Search Results (1,076)

This study assesses how satellite clock products affect Precise Point Positioning (PPP) for GPS, Galileo, and GPS+Galileo. Multi-GNSS data at 30 s were processed for 12 global IGS stations over one week in 2025, with each day split into eight independent three-hour sessions. SP3 clocks (ORB, 5 min) were compared with dedicated CLKs (CLO, 5 s, 30 s, 5 min) across final (FIN), rapid (RAP), and ultra-rapid (ULT; observed/predicted) product lines from multiple analysis centers. Two timing strategies were tested: nearest-epoch sampling (CLOCK0) and linear interpolation (CLOCK1). CLO consistently delivered the lowest 2D/3D errors and the fastest convergence. ORB degraded accuracy by a few millimeters and extended convergence by ~5–10 min, most notably for GPS. With 5 min clocks, CLOCK1 yielded small gains for Galileo but often hurt GPS; with 30 s clocks, interpolation was immaterial; 5 s clocks offered no measurable benefit. FIN outperformed RAP; OPS slightly outperformed MGEX; ESA/GFZ ranked highest. ULT solutions were weaker, especially in the predicted half. Zenith tropospheric delay (ZTD) biases were negligible; variance was smallest for GPS+Galileo with CLO (~7–10 mm), increased by ~1–2 mm with ORB, and was largest in ULT. Dense, high-quality clock products remain essential for reliable PPP. Full article

Rapid, sensitive, and environmentally sustainable spectrophotometric methods for the determination of nitrite (${{\mathrm{N}\mathrm{O}}_2}^{-}$) in environmental specimens are proposed. The presented procedures are grounded in the diazotization of sulphathiazole (STZ), followed by coupling with rhodanine (RDN) or 7-hydroxycoumarin (7-HC) in an alkaline medium, and the results were studied. This reaction gave an intense soluble red color at 504 nm and a pale red color at 525 nm for RDN and 7-HC, respectively. The conditions producing the maximum performance and other important analytical criteria in relation to the proposed procedures were investigated to enhance their sensitivity. Beer’s law was abided by for ${{\mathrm{N}\mathrm{O}}_2}^{-}$ over the concentration ranges of 0.08–2.0 µg mL⁻¹ and 0.04–2.4 µg mL⁻¹ using RDN and 7-HC, respectively. The lower limit of detection (LLOD), lower limit of quantification (LLOQ), molar absorptivity (ε), and Sandell’s sensitivity were calculated as follows: 0.0303 µg mL⁻¹, 0.0918 µg mL⁻¹, 4.20 × 10⁴ L mol⁻¹ cm⁻¹, and 1.63 × 10⁻⁶ µg cm⁻² (in the case of RDN); and 0.0387 µg mL⁻¹, 0.1172 µg mL⁻¹, 6.90 × 10⁴ L mol⁻¹ cm⁻¹, and 1.00 × 10⁻⁶ µg cm⁻² (in case of 7-HC). Furthermore, the ecological implications were assessed using three green assessment methodologies: Analytical Eco-Scale (ESA), Analytical GREEnness metric (AGREE), and Green Analytical Procedure Index (GAPI). Thus, our proposed procedures are fully validated and implemented in order to carry out ${{\mathrm{N}\mathrm{O}}_2}^{-}$ quantification in the selected ecological samples (water and soil samples). Full article

The Bactericidal action of β-lactam antibiotics is related to covalent modification of transpeptidases, enzymes that take part in the synthesis of bacterial cell wall. The β-lactam moiety mimics the transpeptidase substrate and irreversibly inhibits the enzyme. In penicillin and cephalosporin, the β-lactam ring is coupled with a five-membered thiazolidine ring or a six-membered dihydrothiazine ring, respectively. In the case of penicillins, such conjunction causes higher tension of this bicyclic moiety; therefore, the β-lactam ring can be hydrolyzed in certain conditions, inactivating the antibiotic. Serum albumin is known for its drug binding capabilities, which enable it to transport pharmaceuticals through the circulatory system. Penicillins and cephalosporins are no exception in this aspect, and they are also carried by serum albumin in the bloodstream. In this study, we structurally investigate the ability of three serum albumins—equine (ESA), caprine (CSA), and ovine (OSA)—to bind two penicillins, ampicillin (Amp) and oxacillin (Oxa), and two cephalosporins, cefaclor (Cef) and cephalosporin C (Csc). The crystal structures of these mammalian serum albumin complexes shed new light on the albumin binding properties of β-lactam antibiotics, showing one common binding site for Amp, Oxa, and Cef in Fatty Acid Site 6 (FA6), and a second cefaclor molecule bound in domain I of the equine serum albumin. It was surprising that these antibiotics are not bound in the main drug binding site. However, cephalosporin C is bound in OSA Drug Site 1 (DS1). Full article

