Search Results (359)

Functional foods like flax (Linum usitatissimum L.) are rich sources of specialized metabolites that contribute to their nutritional and health-promoting properties. Understanding the biosynthesis of these compounds is essential for improving their quality and potential applications. However, dissecting complex metabolic networks in plants remains challenging due to the dynamic nature and interconnectedness of biosynthetic pathways. In this study, we present a synergistic approach combining stable isotopic labeling (SIL), Candidate Substrate–Product Pair (CSPP) networks, and a time-course study with high temporal resolution to reveal the biosynthetic fluxes shaping phenylpropanoid metabolism in young flax seedlings. By feeding the seedlings with ¹³C₃-p-coumaric acid and isolating isotopically labeled metabolization products prior to the construction of CSPP networks, the biochemical validity of the connections in the network was supported by SIL, independent of spectral similarity or abundance correlation. This method, in combination with multistage mass spectrometry (MSⁿ), allowed confident structural proposals of lignans, neolignans, and hydroxycinnamic acid conjugates, including the presence of newly identified chicoric acid and related tartaric acid esters in flax. High-resolution time-course analyses revealed successive waves of metabolite formation, providing insights into distinct biosynthetic fluxes toward lignans and early lignification intermediates. No evidence was found here for the involvement of chlorogenic or caftaric acid intermediates in chicoric acid biosynthesis in flax, as has been described in other species. Instead, our findings suggest that in flax seedlings, chicoric acid is synthesized through successive hydroxylation steps of p-coumaroyl tartaric acid esters. This work demonstrates the power of combining SIL and CSPP strategies to uncover novel metabolic routes and highlights the nutritional potential of flax sprouts rich in chicoric acid. Full article

Leaf phenology is key to ecosystem functioning by regulating carbon, water, and energy fluxes and influencing vegetation productivity. Yet, detecting land surface phenology (LSP) in drylands using remote sensing remains particularly challenging due to sparse and heterogeneous vegetation cover, high spatiotemporal variability, and complex spectral signals. Unlike the Northern Hemisphere, these challenges are further compounded in the Southern Hemisphere (SH), where several regions experience year-round moderate temperatures. When combined with irregular rainfall, this leads to highly variable vegetation activity throughout the year. However, LSP dynamics in the SH remain poorly understood. This study presents a review of remote sensing-based phenology research in drylands, integrating (i) a synthesis of global methodological advances and (ii) a systematic analysis of peer-reviewed studies published from 2015 through April 2025 focused on SH drylands. This review reveals a research landscape still dominated by conventional vegetation indices (e.g., NDVI) and moderate-spatial-resolution sensors (e.g., MODIS), though a gradual shift toward higher-resolution sensors such as PlanetScope and Sentinel-2 has emerged since 2020. Despite the widespread use of start- and end-of-season metrics, their accuracy varies greatly, especially in heterogeneous landscapes. Yet, advanced products such as solar-induced chlorophyll fluorescence or the fraction of absorbed photosynthetically active radiation were rarely employed. Gaps remain in the representation of hyperarid zones, grass- and shrub-dominated landscapes, and large regions of Africa and South America. Our findings highlight the need for multi-sensor approaches and expanded field validation to improve phenological assessments in dryland environments. The accurate differentiation of vegetation responses in LSP is essential not only for refining phenological metrics but also for enabling more realistic assessments of ecosystem functioning in the context of climate change and its impact on vegetation dynamics. Full article

