Search Results (8,038)

Baicalin is a natural compound sourced from Scutellaria baicalensis which possesses various biological activities. To date, a large amount of research has been conducted on the antibacterial activity and related mechanisms of baicalin, making it a promising candidate for new broad-spectrum antibacterial drugs. However, the solubility of baicalin is limited. To improve its solubility and overcome the clinical application bottleneck, researchers have developed various solubilization techniques. Therefore, this article introduces the biological characteristics of baicalin; explores its effects as an antibacterial agent on bacterial biofilms, quorum sensing, virulence factors, inflammatory responses, and the immune system; and discusses the applications of nano-carrier loading technology, cyclodextrin inclusion technology, metal ion coordination and organometallic complexation technology, and dynamic covalent hydrogel assembly technology in improving the solubility of baicalin, thereby enhancing its antibacterial activity. Full article

This study presents a fire risk prediction framework applied to two conservation units within the Atlantic Forest biome (AFb): Serra da Gandarela National Park (PNSG), Minas Gerais, and Campos de Palmas Wildlife Refuge (RVSCP), Paraná. Daily climate data (2001–2023), remote sensing vegetation indices Normalized Difference Vegetation Index (NDVI) and Normalized Multi Band Drought Index (NMDI), fire foci, and estimates of soil volumetric moisture were integrated to analyze the climatic and environmental drivers of fire occurrence and to develop predictive models. Sea Surface Temperature (SST) anomalies in the Niño 3.4 region revealed the influence of El Niño–Southern Oscillation (ENSO) variability on local hydrometeorological dynamics. Vegetation indices and soil moisture data reinforced this relationship, with NMDI values below 0.4 and sharp declines in volumetric moisture indicating water stress during the dry season. Kernel density maps identified clusters of fire foci during this period, confirming the strong seasonality of fire occurrence. Based on climatic predictors and environmental indicators, fire risk indices were developed for each conservation unit and validated using independent data. Model performance showed moderate explanatory capacity, with coefficients of determination ranging from 0.53 to 0.68 and high agreement between estimated and observed values. Validation stratified by ENSO phases (Neutral, El Niño, and La Niña) demonstrated stable performance across contrasting climatic regimes, indicating temporal resilience of the modeling framework. Overall, the integration of climate data, spectral indices, and soil moisture information improves the ability to anticipate fire risk in Atlantic Forest conservation units, providing a useful tool to support prevention, monitoring, and decision-making in protected areas. Full article

Tomato landraces possess distinct flavors, colors, textures and aromas, making them suitable for traditional cuisine. Tomato landraces contain a wide range of genes, including those involved in fruit quality, that can be isolated and used in local breeding programs. In regions recognized as centers of origin, domestication and diversification, traditional farmers play an important role in the preservation of tomato landraces adapted to local conditions and agricultural practices, on the whole maintaining high genetic diversity. This work aimed to evaluate the effects of the crop cycle (C), genotype (G) and C × G interactions on the contents of soluble solids, reducing sugars, lycopene, total polyphenols, flavonoids, and vitamin C, as well as the pH and antioxidant activity, in fifteen tomato landraces (genotypes) undergoing phenotypic selection and a commercial tomato variety (control). All the varieties were grown in two crop cycles under uniform greenhouse management using a randomized block design with four repetitions. Fruit composition was analyzed with AOAC and spectrophotometric methods. Significant differences (p ≤ 0.01) were detected in the soluble solid content, pH, flavor and maturity indices, polyphenol and flavonoid contents, and antioxidant activity between C, G and C × G interactions. In contrast, titratable acidity, reducing sugars, lycopene and vitamin C did not differ between cycles. Coefficients of phenotypic and genotypic variation and broad-sense heritability (H²) ranged from 4.3 to 33.7, 2.0 to 19.0, and 3.2 to 63.5%, respectively. H² for bioactive compounds ranged from moderate to slightly high (16.3–38.8%). These findings, supported by laboratory analyses, suggest that genotypes under agronomic selection have potential as parents to enhance fruit quality in current and future breeding programs. Full article

