Search Results (4,319)

Accurate estimation of crop yield from remote sensing remains challenging due to the crop-specific nature of yield drivers and the difficulty of interpreting spectral indicators across agronomic systems. While many studies prioritise predictive accuracy through complex models, fewer explicitly examine the stability and physiological relevance of individual spectral and phenological indicators under controlled analytical conditions. This study investigates yield–spectral relationships in wheat and cotton using a unified Sentinel-2 indicator framework applied across multiple growing seasons in a Mediterranean agricultural environment. A consistent set of spectral and thermal indicators was derived from two phenologically targeted Sentinel-2 acquisitions per season and analysed using correlation analysis, univariate regression, constrained multivariate modelling, and recurrence analysis within an identical workflow for both crops. Distinct crop-specific patterns were observed. Wheat yield was most strongly associated with water-sensitive and canopy-related indicators, with NDWI-based metrics reaching Pearson correlations up to r = 0.85 and multivariate models explaining a substantial proportion of yield variability (up to R² ≈ 0.70) under controlled analytical conditions. In contrast, cotton yield variability was dominated by thermal accumulation, with growing degree day indicators showing correlations up to |r| = 0.59 and multivariate performance reaching R² = 0.74. Recurrence analysis indicated consistent recurrence of these indicator families across analytical stages under the examined conditions. Overall, the results indicate that parsimonious, physiologically interpretable indicator combinations can account for a meaningful proportion of yield variability without reliance on highly complex or high-dimensional modelling approaches, supporting crop-aware indicator selection for precision agriculture applications. Full article

Hereditary damping and fractional attenuation are widely used to model wave propagation in complex media, but the variational and spectral origin of the corresponding nonlocal-in-time operators is often left implicit. In this work, we derive such operators from a minimal conservative field–reservoir model. A real scalar field is coupled locally to a continuum of harmonic reservoir modes, which are then eliminated exactly. The resulting reduced dynamics is a causal wave equation with a memory-friction term acting on the field velocity. The memory kernel is generated by the reservoir coupling spectrum through a cosine-transform relation, establishing a direct spectrum-to-kernel correspondence. This relation provides both a physical interpretation of hereditary damping and a practical admissibility criterion: macroscopic attenuation and dispersion arise from the delayed back-action of unresolved internal modes, while physically admissible kernels are constrained by the non-negativity of the underlying spectral density. The framework unifies several standard damping regimes. A broadband reservoir recovers the Markovian locally damped wave equation, reservoirs with a finite characteristic time generate finite-memory relaxation and frequency-dependent dispersion, and scale-free reservoir spectra produce power-law memory kernels. In the latter case, the hereditary damping operator reduces to a Caputo-type fractional derivative, showing that fractional wave attenuation can emerge as an effective reduced dynamics rather than being postulated phenomenologically. We further analyze dispersion, attenuation, causality, stability, and admissibility conditions in terms of the reservoir spectrum. The main contribution of the work is therefore to provide a variational and spectral derivation of hereditary and fractional wave damping, linking the structure of unresolved reservoir modes to macroscopic nonlocal wave dynamics. Full article

The practical aspects of the full regioselective preparation of 2-(tert-butyl)-3-hydroxy-7-2,3-dihydro-1H-pyrrolo[3,4-c]pyridine-1-ones and their derivatives were described. Created in our laboratory, the reaction protocol is simple and occurs under mild conditions. It is important that all obtained products are stable, pure, crystalline and can be easily identified based on spectral data and X-ray analysis results. Key aspects of the reaction course were explained based on the DFT quantum chemical calculations. Full article

