

On 28 March 2025, a Mw 7.7 earthquake struck central Myanmar, where rapid mapping of early impacts is crucial for post-earthquake assessment and emergency response. Existing nighttime light studies often emphasize single-scale brightness loss, with limited characterization of azimuthal differences within intensity zones and their coupling with population/building exposure, although these factors are essential for explaining spatially uneven earthquake impacts and for improving the interpretation of nighttime light loss patterns. This study integrates daily VIIRS nighttime lights (500 m) with USGS intensity and population/building density to build an intensity–azimuth framework with six directional sectors, quantify pre-/post-earthquake changes at county, patch, and pixel scales, apply bivariate LISA to detect local coupling patterns, and validate against CEMS Rapid Mapping. The results show clear scale complementarity: county aggregation robustly delineates the macro impact extent but smooths internal contrasts; pixel analysis captures fragmented disturbances yet is noise-sensitive; patch-based mapping best aligns with built-up areas at 500 m resolution and shows higher agreement with CEMS in well-lit urban areas. Azimuth–intensity patterns indicate more concentrated NTL reduction in north–south high-intensity zones (NTL = −0.53–−15.67 nW·cm⁻²·sr⁻¹), with local rebounds in some east–west sectors. The framework provides interpretable support for rapid loss assessment and priority-based resource allocation. Full article

Vision-based navigation systems (VINS) are increasingly utilised as an alternative to GNSS for UAVs operating in urban environments, but they suffer from performance degradation under visual fault conditions like illumination variation, rapid motion, texture-less environments, and weather effects. While hybrid architecture incorporating Kalman filters and machine learning (ML) improves performance, they often lack evidence of providing contingency for non-Gaussian error distributions, limiting operational safety. To address these shortcomings, an enhanced hybrid VINS architecture is proposed, featuring a Bayesian GRU-based error correction network (B-GRU) to provide a contingency while compensating model errors. To the best of the authors’ knowledge, this is the first attempt to estimate uncertainty using a B-GRU compensator while addressing data uncertainty for VINS applications. The system architecture integrates an Error-State Kalman Filter (ESKF) and the B-GRU, compensating for position errors with uncertainty prediction. The proposed approach is validated using datasets from MATLAB incorporated in an Unreal Engine simulated environment, replicating the complex fault conditions. The ML model is trained on various visual failure modes to adapt the variability in the signal patterns during flights with simulated datasets and tested across varied flight paths and lighting scenarios. The results demonstrate that the fusion strategy effectively corrects erroneous measurements arising from corrupted sensor data and imperfect models and achieves an improvement of 78.06% compared to SOTA hybrid VIO on the horizontal axis while capturing complex flight dynamics in an unseen environment. A comparative analysis demonstrates the effectiveness of B-GRU in mitigating failure modes with a predictive error boundary, achieving a 72% improvement in performance compared to the architecture that integrates GRU-based error compensation. This approach shows a step forward in enhancing positioning accuracy and contingency in challenging urban environments. Full article

Background: Early detection of oral potentially malignant disorders (OPMDs) and early-stage oral squamous cell carcinoma (OSCC) remains a major clinical challenge, as initial lesions often present with subtle or nonspecific findings during conventional white-light examination. Narrow-band imaging (NBI) enhances visualization of mucosal microvasculature and may improve the identification of dysplastic and malignant transformation. Methods: A narrative review of the literature was conducted in the PubMed, Scopus and Google Scholar databases. Studies published between January 2012 and January 2025 evaluating clinical applications of NBI in oral mucosal lesions, OPMDs, or OSCC were included. Results: NBI enhances visualization of intraepithelial papillary capillary loops (IPCLs), whose morphological alterations correlate with epithelial dysplasia and malignant transformation. Evidence suggests high diagnostic sensitivity (up to 87–100%) and specificity (approximately 83–96%) for detecting high-grade dysplasia and early OSCC. NBI also improves biopsy site selection, reduces sampling error, and supports surveillance of high-risk patients. Conclusions: NBI represents a valuable adjunctive diagnostic tool in oral medicine and dentistry. Although it does not replace histopathological examination, its integration into clinical assessment may enhance early cancer detection and improve management of patients with OPMDs. Full article