Super-resolution reconstruction of remote sensing images has significant application value in fields such as smart cities, land monitoring, and traffic management. However, current super-resolution methods often overlook the differences between semantic and texture feature representations. This limitation makes it difficult to collaboratively preserve semantic structures and fine details during reconstruction, thereby affecting overall reconstruction quality. To address these challenges, this paper proposes a novel remote sensing image super-resolution network based on feature decoupling and differential window design, termed FDWD-Net. Specifically, we introduce an Adaptive Energy-driven Channel Selection module and a Multi-Directional Gradient-based Semantic–Texture Decoupling module to identify informative channels from the feature maps and decouple them into semantic and texture representations for independent optimization. Furthermore, we design a Differential Window-based Cross-scale Interaction module that dynamically adjusts window sizes based on local texture complexity, enabling adaptive feature modeling and effective multi-scale information interaction. Experimental results confirm that our method surpasses existing mainstream models on several remote sensing datasets. It also performs better in preserving structures and restoring detailed information. Full article

Knowledge about the condition of electrical equipment in energy networks is of great importance to network operators. Partial discharges are a key parameter for evaluating the health of the insulation. While a quantifiable PD measurement for offline tests is state of the art, it is costly and labour-intensive. It, therefore, makes sense to carry out permanent monitoring during operation. At the medium-voltage level in the European interconnected grid, comprehensive monitoring of PD is not implemented. This study presents a novel sensor concept that is used to detect PD in medium-voltage switchgear and cables: the so-called Magnetic Flux Concentrator Sensor (MFCS). It is an inductive sensor concept with high sensitivity in the frequency range of a few MHz, like well-established High-Frequency Current Transformers (HFCTs) but with better magnetic saturation properties in specific use cases. The highly permeable ferrite core of the MFCS is unconventionally shaped, resulting in a higher-saturation field strength. Therefore, this sensor is not driven into saturation by the operating currents of typical MV power cables. Using the MFCS and conventional HFCT in a suitable combination enables direction-selective PD detection. This work presents the sensor concept and the method for directional detection of the PD location, as analysed and evaluated theoretically and practically with laboratory experiments. Full article

The Industrial Internet of Things (IIoT) has become a cornerstone of modern industrial automation, enabling real-time monitoring, intelligent decision-making, and large-scale connectivity across cyber–physical systems. However, the growing scale, heterogeneity, and dynamic behavior of IIoT environments significantly expand the attack surface and challenge the effectiveness of conventional security mechanisms. In this paper, we propose DeepSense, a hybrid and adaptive anomaly and intrusion detection framework specifically designed for resource-constrained and heterogeneous IIoT deployments. DeepSense integrates three complementary components: DataSense, a realistic data pipeline and experimental testbed supporting synchronized sensor and network data processing; RuleSense, a lightweight rule-based detection layer that provides fast, deterministic, and interpretable anomaly screening at the edge; and NeuroSense, a learning-driven detection module comprising an adaptive ensemble of 22 machine learning and deep learning models spanning classical, neural, hybrid, and Transformer-based architectures. NeuroSense operates as a second detection stage that validates suspicious events flagged by RuleSense and enables both coarse-grained and fine-grained attack classification. To support rigorous and practical assessment, this work further introduces a comprehensive performance evaluation framework that extends beyond accuracy-centric metrics by jointly considering detection quality, latency, resource efficiency, and detection coverage, alongside an optimization-based process for selecting Pareto-optimal model ensembles under realistic IIoT constraints. Extensive experiments across diverse detection scenarios demonstrate that DeepSense exhibits strong generalization, lower false positive rates, and robust performance under evolving attack behaviors. The proposed framework provides a scalable and efficient IIoT security solution that meets the operational requirements of Industry 4.0 and the resilience-oriented objectives of Industry 5.0. Full article