Remote sensing provides an efficient approach for monitoring ecosystem dynamics in the arid and semi-arid regions of Xinjiang, yet traditional forest-land extraction methods (e.g., spectral indices, threshold segmentation) show limited adaptability in complex environments affected by terrain shadows, cloud contamination, and spectral confusion with grassland or cropland. To overcome these limitations, this study used three convolutional neural network-based models (FCN, DeepLabV3+, and PSPNet) for accurate forest-land extraction. Four tri-band training datasets were constructed from Sentinel-2 imagery using combinations of visible, red-edge, near-infrared, and shortwave infrared bands. Results show that the FCN model trained with B4–B8–B12 achieves the best performance, with an mIoU of 89.45% and an mFscore of 94.23%. To further assess generalisation in arid landscapes, ESA WorldCover and Dynamic World products were introduced as benchmarks. Comparative analyses of spatial patterns and quantitative metrics demonstrate that the FCN model exhibits robustness and scalability across large areas, confirming its effectiveness for forest-land extraction in arid regions. This study innovatively combines band combination optimization strategies with multiple deep learning models, offering a novel approach to resolving spectral confusion between forest areas and similar vegetation types in heterogeneous arid ecosystems. Its practical significance lies in providing a robust data foundation and methodological support for forest monitoring, ecological restoration, and sustainable land management in Xinjiang and similar regions. Full article

Vegetation degradation in arid and semi-arid regions is intensifying due to rising temperatures, declining rainfall, soil exposure, and persistent human pressures. Drylands cover over 41% of the global land surface and support nearly two billion people, making their degradation a major environmental and socio-economic concern. However, many remote sensing and GIS-based assessment approaches remain fragmented and difficult to reproduce. This study proposes a Computational Sustainability Framework for vegetation degradation assessment that integrates multi-source satellite data, biophysical indicators, automated geospatial preprocessing, and the Analytical Hierarchy Process (AHP) within a transparent and reproducible workflow. The framework comprises four phases: data preprocessing, indicator extraction and normalization, AHP-based modeling, and spatial classification with qualitative validation. The framework was applied to the Al-Khunfah and Harrat al-Harrah Protected Areas in northern Saudi Arabia using multi-source datasets for the January–April 2023 period, including Sentinel-2, Landsat-8, CHIRPS precipitation, ESA-CCI land cover, FAO soil data, and SRTM DEM. High degradation zones were associated with low NDVI (<0.079), high BSI (>0.276), and elevated LST (>49 °C), whereas low degradation areas were concentrated near wadis and relatively more fertile soils. Overall, the proposed framework provides a scalable and interpretable tool for early-stage vegetation degradation screening in arid environments, supporting the prioritization of areas for ecological investigation and restoration planning. Full article

The pokeweed (Phytolacca americana L.) plant is native to North America and contains bioactive compounds with medicinal potential, particularly phenolics and saponins. This study enhanced the production of secondary metabolites in pokeweed callus cultures using sodium chloride (NaCl) and polyethylene glycol (PEG) as elicitors under aseptic conditions. Pokeweed seeds were cultured on Murashige and Skoog (MS) medium for 8 weeks. Fully expanded leaves from the second to third position from the shoot were excised and induced to form calli on MS medium supplemented with 2 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D) for 5 weeks. Fully developed calli were elicited with PEG6000 at concentrations of 0, 1.25, 2.5, and 5% (w/v) in combination with NaCl at concentrations of 0, 100, 200, and 300 mM for 15 days. Callus growth was recorded, followed by drying and extraction using methanol (MeOH) for biochemical analysis. Calli elicited with 2.5% PEG and 300 mM NaCl exhibited the highest total phenolic content (TPC) (21.063 µg GAE/mg DW) and total flavonoid content (TFC) (1.927 µg QUE/mg DW). The highest antioxidant activities determined by the 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS), and ferric ion reducing antioxidant potential (FRAP) assays were 0.998, 1.574, and 0.998 µg TE/mg DW, respectively. The elicitation of pokeweed calli with 300 mM NaCl yielded the highest amount of Esculentoside A (EsA) (15.753 µg/mg DW). All the elicitor treatments significantly enhanced metabolite accumulation compared to the control group (p < 0.05). The findings indicated that elicitation with PEG and NaCl effectively enhanced the production of secondary metabolites in P. americana callus cultures. This study offers a promising alternative approach for utilizing P. americana in pharmaceutical and medicinal applications. Full article

