Search Results (18,724)

This study aimed to evaluate the aromatic potential of four new Monastrell-derived white grapevine genotypes (MC180, MC69, MT103, MV67) compared with Verdejo over four consecutive seasons (2020–2023), with particular emphasis on both free and glycosidically bound volatile compounds. This approach provided novel insight into the aromatic composition of emerging cultivars under warm climate conditions and their potential suitability for future viticultural use. Free and glycosidically bound volatile compounds were extracted and analyzed using Gas Chromatography–Mass Spectrometry (GC-MS). Differences in aroma profiles were observed among genotypes and seasons. MV67 and MC69 showed higher levels of monoterpenes and volatile phenols, suggesting enhanced floral and complex aromatic potential. Seasonal effects strongly influenced C6 compounds and norisoprenoids, highlighting the importance of climatic conditions in shaping grape aroma. Multifactorial analysis revealed that season had the greatest impact on most compound families, although genotype and its interaction with season were also significant. These results demonstrate that genotype–environment interactions play a key role in determining aromatic composition. The elevated levels of aroma precursors, particularly glycosidically bound compounds, indicate promising enological potential for producing fresh, aromatic white wines. Therefore, these new cultivars represent suitable alternatives for white wine production in warm climates. Full article

Power systems with high renewable penetration impose multi-dimensional demands on energy storage (ES) regulation. Short-duration ES is required for power balance and frequency support, while medium- and long-duration ES is essential for daily, weekly, and seasonal peak shaving and energy time-shifting. Aiming at the challenge of multi-timescale configuration of hybrid energy storage (HES) in the initial planning stage of carbon-neutral transition, this paper proposes an optimal configuration strategy combining STL-PDM-VMD. First, the seasonal and trend decomposition using Loess (STL) is used to extract quarterly trends of annual net power for seasonal ES configuration. Then, the Past Decomposable Mixing (PDM) module in the time-mixer model is applied to decouple and mix multi-scale features of the detrended power curve for monthly and weekly configurations. Finally, an improved Variational Mode Decomposition (VMD) is adopted to decompose daily net power fluctuations and optimize intra-day energy storage schemes. Based on actual data from a carbon-neutral transition region, simulations are carried out and compared with the VMD method with decomposition layers optimized by Gurobi. The results show that the proposed STL-PDM-VMD multi-timescale hybrid energy storage configuration strategy can effectively capture the multi-timescale fluctuation characteristics of net load, significantly improve the Renewable Energy (RE) penetration rate, and ensure the power and energy balance of the new power system at multiple timescales. penetration, and maintain power and energy balance in the new-type power system. Full article

Nosema ceranae is a common and highly contagious fungal pathogen that primarily infects the gut of adult honeybees, causing nosemosis. As a chronic disease of the digestive system, it poses a global threat to honeybee health and colony sustainability. This study aimed to investigate the inhibitory effects of different concentrations of Scutellaria baicalensis aqueous extract on N. ceranae in the intestines of infected Apis cerana through feeding experiments. In addition, the therapeutic efficacy of its major active component, baicalin, was evaluated, and its potential molecular mechanisms of action were explored. The results showed that, compared with the control group, administration of S. baicalensis aqueous extract at concentrations of 1 mg/mL, 5 mg/mL, and 10 mg/mL significantly reduced midgut spore loads (p < 0.05). Further experiments showed that a 0.5 mg/mL baicalin sucrose solution, prepared with 0.5% (v/v) DMSO as co-solvent, exhibited optimal solubility and significantly inhibited the proliferation of spores in the honeybee midgut. Transcriptomic analysis of A. cerana revealed varying numbers of significantly differentially expressed genes among the baicalin-treated (HG) group, the co-solvent control (DMSO) group, and the blank control (C) group. Four candidate DEGs associated with the effects of baicalin were further identified, namely LOC108003965, LOC108000905, LOC107996681, and CYP4G11. Gene Ontology enrichment analysis showed that, in the comparison between the HG group and the C group, these DEGs were significantly enriched in six functional categories: iron ion binding, phosphoric ester hydrolase activity, heme binding, tetrapyrrole binding, hydrolase activity (acting on ester bonds), and oxidoreductase activity (acting on paired donors, with incorporation or reduction of molecular oxygen). Collectively, these results demonstrate that S. baicalensis aqueous extract effectively inhibits the proliferation of N. ceranae within the host, and its active component, baicalin, exhibits a similar inhibitory effect. The present study proposes a novel strategy in which baicalin may enhance host endogenous chitinase-related activity to target and disrupt the spore wall, offering a new perspective for the prevention and control of honeybee nosemosis. Full article