Background/Objectives: Cell-free DNA (cfDNA) end-motifs (EDMs) are promising fragmentomic features for noninvasive cancer detection; however, their diagnostic utility may be limited by background signals from abundant hematopoietic-derived cfDNA fragments. Existing EDM-based approaches, including the Motif Diversity Score (MDS) and classifiers based on raw motif frequencies, often show limited robustness across different datasets. Methods: To address this limitation, we developed a frequency-domain analytical framework based on the Discrete Fourier Transform (DFT), converting k-mer EDM frequency profiles into amplitude spectral features. We further constructed a stacking-based Ensemble Spectral Model (ESM) integrating multi-scale spectral features from 4–6-mer EDMs. Results: The framework was evaluated using 1782 plasma cfDNA samples from four independent studies comprising six datasets. Raw EDM profiles showed extremely high similarity between cancer and non-cancer samples (mean Spearman R = 0.999). Following DFT transformation, amplitude spectra showed improved separability between groups. Across datasets, the ESM achieved a mean AUC of 0.843, representing a 15.0% improvement over raw 4-mer EDM-based SVM models and a 56.4% improvement over the MDS. At 95% specificity, mean sensitivity reached 0.585, exceeding those of the raw EDM (0.418) and MDS (0.195). Frequency-guided motif attribution further linked spectral features to sequence-level motif patterns and potential regulatory programs. Conclusions: Frequency-domain transformation improves the representation of cfDNA EDM profiles and provides a robust analytical framework for cross-dataset cancer detection. Full article

The detection of small archaeological artifacts in high-resolution aerial imagery is challenged by minimal target size and local spectral and geometric similarity to background soils. This study identifies a failure mode in end-to-end deep learning where radiometrically dominant chromatic signals destabilize gradient-based optimization, leading to rapid training collapse. Using UAV imagery of Maya archaeological sites in Belize, we examine fingernail-sized ceramic sherds characterized by a consistent reddish hue. A Hue-Weighted Loss Function (HWLF) is introduced as a diagnostic instrument. Under severe class imbalance, chromatic gradients suppress geometric feature learning, collapsing detection within 300 iterations. Motivated by this discovery, we propose a staged detection architecture that decouples geometric candidate generation from chromatic validation. Candidates are detected via a transformer-based object detector and validated using hue constraints derived from unmodified 16-bit HSV representations. This approach reduced the Phase I candidate pool (177,148 geometric detections) to 1647 prioritized detections—a 99.1% reduction—while retaining 97.8% of annotated targets (F1 = 0.731). Chromatic priors may be more effective as decoupled post-inference discriminants than as embedded end-to-end optimization signals under severe class imbalance, where their gradient influence risks suppressing geometric feature learning entirely. Full article

The present work introduces the Bulgarian Spectral Database for Painting Materials, a freely accessible web-based resource containing FTIR and Raman spectra, together with complementary analytical information, for materials commonly found in Bulgarian artworks. The database encompasses a collection of over 200 reference materials and more than 100 entries derived from authentic samples obtained from wall paintings, dating from the 5th century BC to the 20th century. The largest section of the database consists of inorganic reference materials, including natural and synthetic mineral pigments, fillers, and additives commonly identified in historical mural paintings, complemented by organic binders and natural dyes. Reference model mixtures simulating historical painting techniques are also included. The database provides interactive visualization and downloadable spectra in plain text formats (.txt) compatible with all spectroscopic software. The integration of spectral data obtained from artworks represents a distinctive feature of the resource. The database is a practical tool for material identification, comparative studies, and conservation research in the field of cultural heritage science. It also provides a robust foundation for comparative studies and facilitates interdisciplinary research across the Balkan region and beyond. Full article