The galaxy NGC 7469 is a bright infrared source with an active galactic nucleus (AGN) and an intense star-forming region with a radius of approximately 500 parsecs, where the star formation rate is estimated to be 20–50 $M_{\odot }{\mathrm{yr}}^{-1}$. This study presents the results of spectral and photometric observations carried out during the period from 2020 to 2024 at the Fesenkov Astrophysical Institute (Almaty, Kazakhstan) and the Nasreddin Tusi Shamakhy Astrophysical Observatory (Shamakhy, Azerbaijan). Photometric data were obtained using B, V, and Rc filters, while spectroscopic observations covered the wavelength range of $\lambda$ 4000–7000 Å. Data reduction was performed using the IRAF and MaxIm DL Pro6 software packages. An analysis of the light curves revealed that after the 2019–2020 outburst, the luminosity level of NGC 7469 remained relatively stable until the end of 2024. In November–December 2024, an increase in brightness (∼0.3–0.5 magnitudes) was recorded. Spectral data show variations in the Ha fluxes and an enhancement of them at the end of 2024. On BPT diagrams, the emission line flux ratios [OIII]/H $\beta$ and [NII]/H $\alpha$ place NGC 7469 on the boundary between regions dominated by different ionization sources: AGN and star-forming regions. The electron density of the gas, estimated from the intensity ratios of the [SII] 6717, 6731 Ålines, is about $900–1000{\mathrm{cm}}^{-3}$. Continued observations will help to determine whether the trend of increasing brightness and emission line fluxes recorded at the end of 2024 will persist. Full article

Optimizing artificial lighting in controlled-environment agriculture is crucial for enhancing crop productivity and resource efficiency. This study presents a spectral–spatial co-optimization strategy for LED lighting tailored to the physiological needs of Italian lettuce (Lactuca sativa L. var. italica). A miniature plant factory system was developed with dimensions of 400 mm × 400 mm × 500 mm (L × W × H). Seven customized spectral treatments were created using 2835-packaged LEDs, incorporating various combinations of blue and violet LED chips with precisely controlled concentrations of red phosphor. The spectral configurations were aligned with the measured absorption peaks of Italian lettuce (450–470 nm and 640–670 nm), achieving a spectral mixing uniformity exceeding 99%, while the spatial light intensity uniformity surpassed 90%. To address spatial light heterogeneity, a particle swarm optimization (PSO) algorithm was employed to determine the optimal LED arrangement, which increased the photosynthetic photon flux density (PPFD) uniformity from 83% to 93%. The system operates with a fixture-level power consumption of only 75 W. Experimental evaluations across seven treatment groups demonstrated that the E-spectrum group—comprising two violet chips, one blue chip, and 0.21 g of red phosphor—achieved the highest agronomic performance. Compared to the A-spectrum group (three blue chips and 0.19 g of red phosphor), the E-spectrum group resulted in a 25% increase in fresh weight (90.0 g vs. 72.0 g), a 30% reduction in SPAD value (indicative of improved light-use efficiency), and compared with Group A, Group E exhibited significant improvements in plant morphological parameters, including a 7.05% increase in plant height (15.63 cm vs. 14.60 cm), a 25.64% increase in leaf width (6.37 cm vs. 5.07 cm), and a 6.35% increase in leaf length (10.22 cm vs. 9.61 cm). Furthermore, energy consumption was reduced from 9.2 kWh (Group A) to 7.3 kWh (Group E). These results demonstrate that integrating spectral customization with algorithmically optimized spatial distribution is an effective and scalable approach for enhancing both crop yield and energy efficiency in vertical farming systems. Full article

Wildfires play a critical role in boreal forest ecosystems, yet their increasing frequency poses significant challenges for carbon emissions, ecosystem stability, and fire management. Accurate burned area detection is essential for assessing post-fire landscape recovery and fire-induced carbon fluxes. This study develops, compares, and optimizes machine learning (ML)-based models for burned area classification in the eastern Canadian boreal forest from 2000 to 2023 using MODIS-derived features extracted from Google Earth Engine (GEE), and the feature extraction includes maximum, minimum, mean, and median values per feature to enhance spectral representation and reduce noise. The dataset was randomly split into training (70%), validation (15%), and testing (15%) sets for model development and assessment. Combined labels were used due to class imbalance, and the model performance was assessed using kappa and the F1-score. Among the ML techniques tested, deep learning (DL) with a Multi-Layer Perceptron (MLP) outperformed Support Vector Machines (SVMs) and Random Forest (RF) by demonstrating superior classification accuracy in detecting burned area. It achieved an F1-score of 0.89 for burned pixels, confirming its potential for improving the long-term wildfire monitoring and management in boreal forests. Despite the computational demands of processing large-scale remote sensing data at 250 m resolution, the MLP modeling approach that we used provides an efficient, effective, and scalable solution for long-term burned area detection. These findings underscore the importance of tuning both network architecture and regularization parameters to improve the classification of burned pixels, enhancing the model robustness and generalizability. Full article