The new energy vehicle (NEV) industry generates massive multi-source heterogeneous data. To overcome traditional database limitations in terminology disambiguation and multi-hop reasoning, this paper proposes a knowledge graph (KG)-based question-answering (QA) architecture. Three primary domain challenges are addressed: First, to tackle the poor semantic extraction of informal diagnostic texts, a deep semantic parsing network (BERT-BiLSTM-CRF) is integrated to extract high-precision knowledge from 150,000 real-world maintenance records. Second, to solve topological redundancy, the Labeled Property Graph (LPG) specification is employed to encapsulate parameters of 2157 vehicle models as internal attributes, significantly streamlining complex multi-hop reasoning. Finally, to enhance limited reasoning capabilities, an intent classification module (TextCNN) automatically translates natural language into graph queries, enabling deep fault tracing across up to five semantic levels. Experimental results demonstrate 98% and 93% accuracy in entity-relation recognition and intent classification, respectively. The resulting KG (8274 nodes, 14,488 edges) establishes a scalable paradigm for intelligent diagnostic reasoning in complex vertical domains. Full article

Despite the widespread use of Aloe vera extracts, their developmental toxicity in aquatic organisms remains poorly understood. This study investigated the effects of a commercial Aloe vera extract on zebrafish embryogenesis, focusing on developmental, morphological, behavioural, and oxidative stress-related endpoints. The 96 h-LC₅₀ was determined to be 0.03%. Embryos at 2 h post-fertilization (hpf) were exposed for 96 h to 0.0004% (LC₁₀) and 0.03% (LC₅₀). Exposure to 0.0004% caused no significant effects compared to controls. In contrast, exposure to 0.03% significantly increased mortality, reduced heart rate, impaired locomotion, and induced multiple malformations. Biochemical analyses revealed alterations in redox-associated biomarkers, characterized by unchanged ROS levels and mitochondrial activity, increased antioxidant enzyme activities (SOD, GPx, GR), and a decreased GSH:GSSG ratio. Lipid peroxidation levels were reduced, while a significant increase in DNA double-strand breaks (DSBs) was observed. Additionally, Nrf2 protein expression was upregulated at 0.03%. Together, these findings suggest concentration-dependent developmental toxicity correlated with alterations in redox homeostasis and genomic stability during early zebrafish development. This study provides new insight into the developmental hazard potential of a commercial Aloe vera extract in an aquatic vertebrate model. Full article

Mango leaves (Mangifera indica), an underutilized residue, represent a promising source of functional proteins with potential applications in emulsion-based delivery systems. Leaf protein concentrate (LPC) was extracted and modified by high-intensity ultrasound (HIU) to enhance its techno-functional properties. The modified protein was subsequently used as a natural emulsifier to develop oil-in-water (O/W) emulsions enriched with Curatella americana leaf extract, a phenolic-rich source of antioxidant bioactive compounds. Ultrasound-assisted emulsification (UAEm) conditions were optimized using a Box–Behnken experimental design, evaluating the effects of protein concentration (0.5, 1, and 1.5%), oil-to-water ratio (1:4, 1:4.5, and 1:5, mL:mL), and sonication time (2.5, 5, and 7.5 min) on droplet size (D_[4,3], µm). The optimized formulation consisted of 1.5% protein, an O/W ratio of 1:4 mL, and a time of 7.5 min, producing an emulsion with a droplet diameter of 7.23 µm. The emulsions exhibited high resistance to storage, pH variation (2–10), ionic strength (100–500 mM NaCl), and thermal treatments up to 50 °C. Additionally, incorporating C. americana extract enhanced thermal stability, photostability, and antioxidant retention under UV exposure, suggesting the formation of reinforcing protein–polyphenol interactions. These findings demonstrate the potential of mango leaf protein as a sustainable emulsifier and protective carrier for sensitive bioactive compounds, supporting its application in functional food and nutraceutical formulations. Full article

Background/Objectives: Sosnowsky’s hogweed Heracleum sosnowskyi, which originated in the Greater Caucasus region and spread rapidly across Central and Eastern Europe after being introduced as cattle fodder in the 1950s, is an example of an extremely dangerous invasive species listed by the European Union. This study aimed to estimate the genetic diversity of 6 native populations of Sosnowsky’s hogweed from the Caucasus region of Russia and Georgia, as well as 15 invasive populations from Lithuania and Poland, and to assess the adaptability of hogweed in new environments. Methods: Genetic analyses of plant material were conducted, including DNA extraction, ISSR genotyping, PCR product separation, and subsequent molecular data mining and analysis. Results: A pairwise Mantel test revealed a positive correlation between geographical distance and the genetic diversity of the hogweed populations. The presence of three distinct allele pools was confirmed in the populations under study, with genotypes from Poland dominated by the first allele pool, which had the largest number of polymorphic and private loci. Analysis of molecular variance by origin showed that 99% of the variation was within the analysed hogweed populations, with only 1% being between them. Native populations from Russia were genetically distinct from those in Poland and Lithuania. Some of the Georgian population shows genetic similarities to Russians, while the rest shows similarities to the secondary invasive Lithuanians. Conclusions: Introduced populations of H. sosnowskyi are characterised by considerable genetic variation, likely resulting from multiple introductions and subsequent evolutionary processes, which may facilitate local adaptation and invasiveness, although overall large-scale genetic differentiation remains low. Full article

