Search Results (1,334)

This study explores the sustainable extraction and application of natural dyes from figs (Ficus carica) and Eucalyptus leaves using an aqueous alkaline medium. The dyeing process was optimized for cotton fabric using the exhaust-dyeing method. Fabrics dyed with Ficus carica extract and its blend with Eucalyptus exhibited enhanced color strength, excellent crocking fastness (rated 4–5), and good washing fastness (rated 3–4 on the gray scale). The use of Aloe barbadensis Miller as a bio-mordant significantly improved dye fixation, resulting in deeper, earthy shades, such as green, yellow–green, and yellowish brown. The highest K/S value (5.85) was recorded in samples treated with a mordant, sodium chloride (NaCl), and the combined dye extracts, indicating a synergistic effect among the components. Mosquito repellency tests revealed that treated fabrics exhibited up to 70% repellency, compared to just 20% in undyed samples. Antibacterial testing against E. coli showed that dyed fabrics achieved over 80% bacterial reduction after 24 h, indicating promising antimicrobial functionality. Air permeability slightly decreased post-dyeing due to the potential shrinkage in cotton fabrics. Furthermore, adsorption studies showed a removal efficiency of 57% for Ficus carica dye on graphene oxide (GO) under ultrasonication. These findings confirm the potential of GO as an effective adsorbent material for treating wastewater from natural textile dyes. Overall, the study highlights the environmental safety, functional performance, and multifunctional advantages of plant-based dyeing systems in sustainable textile applications. Full article

Accurate assessment of terrestrial ecosystem carbon storage is essential for understanding the global carbon cycle and informing climate change mitigation strategies. However, traditional estimation models face significant challenges in complex mountainous regions due to difficulties in data acquisition and high ecosystem heterogeneity. This study focuses on the Nanling Mountains in Guangdong Province, China, utilizing the Google Earth Engine (GEE) platform to integrate multi-source remote sensing data (Sentinel-1/2, ALOS, GEDI, MODIS), topographic/climatic variables, and field-collected samples. We employed machine learning models to achieve high-precision prediction and high-resolution mapping of ecosystem carbon storage while also analyzing spatial differentiation patterns. The results indicate that the Random Forest algorithm outperformed Gradient Boosting Decision Tree and Classification and Regression Tree (CART) algorithms by suppressing overfitting through dual randomization. The integration of multi-source data significantly enhanced model performance, achieving a coefficient of determination (R²) of 0.87 for aboveground biomass (AGB) and 0.65 for soil organic carbon (SOC). Integrating precipitation, temperature, and topographic variables improved SOC prediction accuracy by 96.77% compared to using optical data alone. The total carbon storage reached 404 million tons, with forest ecosystems contributing 96.7% of the total and soil carbon pools accounting for 60%. High carbon density zones (>160 Mg C/ha) were mainly concentrated in mid-elevation gentle slopes (300–700 m). The proposed integrated “optical-radar-topography-climate” framework offers a scalable and transferable solution for monitoring carbon storage in complex terrains and provides robust scientific support for carbon sequestration planning in subtropical mountain ecosystems. Full article

Tropical natural forests play a crucial role in regulating the climate and maintaining global ecosystem functions. However, they face significant challenges due to human activities and climate change. Accurate classification of these forests can help reveal their spatial distribution patterns and support conservation efforts. This study employed four machine learning algorithms—random forest (RF), support vector machine (SVM), Logistic Regression (LR), and Extreme Gradient Boosting (XGBoost)—to classify tropical rainforests, tropical monsoon rainforests, tropical coniferous forests, broadleaf evergreen forests, and mangrove forests on Hainan Island using optical and synthetic aperture radar (SAR) multi-source remote sensing data. Among these, the XGBoost model achieved the best performance, with an overall accuracy of 0.89 and a Kappa coefficient of 0.82. Elevation and red-edge spectral bands were identified as the most important features for classification. Spatial distribution analysis revealed distinct patterns, such as mangrove forests occurring at the lowest elevations and tropical rainforests occupying middle and low elevations. The integration of optical and SAR data significantly enhanced classification accuracy and boundary recognition compared to using optical data alone. These findings underscore the effectiveness of machine learning and multi-source data for tropical forest classification, providing a valuable reference for ecological monitoring and sustainable management. Full article