Studying the host galaxies of fast radio bursts (FRBs) is critical to understanding the formation processes of their sources and, hence, the mechanisms by which they radiate. Toward this end, we have extended the Blinkverse database version 1.0, which already included burst information about FRBs observed by various telescopes, by adding information about 92 published FRB host galaxies to make version 2.0. Each FRB host has 18 parameters describing it, including redshift, stellar mass, star-formation rate, emission line fluxes, etc. In particular, each FRB host includes images collated by FASTView, streamlining the process of looking for clues to understanding the origin of FRBs. FASTView is a tool and API for quickly exploring astronomical sources using archival imaging, photometric, and spectral data. This effort represents the first step in building Blinkverse into a comprehensive tool for facilitating source observation and analysis. Full article

A composite-coated inclusion is embedded in a matrix, where the conductivity (permittivity) of the phases is assumed to be complex-valued. The purpose of this paper is to demonstrate that a non-zero flux can arise under specific conditions related to the conductivities of the components in the absence of external sources. These conditions are unattainable with conventional positive conductivities but can be satisfied when the conductivities are negative or complex—a scenario achievable in the context of metamaterials. The problem is formulated as a spectral boundary value problem for the Laplace equation, featuring a linear conjugation condition defined on a smooth curve L. This curve divides the plane ${\mathbb{R}}^2$ into two regions, $D^{+}$ and $D^{-}\ni \infty$. The spectral parameter appears in the boundary condition, drawing parallels with the Steklov eigenvalue problem. The case of a circular annulus is analyzed using the method of functional equations. The complete set of eigenvalues is derived by applying the classical theory of self-adjoint operators in Hilbert space. Full article

This paper introduces Harmonizer, a universal framework designed for tokenizing heterogeneous input signals, including text, audio, and video, to enable seamless integration into multimodal large language models (LLMs). Harmonizer employs a unified approach to convert diverse, non-linguistic signals into discrete tokens via its FusionQuantizer architecture, built on FluxFormer, to efficiently capture essential signal features while minimizing complexity. We enhance features through STFT-based spectral decomposition, Hilbert transform analytic signal extraction, and SCLAHE spectrogram contrast optimization, and train using a composite loss function to produce reliable embeddings and construct a robust vector vocabulary. Experimental validation on music datasets such as E-GMD v1.0.0, Maestro v3.0.0, and GTZAN demonstrates high fidelity across 288 s of vocal signals (MSE = 0.0037, CC = 0.9282, Cosine Sim. = 0.9278, DTW = 12.12, MFCC Sim. = 0.9997, Spectral Conv. = 0.2485). Preliminary tests on text reconstruction and UCF-101 video clips further confirm Harmonizer’s applicability across discrete and spatiotemporal modalities. Rooted in the universality of wave phenomena and Fourier theory, Harmonizer offers a physics-inspired, modality-agnostic fusion mechanism via wave superposition and interference principles. In summary, Harmonizer integrates natural language processing and signal processing into a coherent tokenization paradigm for efficient, interpretable multimodal learning. Full article

Magnetic flux density (MFD) map matching is a technique that can provide absolute position solutions by comparing a series of MFD measurements with a database. Map matching relies on the consistent measurement of the same physical phenomena during the surveying and positioning phases. However, fluctuations in MFD due to alternating current (AC) electricity, influenced by dynamic power requirements, pose a challenge. This paper analyses the characteristics of the influences of AC sources on MFD measurements. It shows that employing spectral filtering can isolate magnetic perturbations from AC sources, which could be used to enhance the magnetic map matching’s resilience to this form of temporal change. Full article