Structural health monitoring (SHM) plays an increasingly important role in managing aging, safety-critical infrastructure under growing environmental and operational demands. In recent years, fiber-optic sensors (FOSs) have attracted significant attention for SHM applications due to their immunity to electromagnetic interference, durability in harsh environments, multiplexing capability, and suitability for both localized and fully distributed measurements. In parallel, advances in machine learning (ML) have enabled new approaches for extracting actionable insights from large, high-dimensional sensing datasets. This paper presents a systematic review of FOS-based SHM systems integrated with ML across civil, transportation, energy, marine, and aerospace infrastructures. Following PRISMA 2020 guidelines, peer-reviewed studies were identified and synthesized to examine sensing principles, deployment configurations, data characteristics, and learning-based analytical strategies. Fiber optic technologies are categorized into point-based, quasi-distributed, and fully distributed systems, and their capabilities for capturing strain, temperature, and spatiotemporal structural responses are critically evaluated. ML approaches are examined from a task-oriented perspective, including damage detection, localization, severity assessment, environmental compensation, and prognosis, with emphasis on the alignment between sensing configurations and appropriate learning paradigms. Key challenges remain, particularly regarding large data volumes, environmental variability, limited labeled damage datasets, model generalization, and system-level integration. Emerging directions such as physics-informed and hybrid learning, transfer learning, uncertainty-aware modeling, and integration with digital twins are discussed as pathways toward more robust and scalable SHM systems. By jointly addressing sensing physics and data-driven intelligence, this review provides a structured reference and practical roadmap for advancing intelligent FOS-based SHM in next-generation infrastructure. Full article

Chalcones, a subclass of flavonoid-derived phenolic compounds, have demonstrated promising anthelmintic activity against parasitic nematodes. This study aimed to obtain insights into the biological effects a cis/trans mixture of chalcone and its geometric isomer, trans-chalcone, using RNA sequencing in the model organism Caenorhabditis elegans. Fourth-stage larvae (L4) were exposed to cis/trans-chalcone or trans-chalcone for 3 h, and total RNA was extracted for high-throughput sequencing. Transcriptomic analysis revealed that exposure to cis/trans-chalcone and trans-chalcone induced pronounced modulation of genes involved in lipid metabolism and repression of collagen and structural genes, potentially leading to defective extracellular matrix maintenance, thereby suggesting these combined effects as potential mechanisms underlying their anthelmintic activity. Also, metabolic and stress response pathways, with several genes implicated in detoxification and cellular defense, were markedly upregulated. These findings provide new insights into the molecular mechanisms affected by chalcones, advancing our understanding of their anthelmintic potential and supporting future drug development efforts. Full article

The demand for sustainable weed management and the limited discovery of new herbicide molecules have led to high interest in plant-derived bioherbicides, such as the water residues (WRs) from the hydrodistillation of aromatic plants, which contain biologically active secondary metabolites. Here, the bioherbicidal activity of WRs of four aromatic plant species was investigated. Chemical composition of WRs was determined by solid-phase microextraction (SPME) coupled to gas chromatography–mass spectrometry (GC-MS), and their effect was assessed on seed germination and seedling growth characteristics of Avena sterilis, Echinochloa crus-galli, and Zea mays. Five concentrations, i.e., 0, 10, 20, 50, and 100% (v/v), with 100% representing pure WR, were tested. Phenolic monoterpenes dominate WRs in oregano and thyme, and oxygenated monoterpenes in laurel and lavender. Germination and growth responses were dose-dependent and species-specific. Oregano and lavender WRs exhibited the strongest inhibitory effect, reducing weed germination by 82% and 79%, respectively. In contrast, laurel extracts showed weaker germination inhibition. Across all tested species, germination delays were observed, making WRs a promising candidate for weed control. The results also showed that WR reduced root growth by up to 95% and shoot growth by 70–80%. Maize exhibited greater tolerance than the weed species, maintaining higher germination. Overall, WRs represent a promising tool for integrated weed management. Full article

Food safety, nutrition, and public health are actual economic and medical problems. Sweetness is an important feature of food technology. Models for the sweetness of special organic compounds used in the food industry are suggested. The models are built using the CORAL software. New statistical coefficients of predictive potential are studied. These are the index of ideality of correlation (IIC) and correlation intensity index (CII). The effectiveness of using the IIC and CII has been tested in simulated sweetness via Monte Carlo optimization of correlation weights for molecular features extracted from Simplified Molecular Input Line Entry System (SMILES) strings. Both factors have been shown to improve the model’s statistical quality on the calibration and validation sets. However, this is accompanied by a decrease in the statistical quality of the training sets. Full article