Cultivated land, as a vital resource for human sustenance, requires region-specific protection strategies worldwide. Semantic segmentation technology for agricultural land remote sensing imagery offers a scientific foundation and decision-making support for cultivated land protection through accurate identification and dynamic monitoring. In China, the fragmented distribution, small parcel sizes, complex terrain, and indistinct boundaries of cultivated land pose challenges to the intelligent interpretation of high-resolution remote sensing (HRRS) imagery. Conventional semantic segmentation methods often struggle to address these complexities. To address this issue, we propose a hybrid network called STAR-Net (Swin Transformer Auxiliary Residual Structure) for semantic segmentation of agricultural land in HRRS imagery whose encoder integrates a Global-Local Feature Fusion Module to effectively merge complementary information from both branches. A Multi-Scale Aggregation Module within the decoder facilitates the fusion of shallow spatial details and deep semantic cues, enhancing the model’s ability to discriminate objects at varying scales. Using the LoveDA dataset, we show that STAR-Net generates the highest Intersection over Union (IoU) on the “Barren” and “Forest”, achieving the improvement of 9.88% and 7.05% respectively, while delivering comparable IoU performance on other categories. Overall performance improved by 0.46% in mIoU compared to state-of-the-art models. Across all target categories, the method also achieves the greatest count of leading segmentation metrics. Full article

Rare-earth elements including the fifteen lanthanides, from lanthanum (La) to lutetium (Lu), together with scandium (Sc) and yttrium (Y), can act either as matrix cations or as active luminescent centers when incorporated into host lattices. Owing to their relatively large ionic radii, high coordination numbers, and structural stability, ions such as La, Lu, Sc, Y, and gadolinium (Gd) typically serve as matrix cations in rare-earth vanadate (REVO₄)-based phosphors, while other trivalent lanthanide (Ln³⁺) ions act as active luminescent centers. These REVO₄ phosphors have proved to be good host lattices for optically active Ln³⁺ ions giving strong luminescence assigned to absorption of the vanadate (VO₄³⁻) groups, and the efficient energy transfer between host lattice and Ln³⁺ ions. The unique electronic configuration of Ln³⁺ ions, particularly their unpaired 4f electrons, makes them ideal for applications in luminescence, magnetism, electronic and magnetic relaxation, and catalysis. Due to their complementary luminescent characteristics, Ln³⁺-doped REVO₄ phosphors have attracted significant attention in recent years. Their unique optical properties make them highly valuable across a broad spectrum of applications. This paper provides a comprehensive review of the state of the art in Ln³⁺ (Eu³⁺, Sm³⁺, Tm³⁺, Er³⁺, Ho³⁺, Tb³⁺, Nd³⁺, and Yb³⁺)-doped REVO₄ (RE = Y, Gd, Lu, La) phosphors. It examines current synthesis approaches, alongside the development of advanced strategies, and explores structural characteristics, innovative designs, and luminescent behavior, including both downconversion and upconversion processes and sensing applications, of the Ln³⁺-doped REVO₄ phosphors. Full article

Water-level monitoring in rice paddies supports sustainable farming, responsible water management, and greenhouse gas emission mitigation. SAR-based remote sensing is an effective alternative for estimating water levels, especially in regions where optical observations are limited. This study evaluates ten ALOS-2/PALSAR-2 L-band SAR-derived polarimetric parameters for their contribution and effectiveness in water-level estimation across rice-growing phases using random forest regression in the Subang District, which is one of the largest rice-yield areas in West Java, Indonesia. Overall, L-band polarimetric information is clearly related to water-level dynamics throughout the rice-growing cycle, confirming its strong potential for quantitative water-level retrieval. The highest estimation accuracy was achieved by integrating all polarimetric parameter groups (MAE = 1.37 cm, RMSE = 1.79 cm, R² = 0.52, r = 0.73), indicating that no single group can adequately represent the complex scattering mechanisms governing water-level variability across an entire cropping season. Variable importance analysis shows a relatively uniform contribution (7.63–12.90%), suggesting synergies across parameters in water-level estimation. Phase-specific evaluation further reveals that Phase 2, corresponding to the vegetative-to-generative transition, is the optimal temporal window for L-band SAR-based water-level retrieval due to enhanced double-bounce scattering and reduced signal saturation. While Phase 2 data maximizes physical sensitivity and correlation, whole-phase modeling provides greater robustness and lower absolute errors, making it more suitable for L-band SAR-based operational water-level monitoring applications. Full article