This paper presents the results of the photometric and spectral monitoring of the galaxy NGC 3516, which is an active galactic nucleus (AGN) of type Sy $1.5$ with a changing look. Observations were carried out at the Fesenkov Astrophysical Institute (FAI, Almaty, Kazakhstan) and the Shamakhy Astrophysical Observatory (ShAO, Shamakhy, Azerbaijan). Spectral monitoring of this galaxy in the wavelength range 4000–7000 Å began in 2020, while photometric observations have been conducted since 2014. During the observation period, estimates of the galaxy’s brightness in the B, V and $Rc$ filters were obtained, as well as measurements of the emission line and continuum fluxes. The light curve shows increased brightness of NGC 3516 in 2016 and 2019. The increase of emission line fluxes of H$\beta$ and H$\alpha$ and continuum began in 2019 and continued until spring 2020, when these characteristics reached their maximal values. A powerful X-ray flare took place on 1 April 2020. A new phase of brightening began in 2021 and has continued until 2025. After reaching their maxima in 2020, the emission fluxes of H$\beta$ and H$\alpha$ decreased by a factor of 1.5–2 and remained at a low level until 2022–2023, when they began to increase again. Medium-resolution spectra obtained on 20 April 2020, with the 1-meter “West” telescope (TSHAO) were used to study the broad components of the H$\beta$ and H$\alpha$ emission line profiles. Model calculations showed that the broad profile of the H$\alpha$ line consists of a central unshifted component and two (blue and red) components shifted symmetrically relative to the central component by a velocity of $v=980\pm 20$ km ${\mathrm{s}}^{-1}$. The H$\beta$ emission line was relatively weak, so the radial velocity of its components was determined with a large uncertainty: $900\pm 600$ km ${\mathrm{s}}^{-1}$. Full article

An estimate of the trigger effect of the proton flux on seismicity was obtained. The proton flux time series with a time step of 5 min, 2000–2024, was analyzed. In each time interval of 5 days, statistics of the proton flux time series were calculated: mean values, logarithm of kurtosis, spectral slope, singularities spectrum support width, wavelet-based entropy, and the Donoho–Johnston wavelet-based index. For each of the used statistics, time points of local extrema were found, and for each pair of time sequences of proton flux statistics and earthquakes with a magnitude of at least 6.5 in sliding time windows, the “advance measures” of each time sequence relative to the other were estimated using a model of the intensity of interacting point processes. The difference between the “direct” measure of the advance of time points of local extrema of proton flux statistics relative to the time moments of earthquakes and the “inverse” measure of the advance was calculated. The maximum proportion of the intensity of seismic events for which the proton flux was a trigger was estimated as 0.28 for using the points of the local minima of the singularities spectrum support width. Full article

Optimizing light conditions in controlled-environment agriculture is critical for enhancing crop yield and energy efficiency, particularly in high-value crops like strawberries, where precise spectral tuning can significantly influence both vegetative growth and fruit production. In this study, a windmill-style vertical farming system was developed to facilitate efficient strawberry cultivation under low-light conditions. A custom LED lighting fixture, measuring 3 m in length, was suspended 30 cm above the canopy to uniformly illuminate a planting zone of 3.0 m × 0.3 m. The lighting system, which combines red (655–665 nm) and full-spectrum white LEDs, was optimized using a particle swarm optimization (PSO) algorithm to enhance spatial light distribution. The uniformity of photosynthetic photon flux density (PPFD) improved from 71% to 85%, and the standard deviation decreased from 75 to 15. Under a 16 h optimized lighting regime, strawberry plants exhibited a 55% increase in height compared to the non-supplemented control group (Group D), a 40% increase in leaf width, and a 36% increase in fruit weight (69.76 g per plant) relative to the 12 h supplemental lighting group (Group A). The system operates at a fixture-level power consumption of just 160 W, with its spectral output aligned with the absorption characteristics of strawberry foliage and fruit. These results demonstrate that an algorithm-driven lighting layout can significantly enhance both vegetative and reproductive performance in vertical strawberry farming while maintaining high energy efficiency. Full article

