Search Results (1,020)

In this study, we evaluated the spatial variability in droplet deposition in herbicide applications using a remotely piloted aircraft (RPA) in pasture areas. The investigation was conducted in a square grid (50.0 m × 50.0 m), with 121 sampling points, at two operational flight heights (3.0 and 4.0 m). Droplet deposition was quantified using the fluorescent dye rhodamine B, and the droplet spectrum was characterised using water-sensitive paper tags. Geostatistical analysis was implemented to characterise spatial dependence, complemented by multivariate statistical analysis. Droplet deposition ranged from 1.01 to 9.02 and 1.10–6.10 μL cm⁻² at 3.0 and 4.0 m flight heights, respectively, with the coefficients of variation between 19.72 and 23.06% for droplet spectrum parameters. All droplet spectrum parameters exhibited a moderate to strong spatial dependence (relative nugget effect ≤75%) and a predominance of adjustment to the exponential model, with spatial dependence indices ranging from 12.55 to 47.49% between the two flight heights. Significant positive correlations were observed between droplet deposition and droplet spectrum parameters (r = 0.60–0.79 at 3.0 m; r = 0.37–0.66 at 4.0 m), with the correlation magnitude decreasing as the operational flight height increased. Cross-validation indices demonstrated acceptable accuracy in spatial prediction, with a mean estimation error ranging from −0.030 to 0.044 and a root mean square error ranging from 0.81 to 2.25 across parameters and flight heights. Principal component analysis explained 99.14 and 85.72% of the total variation at 3.0 and 4.0 m flight heights, respectively. The methodological integration of geostatistics and multivariate statistics provides a comprehensive understanding of the spatial variability in droplet deposition, with relevant implications for the optimisation of phytosanitary applications performed using RPAs. Full article

Promoting the sustainable development of virtual water trade is of great significance to safeguarding China’s water resource security and balanced regional economic growth. This study analyzes the virtual water trade network among 31 Chinese provinces based on multi-regional input–output tables from 2012, 2015, and 2017, using total trade decomposition, social network analysis, and exponential random graph models. The key findings are as follows: (1) The total virtual water trade volume remains stable, with Xinjiang, Jiangsu, and Guangdong as the core regions, while remote areas such as Shaanxi and Gansu have lower trade volumes. The primary industry dominates, and it is driven by simple value chains. (2) Provinces such as Xinjiang, Heilongjiang, and Jiangsu form the network’s core. Network density and symmetry increased from 2012 to 2015 but declined slightly in 2017, with efficiency peaking and then dropping, and the clustering coefficient decreased annually. Four economic sectors exhibit distinct interactions: frequent two-way flows in Sector 1, significant inflows in Sector 2, prominent net spillovers in Sector 3, and key brokers in Sector 4. (3) The network evolved from a core-periphery structure with weak ties to a stable, heterogeneous, and resilient system. (4) Influencing factors, such asper capita water resources, economic development, and population, significantly impact trade. Similarities in economic levels, population, and water endowments promote trade, while spatial distance has a limited effect, with geographic proximity showing a significant negative impact on long-distance trade. Full article

The combined application of fibers and lightweight aggregates (LWAs) represents an effective approach to achieving high-strength, lightweight concrete. To enhance the predictability of the mechanical properties of fiber-reinforced lightweight aggregate concrete (LWAC), this study conducts an in-depth investigation into its hydration characteristics. In this study, high-strength LWAC was developed by incorporating low water absorption LWAs, various volume fractions of basalt fiber (BF) (0.1%, 0.2%, and 0.3%), and a ternary cementitious system consisting of 70% cement, 20% fly ash, and 10% silica fume. The hydration-related properties were evaluated through isothermal calorimetry test and high-temperature calcination test. The results indicate that incorporating 0.1–0.3% fibers into the cementitious system delays the early hydration process, with a reduced peak heat release rate and a delayed peak heat release time compared to the control group. However, fitting the cumulative heat release over a 72-h period using the Knudsen equation suggests that BF has a minor impact on the final degree of hydration, with the difference in maximum heat release not exceeding 3%. Additionally, the calculation model for the final degree of hydration in the ternary binding system was also revised based on the maximum heat release at different water-to-binder ratios. The results for chemically bound water content show that compared with the pre-wetted LWA group, under identical net water content conditions, the non-pre-wetted LWA group exhibits a significant reduction at three days, with a decrease of 28.8%; while under identical total water content conditions it shows maximum reduction at ninety days with a decrease of 5%. This indicates that pre-wetted LWAs help maintain an effective water-to-binder ratio and facilitate continuous advancement in long-term hydration reactions. Based on these results, influence coefficients related to LWAs for both final degree of hydration and hydration rate were integrated into calculation models for degrees of hydration. Ultimately, this study verified reliability of strength prediction models based on degrees of hydration. Full article