Within the field of the structural monitoring of bridges, numerous technologies and methodologies have been developed. Among these, methods based on synthetic aperture radar (SAR) which utilise satellite data from missions such as Sentinel-1 (European Space Agency-ESA) and COSMO-SkyMed (Agenzia Spaziale Italiana—ASI) to capture displacements, temperature-related changes, and other geophysical measurements have gained increasing attention. However, SAR has yet to establish its value and potential fully; its broader adoption hinges on consistently demonstrating its robustness through recurrent applications, well-defined use cases, and effective strategies to address its inherent limitations. This study presents a systematic literature review (SLR) conducted in accordance with key stages of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. An initial corpus of 1218 peer-reviewed articles was screened, and a final set of 25 studies was selected for in-depth analysis based on citation impact, keyword recurrence, and thematic relevance from the last five years. The review critically examines SAR-based techniques—including Differential Interferometric SAR (DInSAR), multi-temporal InSAR (MT-InSAR), and Persistent Scatterer Interferometry (PSI), as well as approaches to integrating SAR data with ground-based measurements and complementary digital models. Emphasis is placed on real-world case studies and persistent technical challenges, such as atmospheric artefacts, Line-of-Sight (LOS) geometry constraints, phase noise, ambiguities in displacement interpretation, and the translation of radar-derived deformations into actionable structural insights. The findings underscore SAR’s significant contribution to the structural health monitoring (SHM) of bridges, consistently delivering millimetre-level displacement accuracy and enabling engineering-relevant interpretations. While standalone SAR-based techniques offer wide-area monitoring capabilities, their full potential is realised only when integrated with complementary procedures such as thermal modelling, multi-sensor validation, and structural knowledge. Finally, this document highlights the persistent technical constraints of InSAR in bridge monitoring—including measurement ambiguities, SAR image acquisition limitations, and a lack of standardised, automated workflows—that continue to impede operational adoption but also point toward opportunities for methodological improvement. Full article

The article analyses the direction and intensity of changes occurring in agriculture in mountain areas in Europe between 2000 and 2022. For the calculations, the ESA CCI Land Cover global land-use map set was used. This dataset was established by the European Space Agency (ESA) through the classification of satellite images from sources (MERIS, AVHRR, SPOT, PROBA, and Sentinel-3). In the next step, the organisational features and economic performance of farms located in mountain areas of the European Union were determined for the period 2004–2022. For this purpose, data from the European Farms Accountancy Data Network (FADN-FSDN) were used. Subsequently, using Poland as a case study, the capacity of mountain agriculture to implement key environmental interventions under the Common Agricultural Policy (CAP) 2023–2027 was assessed. The results highlight the varying directions and intensity of organisational changes occurring in mountain agriculture across Europe. They also show that farms can operate successfully in these areas, although their economic situation varies between EU countries. The findings indicate the need for further adaptation of CAP instruments to better reflect the ecological and economic conditions of mountain areas. Strengthening support mechanisms for these regions within the current and future CAP is of crucial importance for protecting biodiversity, promoting sustainable land use, and maintaining the socio-environmental functions of rural mountain landscapes. Our study highlights that the CAP for mountain farms should be targeted, long-term, and compensatory, so as to compensate for the naturally unfavorable farming conditions and support their multifunctional role. The most important assumptions of CAP for mountain farms are a fair system of compensatory payments (LFA/ANCs), support for local and high-quality production, income diversification, and investments adapted to mountain conditions. Full article