Ascosphaera apis is a fungal pathogen of honeybee larvae and the primary cause of chalkbrood disease, which weakens bee colonies, impairing their ability to function effectively and making them more susceptible to other pathogens and environmental stressors. This study aimed to develop and validate an in-house matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral library for A. apis. A new MALDI-TOF MS library was constructed using reference Ascosphaera species and validated through whole-genome-based confirmation of 31 clinical isolates of A. apis. Three different protein extraction methods were tested and compared: liquid cultivation, formic acid–ethanol extraction and extended direct transfer. Our findings demonstrate that MALDI-TOF MS is a rapid and reliable tool for identifying A. apis under the tested laboratory conditions and within the analyzed strain set, with no misidentifications observed for the liquid cultivation and formic acid–ethanol extraction methods. The extended direct mycelium transfer method was slightly less effective but still showed a high sensitivity of 83.9%. This study provides a foundation for improving diagnostic approaches in the management of honeybee fungal diseases. Full article

Arrhythmia is a common and potentially life-threatening cardiovascular condition. Photoplethysmography (PPG) has emerged as a noninvasive alternative to electrocardiography for cardiac rhythm monitoring, yet most PPG-based methods remain limited to binary classification. In this study, a new deep learning approach is suggested for categorizing six arrhythmia types from PPG data: sinus rhythm (SR), premature ventricular contraction (PVC), premature atrial contraction (PAC), ventricular tachycardia (VT), supraventricular tachycardia (SVT), and atrial fibrillation (AF). The raw PPG signal is enhanced by extracting its first and second derivatives to capture morphological features not readily apparent in the original signal. A hybrid architecture, VGG-BiLSTM, is utilized, merging VGG convolutional layers for spatial features extraction with bidirectional long short-term memory layers for modeling temporal dependencies. A stratified data splitting strategy is further adopted to address class imbalance across arrhythmia types. A publicly available dataset containing 46,827 PPG segments from 91 individuals was employed to assess the effectiveness of the suggested technique. The method yielded an overall accuracy, sensitivity, specificity and F1 score of 88.7%, 78.5%, 97.6% and 80.5% correspondingly. Full article

The aim of this in vitro study was to evaluate the ability of the new strontium-containing composite, Stela (SDI, Victoria, Australia), to induce hydroxyapatite formation and promote remineralization of demineralized dentin, compared to SDR Flow+ (York, PA, USA). Twenty-four dentin slices (1 mm thick) were obtained from extracted wisdom teeth using a microtome and demineralized with 17% EDTA for 2 h. A layer of either Stela or SDR Flow+ was applied to each slice, allowed to set, and preserved in 0.1% thymol solution. Samples were analyzed at 1, 7, 14 and 28 days (n = 3 per group and time). Measurements were taken at baseline, after demineralization, and after application. Apatite formation was assessed using 'Fourier-transform infrared spectroscopy (FTIR), while changes in the Calcium/Phosphate (Ca/P) ratio were evaluated by Energy Dispersive Spectroscopy (EDX). Statistical comparisons were performed using the Wilcoxon test (p < 0.05). Both materials promoted carbonated hydroxyapatite formation and increases in calcium and phosphate. Stela exhibited an apatite peak (1420 cm⁻¹) as early as 24 h and significant increases in calcium and phosphate from day 7. SDR Flow+ reached its peak at 14 days and showed significant increases in the Ca/P ratio. By 28 days, both materials achieved comparable remineralization, confirming their effectiveness in treating demineralized dentine. Full article

In this research paper, we introduce an improved multiple-component decomposition technique based on the refined volume scattering models (MCSMRV) for polarimetric interferometric synthetic aperture radar (PolInSAR) system. The primary objective of this methodology is to address the issue of overestimation in volume scattering (OVS) and to clarify the mixed ambiguities associated with scattering mechanisms. Our approach incorporates an innovative inversion technique for rotation angles in urban areas, alongside the newly proposed volume scattering models. Furthermore, a refined Wishart mixture model (RWMM) is proposed for distinguishing building regions from non-building regions, which can effectively support the rational selection of volume scattering models. Additionally, the polarimetric interferometric similarity parameter (PISP) is employed to modify the volume scattering models for buildings with diverse orientation angles. To validate the effectiveness of MCSMRV, we utilize ESAR PolInSAR data and the PolInSAR data collected by the Aerospace Information Research Institute. Various mathematical methods are applied to assess the performance of MCSMRV. The experimental results clearly demonstrate that MCSMRV represents a robust method for characterizing the scattering mechanisms across different terrain types. Full article