Anthracnose, a significant disease of aloe with characteristics of contact transmission, poses a considerable threat to the economic viability of aloe cultivation. To address the challenges of accurately detecting and classifying crop diseases in complex environments, this study proposes an enhanced algorithm, YOLOv11-seg-DEDB, based on the improved YOLOv11-seg model. This approach integrates multi-scale feature enhancement and a dynamic attention mechanism, aiming to achieve precise segmentation of aloe anthracnose lesions and effective disease level discrimination in complex scenarios. Specifically, a novel Disease Enhance attention mechanism is introduced, combining spatial attention and max pooling to improve the accuracy of lesion segmentation. Additionally, the DCNv2 is incorporated into the network neck to enhance the model’s ability to extract multi-scale features from targets in challenging environments. Furthermore, the Bidirectional Feature Pyramid Network structure, which includes an additional p2 detection head, replaces the original PANet network. A more lightweight detection head structure is designed, utilizing grouped convolutions and structural simplifications to reduce both the parameter count and computational load, thereby enhancing the model’s inference capability, particularly for small lesions. Experiments were conducted using a self-collected dataset of aloe anthracnose infected leaves. The results demonstrate that, compared to the original model, the improved YOLOv11-seg-DEDB model improves segmentation accuracy and mAP@50 for infected lesions by 5.3% and 3.4%, respectively. Moreover, the model size is reduced from 6.0 MB to 4.6 MB, and the number of parameters is decreased by 27.9%. YOLOv11-seg-DEDB outperforms other mainstream segmentation models, providing a more accurate solution for aloe disease segmentation and grading, thereby offering farmers and professionals more reliable disease detection outcomes. Full article

With the rapid increase in global lithium demand, the exploration of newly discovered lithium in the bauxite of the Wenshan area in southeastern Yunnan has become increasingly important. However, the current research on clay-type lithium in the Wenshan area has primarily focused on local exploration, and large-scale predictive metallogenic studies remain limited. To address this, this study utilized multi-source remote sensing data from ZY1-02D and ASTER, combined with ALOS 12.5 m DEM and Sentinel-2 imagery, to carry out remote sensing mineral identification, structural interpretation, and prospectivity mapping for clay-type lithium in the Wenshan area. This study indicates that clay-type lithium in the Wenshan area is controlled by NW, EW, and NE linear structures and are mainly distributed in the region from north of the Wenshan–Malipo fault to south of the Guangnan–Funing fault. High-value areas of iron-rich silicates and iron–magnesium minerals revealed by ASTER data indicate lithium enrichment, while montmorillonite and cookeite identification by ZY1-02D have strong indicative significance for lithium. Field verification samples show the highest Li₂O content reaching 11,150 μg/g, with six samples meeting the comprehensive utilization criteria for lithium in bauxite (Li₂O ≥ 500 μg/g) and also showing an enrichment of rare earth elements (REEs) and gallium (Ga). By integrating stratigraphic, structural, mineral identification, geochemical characteristics, and field verification data, ten mineral exploration target areas were delineated. This study validates the effectiveness of remote sensing technology in the exploration of clay-type lithium and provides an applicable workflow for similar environments worldwide. Full article

This study assesses the accuracy of flood extent predictions in five U.S. watersheds. We generated flood maps for four return periods using various digital elevation models (DEMs)—FABDEM, SRTM, ALOS, and USGS 3DEP—and two versions of the GEOGLOWS River Forecast System (RFS) hydrography. These comparisons are notable because they build on operational global hydrology models so subsequent work can develop global modeled flood products. Models were made using the Automated Rating Curve (ARC) and Curve2Flood tools. Accuracy was measured against USGS reference maps using the F-statistic. Our results show that flood map accuracy generally increased with higher return periods. The most consistent and reliable improvements in accuracy occurred when both the DEM and hydrography datasets were upgraded to higher-resolution sources. While DEM improvements generally had a greater impact, hydrography refinements were more important for lower return periods when flood extents were the smallest. Generally, DEM resolution improved accuracy metrics more as the return period increased and hydrography and bare earth DEMs mattered more as the return period decreased. There was a 38.9% increase in the mean F-statistic between the two principal pairings of interest (FABDEM-RFS2 and SRTM 30 m DEM-RFS1). FABDEM’s bare-earth representation combined with RFS2 sometimes outperformed higher-resolution non-bare-earth DEMs, suggesting that there remains a need for site-specific investigation. Using ARC and Curve2Flood with FABDEM and RFS2 is a suitable baseline combination for general flood extent application. Full article