Vegetation phenology refers to the cyclical growth patterns of vegetation in nature, which are influenced by climatic conditions, human activities, and genetic factors. It plays an irreplaceable role in regulating carbon cycling and energy flow within natural ecosystems. However, the combination of a cloudy and rainy climate with a landscape characterized by the interplay of land and water and fragmented patches has long posed challenges for remote sensing phenological monitoring data, including a scarcity of valid observations, frequent temporal gaps, and spectral distortion in mixed pixels. These issues make it difficult to reliably support the needs of wetland phenological inversion and mapping. To address this issue, this study uses vegetation inversion in the Poyang Lake wetlands as a case study and reconstructs high-spatiotemporal-resolution time-series kNDVI data based on multi-source remote sensing data. Methodologically, we propose an improved and enhanced spatiotemporal adaptive reflectance fusion model, IESTARFM. This model enhances the homogeneity of similar pixel selection through adaptive matching windows and land cover constraints. Additionally, it explicitly incorporates cloud probability and time-lag factors into the weighting structure to systematically downweight unreliable observations, and further employs quadratic term corrections to account for the nonlinear growth response of kNDVI. Using the reconstructed dataset, key phenological information is extracted by combining third-order harmonic analysis with a dynamic thresholding method, thereby enhancing the robust characterization of seasonal trajectories under conditions of missing data and noise. Accuracy evaluation results show that the 10m/8d high-frequency kNDVI dataset reconstructed by IESTARFM achieves at least a 12.61% improvement in fusion accuracy compared to classical methods such as ESTARFM, STARFM, and FSDAF, with a maximum reduction in RMSE of 0.026, and effectively restores details in areas with thin cloud cover. The reconstructed kNDVI series achieved a coefficient of determination $R^2$ = 0.875 and RMSE = 0.066 relative to Sentinel-2 observations, indicating that the reconstructed series closely reproduces the reference imagery in both amplitude and spatial structure. The phenological parameters derived from kNDVI exhibit an RMSE of 4.81 days compared to field observations, demonstrating that the reconstructed time series reliably captures the timing of key phenological events. It should be noted that the proposed approach is designed for post-event time-series reconstruction and is not intended for real-time forecasting. In summary, this study collaboratively enhanced the reliability of high-resolution index time-series reconstruction and phenological identification in cloudy and rainy wetlands through three key aspects: cloud noise suppression, heterogeneous boundary preservation, and nonlinear growth characterization. It provides a generalizable technical foundation for dynamic monitoring of wetland vegetation, ecological restoration assessment, and refined management in regions with frequent cloud and rainfall. Full article

Accurate vineyard yield estimation is essential for harvest planning, resource allocation, and economic decision-making, particularly under conditions of high spatial variability. Traditional sampling-based methods are labor-intensive, destructive, and prone to error, especially in high-productivity free-canopy systems. This study developed and evaluated predictive models for commercial irrigated vineyards of Carménère and Chardonnay in Chile’s Maule Region across two growing seasons (2023–2025). Structural yield components, physiological measurements, and UAV-derived multispectral indices (NDVI, GNDVI, NDRE) were collected from georeferenced sampling grids. Modeling approaches included linear regression, stepwise selection, and machine learning algorithms (Random Forest, Multilayer Perceptron). Validation results showed that cluster number was the primary driver of yield variability, explaining up to 40% of variation. Incorporating physiological and spectral variables improved accuracy, with the best models (least squares and MLP) achieving R² values up to 0.66 and reducing errors to 12–15%. Spatial yield maps reproduced intra-vineyard variability patterns, demonstrating that integrating plant-level and canopy-level data substantially enhances yield prediction. These findings provide a robust framework for precision viticulture applications. Full article

Compact finite difference schemes approximate spatial derivatives through implicit relations between neighboring grid points. Despite using compact stencils and relatively simple algebraic structures, these schemes achieve high-order accuracy and spectral-like resolution, reducing dispersion errors while maintaining low numerical dissipation. These properties make them particularly attractive for problems requiring accurate spatial derivatives and computational efficiency, such as wave propagation, aeroacoustics, and turbulent flow simulations. This review presents the main ideas behind compact finite difference schemes, including their derivation from Taylor expansions and Padé approximations, their accuracy properties, and their resolution characteristics through modified wavenumber analysis. The manuscript is intended as a review and practical synthesis, rather than as the proposal of a new numerical scheme, and aims to connect the theoretical construction of compact schemes with their numerical behavior, practical implementation, and representative applications. To support reproducibility, we provide a fully documented open-source Python 3.11 notebook with a reference implementation of the schemes discussed in the paper. The examples include first- and second-order derivative calculations and representative one- and two-dimensional boundary-value problems, including Helmholtz-type equations. Finally, we survey applications across computational fluid dynamics, acoustics, geophysical flows, structural mechanics, biology, electromagnetism, and quantitative finance. Full article