The mapping and monitoring of coastal regions are critical to ensure their sustainable use and viability in the long term. Delineation of coastlines is becoming increasingly important in the light of climate change and rising sea levels. However, many coastlines are highly dynamic; therefore, mono-temporal assessments of coastal ecosystems and coastlines are mere snapshots of limited practical value for space-based planning. Understanding of the spatio-temporal dynamics of coastal ecosystem boundaries is important to inform ecosystem management but also for a meaningful delineation of the high-water mark, which is used as a benchmark for coastal spatial planning in South Africa. This research aimed to use hyper-temporal Sentinel-2 imagery to extract ecological zones on the coast of KwaZulu-Natal, South Africa. A total of 613 images, collected between 2019 and 2023, were classified into four distinct coastal ecological zones—vegetation, bare, surf, and water—using a Random Forest model. Across all classifications, the percentage of each of the four classes’ occurrence per pixel over time was determined. This enabled the identification of ecosystem locations, spatially static ecosystem boundaries, and the occurrence of ecosystem boundaries with a more dynamic location over time, such as the non-permanent vegetation zone of the foredune area as well as the intertidal zone. The overall accuracy of the model was 98.13%, while the Kappa coefficient was 0.975, with user’s and producer’s accuracies ranging between 93.02% and 100%. These results indicate that cloud-based analysis of Sentinel-2 time series holds potential not just for delineating coastal ecosystem boundaries, but also for enhancing the understanding of spatio-temporal dynamics between them, to inform meaningful environmental management, spatial planning, and climate adaptation strategies. Full article

The presence of sea ice threatens low ice-class vessels’ navigation safety in the Arctic, and traditional Navigation Risk Assessment Models based on sea ice parameters have been widely used to guide safe passages for ships operating in ice regions. However, these models mainly rely on empirical coefficients, and the accuracy of these models in identifying sea ice navigation risk remains insufficiently validated. Therefore, under the binary classification framework, this study used Automatic Identification System (AIS) data along the Northeast Passage (NEP) as positive samples, manual interpretation non-navigable data as negative samples, a total of 10 machine learning (ML) models were employed to capture the complex relationships between ice conditions and navigation risk for Polar Class (PC) 6 and Open Water (OW) vessels. The results showed that compared to traditional Navigation Risk Assessment Models, most of the 10 ML models exhibited significantly improved classification accuracy, which was especially pronounced when classifying samples of PC6 vessel. This study also revealed that the navigability of the East Siberian Sea (ESS) and the Vilkitsky Strait along the NEP is relatively poor, particularly during the month when sea ice melts and reforms, requiring special attention. The navigation risk output by ML models is strongly determined by sea ice thickness. These findings offer valuable insights for enhancing the safety and efficiency of Arctic maritime transport. Full article

Efficient and sustainable extraction of bioactive benzopyrans from Hypericum polyanthemum Klotzsch ex Reichardt (Hypericaceae) remains underexplored, despite their potential applications. The current study aimed to optimize this process by integrating computational simulation and experimental extraction with suitable solvents. The COSMO-RS model was employed to screen deep eutectic solvents (DESs), indicating lactic acid/glycine/water 3:1:3 (DES 1) as a highly promising candidate based on activity coefficients at infinite dilution for target benzopyrans (HP1, HP2, HP3). Ultrasound-assisted extraction (UAE) was then conducted using the proposed DES as well as hexane, and the extracts were analyzed via high-performance liquid chromatography (HPLC) and spectrophotometry for total phenolic content (TPC). The results for DES 1 showed yields for benzopyrans HP1 (1.43 ± 0.09 mg/g plant) and HP2 (0.55 ± 0.04 mg/g plant) close to those obtained in the hexane extract (1.65 and 0.78 mg/g plant, respectively), corroborating the use of COSMO-RS for solvent screening. Kinetic analysis using an adapted Crank diffusion model successfully described the mass transfer process for DES 1 (R² > 0.98, mean average percent error < 9%), indicating diffusion control and allowing estimation of effective diffusion coefficients. This work confirms COSMO-RS as a valuable tool for solvent selection and demonstrates that UAE with the identified DES provides an efficient, greener approach for extracting valuable benzopyrans, offering a foundation for further process optimization. Full article