Aloe vera gel is a valuable raw material used in the cosmetic industry for its skin care properties. The present study analyzed the effects of the fermentation of aloe vera gel with a tea fungus kombucha, which is a symbiotic consortium of bacteria and yeast, carried out for 10 and 20 days (samples F10 and F20, respectively). The resulting ferments and unfermented gel were subjected to chromatographic analysis to determine the content of biologically active compounds. The permeability and accumulation of these compounds in pig skin were evaluated. In addition, the methods of DPPH, ABTS and the determination of intracellular free radical levels in keratinocytes (HaCaT) and fibroblasts (HDF) cell lines were used to determine antioxidant potential. The results showed a higher content of phenolic acids and flavonoids and better antioxidant properties of the ferments, especially after 20 days of fermentation. Cytotoxicity tests against HaCaT and HDF cells confirmed the absence of toxic effects; moreover, samples at the concentrations tested (mainly 10 and 25 mg/mL) showed cytoprotective effects. The analysis of enzymatic activity (collagenase, elastase and hyaluronidase) by the ELISA technique showed higher levels of inhibition for F10 and F20. The kombucha ferments also exhibited better moisturizing properties and lower levels of transepidermal water loss (TEWL), confirming their cosmetic potential. Full article

Rapid and comprehensive assessment of building damage caused by earthquakes is essential for effective emergency response and rescue efforts in the immediate aftermath. Advanced technologies, including real-time simulations, remote sensing, and multi-sensor systems, can effectively enhance situational awareness and structural damage evaluations. However, most existing methods rely on isolated time snapshots, and few studies have systematically explored the continuous, time-scaled integration and update of building damage estimates from multiple data sources. This study proposes a stepwise framework that continuously updates time-scaled, single-damage estimation outputs using the best available multi-sensor data for estimating earthquake-induced building damage. We demonstrated the framework using the 2024 Noto Peninsula Earthquake as a case study and incorporated official damage reports from the Ishikawa Prefectural Government, real-time earthquake building damage estimation (REBDE) data, and satellite-based damage estimation data (ALOS-2-building damage estimation (BDE)). By integrating the REBDE and ALOS-2-BDE datasets, we created a composite damage estimation product (integrated-BDE). These datasets were statistically validated against official damage records. Our framework showed significant improvements in accuracy, as demonstrated by the mean absolute percentage error, when the datasets were integrated and updated over time: 177.2% for REBDE, 58.1% for ALOS-2-BDE, and 25.0% for integrated-BDE. Finally, for stepwise damage estimation, we proposed a methodological framework that incorporates social media content to further confirm the accuracy of damage assessments. Potential supplementary datasets, including data from Internet of Things-enabled home appliances, real-time traffic data, very-high-resolution optical imagery, and structural health monitoring systems, can also be integrated to improve accuracy. The proposed framework is expected to improve the timeliness and accuracy of building damage assessments, foster shared understanding of disaster impacts across stakeholders, and support more effective emergency response planning, resource allocation, and decision-making in the early stages of disaster management in the future, particularly when comprehensive official damage reports are unavailable. Full article

The development of bio-based adhesives as sustainable alternatives to synthetic formulations presents a significant opportunity for advancing environmental sustainability in packaging applications. This research aimed to develop and evaluate a bio-based adhesive derived from bacterial nanocellulose (BNC), aloe vera and its mixtures as a potential replacement for commercial synthetic adhesives. Aloe vera, selected for its polysaccharide-rich composition, served as a natural polymeric matrix, while BNC contributed reinforcing properties. The adhesive formulations, with and without BNC, were compared to a commercial adhesive to assess their mechanical performance. T-peel and shear tests were conducted on smooth and rough paper substrates to evaluate adhesive strength. The bio-based adhesive incorporating BNC demonstrated superior shear and peel strength on rough substrates due to enhanced mechanical interlocking within the fibrous structure of paper, whereas performance on smooth surfaces was hindered by uneven BNC distribution, reducing adhesive-substrate interaction. Although the commercial adhesive achieved higher absolute maximum force values, the bio-based formulation exhibited comparable mechanical stability under specific conditions. These findings underscore the influence of substrate properties and application methods on adhesive performance, highlighting the potential of bio-based adhesives in packaging applications and the need for further formulation optimization to fully realize their advantages over traditional synthetic adhesives. Full article