Since its first detection in 2013, Xylella fastidiosa subsp. pauca (Xfp) has caused a devastating Olive Quick Decline Syndrome (OQDS) outbreak in Southern Italy. Effective disease surveillance and treatment strategies are urgently needed to mitigate its impact. This study investigates the short-term (1.5 years) effects of thymol-based treatments on infected olive trees of the susceptible cultivar Cellina di Nardò in two orchards in Salento, Apulia region. Twenty trees per trial received a 3% thymol solution either alone or encapsulated in a cellulose nanoparticle carrier. Over two years, sap flux density and canopy-transmitted solar radiation were monitored using TreeTalker sensors, and spectral greenness indices were calculated. Xfp cell concentrations in plant tissues were quantified via qPCR. Neither thymol treatment halted disease progression nor significantly reduced bacterial load, though the Xfp cell concentration reduction increased over time in the preventive trial. Symptomatic trees exhibited increased sap flux density, though the treatment mitigated this effect in the curative trial. Greenness indices remained lower in infected trees, but the response to symptom severity was delayed. These findings underscore the need for longer-term studies, investigation of synergistic effects with other phytocompounds, and integration of real-time sensor data into adaptive disease management protocols. Full article

As global atmospheric methane concentrations surge at an unprecedented rate, the identification of methane super-emitters with significant mitigation potential has become imperative. In this study, we utilize remote sensing satellite data with varying spatiotemporal coverage and resolutions to detect and quantify methane emissions. We exploit the synergistic potential of Sentinel-2, EnMAP, and GF5-02-AHSI for methane plume detection. Employing a matched filtering algorithm based on EnMAP and AHSI, we detect and extract methane plumes within emission hotspots in China and the United States, and estimate the emission flux rates of individual methane point sources using the IME model. We present methane plumes from industries such as oil and gas (O&G) and coal mining, with emission rates ranging from 1 to 40 tons per h, as observed by EnMAP and GF5-02-AHSI. For selected methane emission hotspots in China and the United States, we conduct long-term monitoring and analysis using Sentinel-2. Our findings reveal that the synergy between Sentinel-2, EnMAP, and GF5-02-AHSI enables the precise identification of methane plumes, as well as the quantification and monitoring of their corresponding sources. This methodology is readily applicable to other satellite instruments with coarse SWIR spectral bands, such as Landsat-7 and Landsat-8. The high-frequency satellite-based detection of anomalous methane point sources can facilitate timely corrective actions, contributing to the reduction in global methane emissions. This study underscores the potential of spaceborne multispectral imaging instruments, combining fine pixel resolution with rapid revisit rates, to advance the global high-frequency monitoring of large methane point sources. Full article

Lighting from light-emitting diodes (LEDs) is one of the largest capital and operational expenses for indoor farms. While broad-waveband white LEDs are relatively inexpensive, their efficacy is lower than most narrow-band LEDs. This study aimed to determine how supplementing warm-white light with additional blue (400–499 nm), green (500–599 nm), red (600–699 nm), or far-red (700–750 nm) light influences lettuce (Lactuca sativa) growth and quality, and whether these effects are consistent across two photon flux densities (PFDs). We grew lettuce ‘Rouxai’ and ‘Rex’ under 90 or 180 µmol∙m⁻²∙s⁻¹ of warm-white light supplemented with 40 or 80 µmol∙m⁻²∙s⁻¹ of blue, green, red, far-red, or warm-white light. Supplemental far-red light increased biomass without reducing secondary metabolites. Supplemental red, far-red, and warm-white light maximized biomass, whereas additional blue light enhanced secondary metabolite concentrations and leaf coloration. Increasing the PFD increased biomass and phenolic content in ‘Rouxai’. Notably, spectral effects were consistent across PFD levels, suggesting that higher PFDs do not diminish spectral responses. These results demonstrate the potential of enriching white light to increase yield or quality in controlled-environment agriculture and provide insights for cost-effective commercial production. Full article