Planted forests are increasingly promoted to meet rising demand for forest products and restore degraded lands, but their extent and ecological implications are often misrepresented because tree crops (e.g., orchards, plantation agriculture) exhibit similar spectral and spatial signatures to planted forests. This study aims to improve differentiation between planted forests and tree crops within national-scale restoration programs. We combined Landsat-derived NDVI time series targeting disturbance-related phenological windows with the LandTrendr algorithm to map planting/clearcutting events and fused in situ spectral measurements with Sentinel-2 to develop a modified orchard flowering index (MOFI). Random forest models evaluated classification performance using combinations of spatiotemporal spectral features, biomass accumulation proxies, and the MOFI. Incorporating the MOFI improved discrimination of tree crops versus planted forests, raising the planted forest F1 from 0.751 to 0.843. The combination of the MOFI and spatiotemporal spectral features achieved the highest accuracy (F1 = 0.843). The results show tree crops are concentrated on plains and gentle mountain slopes, while plantations occur mostly on slopes > 15°, with tree crops comprising 27.1% of mapped planted tree area. These findings imply that many national planted forest map estimates may be biased without phenology- and biomass-informed methods and that integrating Landsat and Sentinel phenology metrics supports more accurate monitoring for management and policy. Full article

Pre-classification of lakes into optical water types (OWTs) is considered a useful step in analyzing satellite-based reflectance data. We used a dataset of 109 reflectance hyperspectra from Minnesota and Wisconsin lakes and rivers to evaluate the usefulness of pre-classification to improve the retrieval of water quality information from satellite data. Three OWT classes were derived from the dataset by K-means clustering using three integrative metrics of reflectance spectral shape and magnitude as clustering variables. Values of the three metrics can be determined from satellite reflectance data as well as hyperspectral data. The OWT classes had distinct water quality characteristics in terms of Secchi depth, chlorophyll-a, and colored dissolved organic matter (CDOM). Algorithms used to retrieve values of the variables from simulated Sentinel-2 band reflectance data usually yielded more accurate predictions when computed separately for each class than when computed for the entire dataset, although exceptions were found for some fitting metrics and models and results for chlorophyll-a were not definitive. The three water quality variables were related in distinct ways to the integrative shape metric of reflectance spectra, apparent visible wavelength (AVW), supporting its use to develop OWTs to organize waterbodies into water quality classes. AVW was correlated (r = 0.933) with the integrative metric, normalized difference index at green and red wavelengths (NDI). Based on that result, we found that OWTs developed using just two variables, AVW and a metric of spectral magnitude, were nearly the same as classifications using all three integrative metrics. Full article

Ecosystems can undergo abrupt, often irreversible transitions between alternative states—phenomena termed critical transitions or regime shifts—with profound consequences for biodiversity, ecosystem services, and human well-being. Early warning signals (EWSs) derived from time-series analysis offer the prospect of anticipating such transitions before they occur, potentially enabling preventive management intervention. This review provides a comprehensive synthesis of EWS methods for ecological systems, encompassing theoretical foundations, statistical indicators, empirical applications, and emerging methodological frontiers. We examine the dynamical basis of EWS in critical slowing down theory, wherein systems approaching bifurcation points exhibit characteristic statistical signatures including rising autocorrelation, increasing variance, and spectral reddening. We present a systematic overview of proposed indicators discuss moving-window frameworks for their computation, and critically evaluate preprocessing requirements and sensitivity to analytical choices. Empirical applications across major ecosystem types—including lakes, coral reefs, grasslands, forests, and marine fisheries—reveal both successes and limitations, with EWS performance depending critically on data quality, transition mechanism, and system-specific dynamics. We address recent advances including machine learning approaches, non-equilibrium thermodynamic indicators, multivariate extensions, and the important distinction between bifurcation-induced, noise-induced, and rate-induced tipping. We conclude with recommendations for specialists, emphasizing the integration of EWS within broader monitoring frameworks, systematic sensitivity analysis, and the interpretation of indicators as probabilistic assessments of changing resilience rather than deterministic predictions of imminent collapse. Full article