Water body detection in synthetic aperture radar (SAR) imagery plays a critical role in applications such as disaster response, water resource management, and environmental monitoring. However, it remains challenging due to complex background interference in SAR images. To address this issue, a bi-encoder and hybrid feature fuse network (BiEHFFNet) is proposed for achieving accurate water body detection. First, a bi-encoder structure based on ResNet and Swin Transformer is used to jointly extract local spatial details and global contextual information, enhancing feature representation in complex scenarios. Additionally, the convolutional block attention module (CBAM) is employed to suppress irrelevant information of the output features of each ResNet stage. Second, a cross-attention-based hybrid feature fusion (CABHFF) module is designed to interactively integrate local and global features through cross-attention, followed by channel attention to achieve effective hybrid feature fusion, thus improving the model’s ability to capture water structures. Third, a multi-scale content-aware upsampling (MSCAU) module is designed by integrating atrous spatial pyramid pooling (ASPP) with the Content-Aware ReAssembly of FEatures (CARAFE), aiming to enhance multi-scale contextual learning while alleviating feature distortion caused by upsampling. Finally, a composite loss function combining Dice loss and Active Contour loss is used to provide stronger boundary supervision. Experiments conducted on the ALOS PALSAR dataset demonstrate that the proposed BiEHFFNet outperforms existing methods across multiple evaluation metrics, achieving more accurate water body detection. Full article

Ravine forests are key to conserving biodiversity and maintaining ecosystem processes in fragmented landscapes. Here, we evaluated the phenology of plant species in the Los Nogales Nature Sanctuary (Lo Barnechea, Chile) using Sentinel-2 images (2019–2024) and the Alos Palsar DEM (12.5 m). We calculated the Normalized Difference Vegetation Index (NDVI), the Topographic Position Index (TPI), and Diurnal Anisotropic Heat (DAH) to assess vegetation dynamics across different topographic and thermal gradients. Generalized Additive Models (GAM) revealed that tree species exhibited more stable, regular seasonal NDVI trajectories, while shrubs showed moderate fluctuations, and herbaceous species displayed high interannual variability, likely reflecting sensitivity to climatic events. Spatial analysis indicated that trees predominated on steep slopes and higher elevations, herbs were concentrated in low-lying, moisture-retaining areas, and shrubs were more common in areas with higher thermal load. These findings highlight the significant role of terrain and temperature in shaping plant phenology and distribution, underscoring the utility of remote sensing and topographic indices for monitoring ecological processes in complex mountainous environments. Full article

The envelopes of Red Supergiants (RSGs) have a unique chemical environment not seen in other types of stars. They foster an oxygen-rich synthesis but are tempered by sporadic and chaotic mass loss, which distorts the envelope and creates complex outflow sub-structures consisting of knots, clumps, and arcs. Near the stellar photosphere, molecules and grains form under approximate LTE conditions, as predicted by chemical models. However, the complicated outflows appear to have distinct chemistries generated by shocks and dust destruction. Various RSG envelopes have been probed for their molecular content, mostly by radio and millimeter observations; however, VY Canis Majoris (VY CMa) and NML Cygni (NML Cyg) display the highest chemical complexity, and also the most complicated envelope structure. Thus far, over 29 different molecules have been identified in the envelopes of RSGs. Some molecules are common for circumstellar gas, including CO, SiO, HCN and H₂O, which have abundances of ∼10⁻⁶–10⁻⁴, relative to H₂. More exotic oxides have additionally been discovered, such as AlO, AlOH, PO, TiO₂, and VO, with abundances of ∼10⁻⁹–10⁻⁷. RSG shells support intricate maser emission in OH, H₂O and SiO, as well. Studies of isotope ratios in molecules suggest dredge-up at least into the H-burning shell, but further exploration is needed. Full article