The protection of natural resources, particularly groundwater, is essential for the sustainability of rural environments, especially when urban centers rely on this water for consumption. The objective of this study was to evaluate the occurrence, seasonal distribution, and associated risk of pesticide residues in groundwater in a region of intensive farming in the Duero river basin (Spain). A total of 40 pesticides and 7 degradation products were analyzed at 20 sampling points over four campaigns conducted in 2018. Overall, twenty-one compounds were detected, including three insecticides, three fungicides, ten herbicides, and five degradation products. Concentrations of eight compounds (one fungicide, five herbicides, and two degradation products) exceeded the limits established by the European Union (EU) for drinking water. Herbicides were the most frequently detected pesticides and were present at the highest concentrations (up to 3.416 μg L⁻¹) across all sampling campaigns. Metolachlor, prosulfocarb, metribuzin, and metolachlor degradation products (ethanesulfonic acid (ESA)– and oxanilic acid (OA)–metolachlor) were detected in concentrations over 1.0 µg L⁻¹. The sum of Toxic Units (∑Tu_i) showed that none of samples posed a high risk. None of compounds presented a high risk for the three aquatic organismstested; only prosulfocarb for algae and Daphnia magna; pendimethalin for algae and fish; and metribuzin, chlorotoluron, and desethyl-terbuthylazine (DETbz) for algae posed high risks. Full article

Broad-scale assessments often track forest productivity, yet they rarely quantify how soil conditions determine whether these gains persist as long-lived carbon and generate measurable economic value. This study focused on Northeast China, where forests include boreal coniferous stands dominated by Dahurian larch, temperate conifer–broadleaf mixed forests with Korean pine, and temperate deciduous broadleaf forests dominated by Mongolian oak. We combined GLASS net primary productivity and ESA CCI Land Cover to delineate forest pixels, used 2000 to 2005 as the baseline, and converted productivity anomalies into pixel level carbon economic value using a consistent pricing rule. Forest NPP increased significantly during 2000 to 2018 (slope = 1.57, p = 0.019), and carbon economic value also increased over time during 2006 to 2018 (slope = 2.24, p = 0.002), with the highest values in core mountain forests and lower values in the western forest–grassland transition zone. Correlation analysis, explainable random forests, and variance partitioning characterized spatial and temporal dynamics from 2000 to 2018 and identified environmental controls. Carbon value increased over time and showed marked spatial heterogeneity that mirrored productivity patterns in core mountain forests. Climate was the dominant predictor of value, while higher soil pH and clay content were negatively associated with value. The random forest model explained about 70% of the variance in carbon value (R² = 0.695), and variance partitioning indicated substantial unique and joint contributions from climate and soil alongside secondary topographic effects. The automatable framework enables periodic updates with new satellite composites, supports ecological compensation zoning, and informs soil-oriented interventions that enhance the monetized value of forest carbon sinks in data-limited regions. Full article

Accurate identification of cultivated land in plateau and mountainous regions remains challenging due to complex terrain and the fragmented, small-scale distribution of farmland. This study develops a high-precision cropland identification model tailored to such environments, aiming to advance precision agriculture and support the scientific planning and refined management of agricultural resources. Taking Xundian County, Yunnan Province, as a case study, multispectral, synthetic aperture radar (SAR), topographic, texture, and time-series features were integrated to construct a comprehensive multi-source feature space. A baseline land use map was generated by fusing datasets from the European Space Agency (ESA), the Environmental Systems Research Institute (ESRI), and the China Resource and Environment Data Cloud (CRLC). Using 4000 randomly selected sample points, five machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), Tabular Multiple Prediction (TABM), XGBoost, and the NSGA-II optimized XGBoost (NSGA-II-XGBoost)—were compared for cropland identification. Results show that the NSGA-II-XGBoost model consistently achieved superior performance in classification accuracy, stability, and adaptability, reaching an overall accuracy of 95.75%, a Kappa coefficient of 0.91, a recall of 0.96, and an F1-score of 0.96. These findings demonstrate the strong capability of the NSGA-II-XGBoost model for cropland mapping under complex topographic conditions, providing a robust technical framework and methodological reference for farmland protection and natural resource classification in other mountainous regions. Full article