Urban green spaces are increasingly threatened by rapid urban expansion, making their continuous monitoring and prediction essential for sustainable urban management. This study investigates the spatiotemporal dynamics of urban garden landscapes in Shiraz, Iran, by integrating multi-temporal Landsat imagery, GIS analysis, and CA–Markov modeling. Landsat data from 2003, 2013, and 2023 were processed to derive the Normalized Difference Vegetation Index (NDVI), which was classified into four vegetation-density categories to quantify land-cover transitions. A CA–Markov framework implemented in IDRISI TerrSet (Version 20.0) was then employed to simulate spatial dynamics and predict vegetation changes for 2033. Results reveal a significant expansion of non-vegetated areas from 711.93 ha in 2003 to 976.66 ha in 2023, accompanied by a decline in dense vegetation from 403.68 ha to 382.64 ha. Model projections indicate a further reduction in dense vegetation to 239.35 ha by 2033, suggesting ongoing fragmentation of urban green infrastructure driven by development pressures. By combining time-series remote sensing, GIS-based spatial analysis, and predictive modeling, this study provides an integrative framework for detecting, interpreting, and forecasting urban land-cover change. The findings offer evidence-based insights to support sustainable urban planning, green infrastructure protection, and climate-resilient city management in rapidly growing urban environments. Full article

Introduction: Financial support is a key driver of international student mobility. This study examines whether the financial incentives attracting international students to Hungary also translate into meaningful academic and social integration and a sense of belonging, comparing scholarship holders with self-funded students. Methods: A mixed-methods, cross-sectional online survey was conducted among international students enrolled in Hungarian higher education institutions (N = 232). The survey assessed motivations for choosing Hungary, academic and social integration, and willingness to recommend the country as a study destination. Group differences were analysed using independent-samples t-tests, Mann–Whitney U tests and multivariate analyses, while open-ended responses were examined using thematic analysis. Results: Scholarship programmes, academic quality, and Hungary’s relative affordability emerged as dominant motivational factors. While no significant difference was observed in overall academic integration (p = 0.127), scholarship recipients reported stronger inclusion within the Hungarian community (p = 0.032) and were markedly more likely to recommend Hungary (p < 0.001). Nonetheless, language barriers, limited interaction with host-country students, and social isolation persisted across groups, indicating that financial support alone does not ensure holistic engagement. Conclusion: Scholarship schemes yield the greatest impact when paired with institutional and social initiatives that actively foster integration, inclusion, and a sense of belonging. The study offers empirical insights from a non-traditional study destination, highlighting strategies to enhance international student experiences and strengthen Hungary’s competitiveness globally. Full article

Milltailings deposition areas in abandoned mines are inherently unstable and spatially extensive and heterogeneous, making regional-scale field investigations challenging under intense rainfall. With the advancement of space–airborne remote sensing technologies, large-scale surface deformation monitoring has become feasible. In this study, a 22.02 km² abandoned mine in Lingqiu County, Shanxi Province, was selected as a case site; during the late-July 2023 extreme rainfall event, the site experienced large-scale surface displacements. Surface deformation was interpreted using Sentinel-1 SBAS-InSAR data, combined with differential digital elevation models (DEMs) derived from UAV surveys before and after heavy rainfall. A bivariate spatial autocorrelation analysis was conducted to evaluate the spatial relationship between differential DEMs and InSAR-derived deformation. The results indicate that: (1) SBAS-InSAR revealed significant spatial heterogeneity of ground deformation, with pronounced subsidence observed in the milltailings deposits; (2) the bivariate spatial autocorrelation analysis yielded a Moran’s I value of 0.2, suggesting a weak but positive spatial correlation between the DEM differences and InSAR results, with dispersed correlation patterns; (3) hotspot analysis highlighted notable clustering of deformation, with approximately 27.84% of the study area showing strong deformation responses, while 25.81% represented low–low clusters with limited deformation. Beyond tailings-deposit settings, this workflow is also applicable to the regional investigation of rainfall-responsive deformation and debris-flow-related terrain change on natural slopes under global change, providing technical support for surface investigations and offering insights for disaster early warning and ecological restoration in similar regions. Full article