Yield estimation in sugarcane systems remains a major challenge in tropical regions due to the reliance on destructive, labor-intensive, and spatially limited field measurements. Although remote sensing has been widely used for crop monitoring, its predictive performance is often constrained when spectral information is used in isolation. This study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions. A commercial field was monitored throughout the 2022–2023 growing season, and machine learning models, including random forest (RF), support vector machine (SVM), and multiple linear regression (MLR), were developed to estimate stem, foliage, and total biomass. To reduce potential spatial data leakage caused by spatial autocorrelation within the field, model performance was evaluated using Spatial Block Cross-Validation. Results showed that integrating spectral and meteorological data consistently improved predictive performance compared to spectral-only and weather-only scenarios. Spectral bands exhibited stronger relationships with biomass than derived vegetation indices, while maximum temperature and solar radiation were identified as key drivers of biomass variability. RF combined with spectral–weather fusion achieved the highest predictive performance, reaching R² values up to 0.95, RMSE values as low as 5296.35, and rRMSE values close to 18% for stem biomass, consistently outperforming SVM and MLR. In contrast, spectral-only scenarios produced lower predictive accuracy and higher prediction errors across all biomass variables. This study provides one of the first field-scale implementations under humid tropical conditions in southeastern Mexico, where georeferenced yield data remain scarce. Full article

Sparse tensors have been widely applied in hypergraph representation, complex network analysis, and high-dimensional data processing, where eigenvalue inclusion sets play a fundamental role in analyzing the properties of such tensors. For sparse tensors, two new eigenvalue inclusion sets are proposed in this paper via the matrix’s digraph. Specifically, we employ the digraphs of the majorization matrix and the representation matrix to establish two new S-type inclusion sets and theoretically prove that these sets are tighter than the existing ones. By applying these inclusion sets, some inequalities for the spectral radius of weakly irreducible nonnegative tensors are derived. Moreover, several criteria for verifying the positive (semi-)definiteness of symmetric sparse tensors are also obtained. Numerical examples are provided to illustrate the effectiveness of the proposed spectral radius bounds and positive definiteness criteria. Full article

Food adulteration with melamine represents a serious threat to food safety due to its toxic effects and its ability to falsely elevate protein values measured by nitrogen-based methods. Visual inspection and visible reflectance spectroscopy are unsuitable for identifying low-level adulteration. This study evaluates Fourier Transform Infrared (FTIR) spectroscopy combined with chemometric tools for the identification of melamine in brown rice flour adulterated at 0–2.00% (w/w). Under the tested conditions, no clear FTIR-detectable interactions between melamine and starch or proteins were observed, suggesting that melamine primarily acts as a physical admixture. Characteristic melamine absorption bands were identified at 3466, 3415, 1431, and 810 cm⁻¹. Spectral normalization and second-order derivative processing improved sensitivity and enabled quantitative calibration models. The method achieved a limit of detection of 1408 mg/kg. Although this value is above the regulatory threshold of 2.5 mg/kg, the approach provides a rapid, non-destructive screening tool for identifying highly adulterated samples and prioritizing them for confirmatory chromatographic or mass spectrometric analysis. Overall, FTIR spectroscopy combined with chemometric analysis offers an efficient first-line approach for identification of melamine adulteration in brown rice flour. Full article