Aim: To prepare two different kinds of wound care creams containing plant extracts and examine their effectiveness in comparison with a placebo cream and a commercial wound care cream, Madecassol^®. Methods: The two cream formulations were developed using the same placebo cream (PC) as base cream. One formulation contained balsam of oriental sweet gum, or Levant storax, named as Levant Storax Cream (LSC); the other contained oil of Calendula, extract of St. John’s Wort, aescin (an extract of horse chestnut), and freeze-dried powder from Aloe vera (L.) Burm. f. leaf juice, designated as Complex Cream (CC). In the characterization of the creams, organoleptic properties, pH, viscosity, size distribution, and zeta potential of oil globules were measured. Furthermore, the stability of the creams was assessed under different environmental conditions. In vitro studies were performed by using an excisional wound model in rats to assess the potential of the creams for stimulating wound healing. The efficacy of LSC and CC was compared with a commercial reference cream, Madecassol^® (M), and the placebo control. The study was also designed with a negative control group of rats that were not treated but handled the same way as the other treatment groups. The wound contraction rate, total skin thickness recovery, and results of histopathological parameter examinations were used to compare the effectiveness of the treatments. Results: The stability of formulated creams confirmed that they were stable for the duration of the study. In vivo studies showed that rats treated with LSC achieved the highest wound healing rates when compared with the other groups. A better response was recorded for the CC-treated population when compared to both control and placebo groups, but there was no significant difference seen in healing score between CC and M groups. Full article

Aloin, a kind of active phenolic component, is sourced from Aloe vera. Recently, the determination of aloin has received enormous attention, owing to its positive performance (including anti-tumor, antibacterial, detoxification, liver protection, anti-stomach damage, and skin protection activities) and painful side effects (increased carcinogenicity caused by excessive use of aloin) impacting human health. This investigation was inspired by the good fluorescence properties of carbon dots (CDs); CD-based sensors have aroused a great deal of interest due to their excellent sensitivity and selectivity. Thus, it is of great significance to develop novel CD-based sensors for aloin determination. Herein, N,F-CDs were designed and synthesized through a convenient hydrothermal strategy; the synthesized N,F-CDs possessed good fluorescence performance and a small particle size (near 4.3 nm), which demonstrated the successful preparation of N,F-CDs. The resulting N,F-CDs possessed a large Stokes shift and could emit a highly stable green fluorescence. The fluorescence of the N,F-CDs could be effectively quenched by aloin through the inner filter effect. Furthermore, the synthesis procedure was easy to operate. Finally, the N,F-CD-coated test strips were fabricated and combined with a miniaturized fluorimeter for the fluorescence detection of aloin via the inner filter effect for the first time. The N,F-CD-coated test strips were fabricated and used for the fluorescence sensing of aloin, and the results were compared with a typical ultraviolet (UV) method. The N,F-CD-coated test strips exhibited high recovery (96.9~106.1%) and sensitivity (31.8 nM, n = 3), good selectivity, low sample consumption (1 μL), high speed (5 min), good stability, and anti-interference properties. The results indicate that N,F-CD-coated test strips are applicable for the quantitative determination of aloin in bovine serum, orange juice, and urine samples. Full article

The airborne Synthetic Aperture Radar (SAR) has the advantages of high-precision real-time observation of wave height variations and portability in the high frequency band, such as the Ku band. In view of the Four Fast Fourier Transform (4-FFT) algorithm, combined with a Gaussian operator, a Laplacian of Gaussian (LoG) Phase Unwrapping (PU) expression was derived. Then, an Adaptive LoG (ALoG) algorithm was proposed based on adaptive variance, further optimizing the algorithm through iteration. Building the models of Kelvin wake on the sea surface and height to phase, the interferometric phase of wave height can be simulated. These PU algorithms were qualitatively and quantitatively evaluated. The Principal Component Analysis (PCA) scores of the ALoG iteration (ALoGI) algorithm are the best under the tested noise levels of the simulation. Through a simulation experiment, it has been proven that the superiority of the ALoGI algorithm in high spatial resolution inversion for the sea-ship surface height of the Kelvin wake, with good stability and noise resistance. Full article