Structural colors generated by interference, diffraction, or light scattering offer vivid visual effects without dyes or electronic components, making them promising for flexible optical sensing. This work reports a simple stretch–plasma–release (S-P-R) strategy to fabricate wrinkled photonic elastomers (WPEs). The flexible periodic structures exhibit mechanically responsive structural colors, as tensile strain alters the grating period, generating optical signals that can be visualized and quantified by spectroscopy. The wrinkle period is tunable in the range of 0.4–3.42 μm by adjusting plasma power, exposure time, pre-stretch ratio, and film thickness. A dumbbell-shaped substrate design reduces edge-induced stress concentration. It shows improved wrinkle uniformity, with the coefficient of variation reduced from 6.64% to 2.74%, and experimental colors agreeing well with modified Bragg condition predictions. The reflection peak shows a significant shift from 356 nm to 658 nm with varying viewing angles. Patterned plasma treatment enables the selective generation of wrinkled structures, producing bright color patterns. The structural color can be fully erased at a critical strain of 20% and recovered upon release, remaining stable over multiple loading–unloading cycles. With excellent mechanical compliance and optical tunability, these materials are well-suited for integration with hydrogel-based systems and show promise for wearable devices, security marking, and anti-counterfeiting applications. Full article

As complex agricultural machinery, traditional combine harvesters face numerous challenges during operation due to their reliance on manual observation. To meet the demands of modern agriculture, intelligent combine harvesters have emerged. Intelligent sensing uses multi-sensor fusion and deep learning to monitor crop lodging, feed rate, loss rate, and impurity content. Under suboptimal conditions, multi-source fusion strategies improve perception reliability. Information processing and decision-making enable dynamic optimization of operational parameters and reduce harvest losses. Multi-machine coordination transforms single-machine operations into fleet control, while remote monitoring leverages a cloud edge collaboration architecture to enable status visualization, remote control, and predictive maintenance for faults. Unmanned operations utilize high-precision positioning and intelligent path planning to improve fleet efficiency and field coverage. However, the field still faces common challenges, including insufficient real-time processing capabilities for multi-source heterogeneous data, poor adaptability to complex agronomic scenarios, and limited economic feasibility. In this review, we examine the complete technology chain, which includes intelligent perception, intelligent decision-making and coordination, remote monitoring, and unmanned operations. We conduct a comparative analysis of the current state of these systems and the challenges they face, providing a systematic reference for future research and industrial applications. Full article

Vicarious calibration is a technique that makes use of radiometrically stable targets such as dry lakebeds, desert sites, and open grasslands for the post-launch calibration of a satellite sensor. Top-of-the-atmosphere radiances or reflectances are provided from those sites for the calibration of a sensor. The reflectance of a remote sensing vicarious calibration site is measured by ratioing the signal from a ground target to the signal from a reference target, often a white panel made of PTFE whose reflectance is known. When physically mapping a vicarious calibration site prior to a satellite sensor overflight, there can be elapsed times between the two measurements as great as 10 min. The solar illumination can vary on time scales relevant to the time between measurements of a ground target and a reference panel, impacting the variance in the measured reflectance. In this work, we explore the impact of a temporal delay between two measurements taken outdoors on the Type A uncertainties in their ratios. A factor of 3 reduction in the Coefficient of Variation of the ratio taken simultaneously versus sequentially with delays on the order of 10 min was realized. Implications for protocols employed to measure the surface reflectance at sites used for the vicarious calibration of aircraft and satellite sensors are discussed. Full article