The Kubuqi Desert represents a key ecologically fragile region in northern China, primarily functioning as a windbreak and sand-fixation barrier while also contributing to regional ecological balance. However, the area’s ecological vulnerability is pronounced, and investigating the spatiotemporal dynamics of vegetation carbon storage and associated driving mechanisms is essential for the scientific formulation of ecological restoration strategies. This research incorporates multi-source remote-sensing datasets (including Landsat 8 OLI/TIRS Level 2, Sentinel-1 Synthetic Aperture Radar (SAR), ERA5 daily meteorological data, GEDI Level 4B, SRTM GL1 v003, and ESA WorldCover v100) based on the Google Earth Engine (GEE) platform, and employs multiple machine-learning algorithms (validation metrics of the machine learning model: $R^2$ = 0.917, RMSE = 0.251) to develop a dynamic monitoring model of vegetation carbon storage in the Kubuqi Desert during the period 2019–2023. The analysis systematically evaluates the influence of climatic variables and anthropogenic activities on the spatiotemporal differentiation of carbon storage. The results indicate a slight upward trend in overall carbon storage across the study area (average annual increase of 0.4%), with high values predominantly concentrated in vegetated regions (up to 5.22 Mg/Ha) and low values distributed in bare lands and desert zones (0.5–0.7 Mg/Ha). Altitude, temperature, and slope serve as the primary driving factors governing carbon-storage variability. The findings suggest that scientifically guided vegetation restoration and optimized water-resource management can enhance the carbon-sink capacity of the Kubuqi Desert, offering a robust scientific basis for ecological governance and carbon budget assessment in arid and semi-arid desert ecosystems. Full article

Astronomy provides an exceptional context for developing data literacy, critical thinking, and computational skills in education. This paper presents a project-based learning (PBL) framework that integrates exploratory data analysis (EDA), fuzzy logic, and explainable artificial intelligence (XAI) to teach students how to extract and interpret scientific knowledge from real astronomical data. Using open-access resources such as NASA’s JPL Horizons and ESA’s Gaia DR3, together with Python libraries like Astroquery and Plotly, learners retrieve, process, and visualize dynamic datasets of comets, asteroids, and stars. The methodology follows the full data science pipeline, from acquisition and preprocessing to modeling and interpretation, culminating with the application of the FAS-XAI framework (Fuzzy-Adaptive System for Explainable AI) for pattern discovery and interpretability. Through this approach, students can reproduce astronomical analyses and understand how data-driven methods reveal underlying physical relationships, such as orbital structures and stellar classifications. The results demonstrate that combining EDA with fuzzy clustering and explainable models promotes deeper conceptual understanding and analytical reasoning. From an educational perspective, this experience highlights how inquiry-based and computationally rich activities can bridge the gap between theoretical astronomy and data science, empowering students to see the Universe as a laboratory for exploration, reasoning, and discovery. This framework thus provides an effective model for incorporating artificial intelligence, open data, and reproducible research practices into STEM education. Full article

Insulin-like androgenic gland factor (IAG) is considered a key regulator of male sexual differentiation and maturation in decapod crustaceans. In several species, IAG expression is thought to be negatively regulated by the eyestalk, as demonstrated by eyestalk ablation (ESA) experiments. In the kuruma prawn Marsupenaeus japonicus, however, the upstream regulatory mechanisms of IAG (Maj-IAG) remain largely unclear. In the present study, males of different body sizes were subjected to ESA to elucidate these mechanisms. Bilateral ESA induced upregulation of Maj-IAG expression from day 7 onward, whereas unilateral ESA did not. Moreover, enhanced development of male reproductive organs and hypertrophy of the androgenic gland were observed from day 7 after bilateral ESA. These findings indicate that Maj-IAG is regulated by eyestalk-derived factor(s), supporting the presence of an eyestalk–androgenic gland endocrine axis in M. japonicus. By contrast, the expression of Maj-Dsx2, a homolog of doublesex (Dsx) that has recently been proposed as an upstream regulator of IAG, did not show a consistent increase following bilateral ESA across all experiments, suggesting that the involvement of Maj-Dsx2 in this axis remains unclear. Overall, this study provides fundamental insights into the regulatory mechanisms of decapod male reproduction. Full article