In landslide-prone areas, spatial gaps in InSAR-derived deformation maps caused by incomplete SAR coverage hinder continuous surface deformation assessment and limit reliable landslide analysis. To address this problem, we propose an explainable AI (XAI) framework that integrates SBAS-InSAR, ensemble machine learning, and Shapley Additive exPlanations (SHAP) to estimate surface deformation in SAR-scarce regions. Geological and engineering factors, including protective measures, distance to roads, and land use, were combined with remote sensing and field data to build a comprehensive dataset. Four ensemble models (LightGBM, XGBoost, Random Forest, and CatBoost) were trained and evaluated, with XGBoost achieving the best performance (R² = 0.816, RMSE = 6.85 mm, MAE = 4.27 mm). Validation against two GNSS benchmarks confirmed sub-millimeter accuracy (0.6 mm and 0.3 mm). Both XGBoost and CatBoost delineated continuous deformation patterns consistent with field-observed damage. SHAP analysis provided model interpretability, highlighting elevation and human-engineering factors as key drivers: areas farther from roads and under cultivation were more prone to downslope movement, while damaged protective works exhibited greater deformation. By coupling InSAR with XAI, this study achieves accurate and interpretable surface deformation estimation in data-scarce regions, advancing landslide assessment and early warning applications. Full article

This review examines current research and development directions in thorium-based nuclear fuel cycles and reactor systems, including innovative and modular reactor concepts being investigated in several nuclear-producing countries. The analysis considers the feasibility of integrating thorium-containing fuels into both existing and emerging reactor technologies. Particular attention is paid to the potential use of thorium-based fuels in pressurized water reactors (PWRs) as transitional platforms that can enable gradual introduction in thorium without requiring immediate deployment of entirely new reactor architectures.This study synthesizes representative quantitative results reported in the recent literature, including neutronic performance metrics, conversion ratio estimates, and fuelbehavior characteristics of mixed Th–U oxide fuels under typical operating conditions. These results are evaluated together with broader system-level considerations, such as fuelcycle closure potential, materials performance, and technology readiness across different reactor classes.A comparative assessment of light water reactors (LWRs), heavy water reactors (HWRs), and molten salt reactors (MSRs) demonstrates that each platform offers distinct advantages and limitations for thorium deployment. While LWR systems provide the most realistic near-term pathway for partial thorium introduction within the existing nuclear infrastructure, HWR and MSR concepts offer more favorable conditions for efficient thorium utilization and potential Th–U fuelcycle closure. These reactor classes are currently being explored within national research and development programs focused on advanced and modular nuclear technologies.By integrating neutronic analysis, materials considerations, fuelcycle strategies, and techno-economic factors, this review provides a system-level perspective on the research and development of innovative thorium reactor concepts and outlines realistic pathways for their gradual implementation in evolving nuclear energy systems. Full article

Accurate determination of the optical absorption coefficient, ${\mu }_a$, in turbid media is fundamental to biomedical optics and material characterization. Integrating sphere techniques, which measure total transmittance and reflectance, are a standard method for this purpose. However, the inverse models typically employed rely on the assumption of isotropic light propagation. In fiber-structured materials—a common geometry in biological tissue–this assumption often breaks down, leading to significant quantification errors. In this study, we investigated this effect using Monte Carlo simulations and proof-of-concept experiments on mechanically stretched PTFE tape. The medium was modeled as a slab of aligned dielectric cylinders embedded in an isotropic matrix, and the performance of an isotropic inverse model was compared with that of an anisotropic inverse model. The isotropic model showed substantial systematic errors in ${\mu }_a$, with a mean absolute error (MAE) of 19.3%, typical errors between approximately $-40\%$ and 50%, and outliers reaching up to 300%. In contrast, the matched anisotropic model achieved a MAE of 1.2%. Even when the structural parameters of the anisotropic model were perturbed, the MAE remained low at 1.8% for moderate perturbations and 3.9% for severe perturbations. The simulation results therefore indicate that, for the integrating sphere framework considered here, incorporating anisotropic light propagation can improve absorption retrieval more strongly than precise knowledge of all geometric details. Measurements on stretched PTFE tape showed the same qualitative trend and provide proof-of-concept experimental support for the simulation-based findings. Full article