The continuous variability in the microbiological quality of surface waters presents significant challenges for ensuring the production of safe drinking water in compliance with public health regulations. Inadequate treatment of surface waters can lead to the presence of pathogenic microorganisms in the drinking water supply, posing serious risks to public health. This research presents an in-depth data analysis using machine learning tools for the induction of models to describe and predict microbiological water quality for the sustainable management of the Butoniga drinking water treatment facility in Istria (Croatia). Specifically, descriptive and predictive models for total coliforms and E. coli bacteria (i.e., classes), which are recognized as key sanitary indicators of microbiological contamination under both EU and Croatian water quality legislation, were developed. The descriptive models provided useful information about the main environmental factors that influence the microbiological water quality. The most significant influential factors were found to be pH, water temperature, and water turbidity. On the other hand, the predictive models were developed to estimate the concentrations of total coliforms and E. coli bacteria seven days in advance using several machine learning methods, including model trees, random forests, multi-layer perceptron, bagging, and XGBoost. Among these, model trees were selected for their interpretability and potential integration into decision support systems. The predictive models demonstrated satisfactory performance, with a correlation coefficient of 0.72 for total coliforms, and moderate predictive accuracy for E. coli bacteria, with a correlation coefficient of 0.48. The resulting models offer actionable insights for optimizing operational responses in water treatment processes based on real-time and predicted microbiological conditions in the Butoniga reservoir. Moreover, this research contributes to the development of predictive frameworks for microbiological water quality management and highlights the importance of further research and monitoring of this key aspect of the preservation of the environment and public health. Full article

To accurately assess the water quality, ecosystem status, distribution of large benthic organisms, and ecological restoration under human intervention, an analysis of benthic organisms on Caofeidian in September and November 2023 and January and May of the following year was conducted in this work. By performing CCA (canonical correspondence analysis) and cluster and correlation coefficient (Pearson) analyses, the temporal variation characteristics of benthic abundance, dominant species, community structure and biodiversity were analyzed. A total of 79 species of macro-benthic animals were found in four months, including 32 species of polychaetes, cnidarians, 1 species of Nemertean, 19 species of crustaceans, and 24 species of molluscs. The use of conventional grab-type mud collectors revealed that the Musculus senhousei dominated the survey (Y > 0.02). While only a small number of Ruditapes philippinarum were collected from bottom-dwelling species, a certain number of bottom-dwelling species (Ruditapes philippinarum and Scapharca subcrenata) were also collected during the trawl survey. Additionally, a significant population of Rapana venosa was found in the area. It is speculated that the dual effects of predation and competition are likely the primary reasons for the relatively low abundance of bottom-dwelling species. The density and biomass of macro-benthos were consistent over time, which was the highest in May, the second highest in January, and the lowest in September and November. The main environmental factors affecting the large benthic communities in the surveyed sea areas were pH, DO, NO₂⁻-N, T, SAL and PO₄³⁻-P. Combined with historical data, it was found that although the environmental condition in the Caofeidian sea area has improved, the Musculus senhousei has been dominant. In addition, the abundance of other species is much less than that of the Musculus senhousei, and the diversity of the benthic community is still reduced. Our work provides valuable data support for the management and improvement of bottom Marine pasture and promotes the transformation of Marine resources from resource plunder to a sustainable resource. Full article

This research aimed to evaluate the biological resistance to xylophagous organisms and the dimensional stability related to water absorption in plastic wood panels manufactured by compression molding and produced with pine wood and recycled thermoplastics. The wood–plastic composites (WPCs) were prepared from 50% pine sawdust and 50% recycled plastics (polyethylene terephthalate-PET, high-density polyethylene-HDPE, and polypropylene-PP). The thickness swelling test was carried out by immersing of the WPC samples in water at room temperature (25–30 °C) and evaluating the total change in WPC thickness after 1500 h (≈9 weeks or two months). In addition, the coefficient of initial swelling was evaluated to verify the variability of the swelling. For the biological resistance evaluation of the WPCs, tests were carried out with soil or arboreal termites (Nasutitermes corniger) and drywood termites (Cryptotermes brevis). The WPC loss of mass and termite mortality were evaluated. The use of PP promoted the best response to thickness swelling. The simple mathematical model adopted offers real predictions to evaluate the thickness of the swelling of the compounds in a given time. For some variables there were no statistical differences. It was shown that treatment 3 (T3) presented visual damage values between 0.4 for drywood termites and 9.4 for soil termites, in addition to 26% termite mortality, represented by the lowest survival time of 12 days. The developed treatments have resistance to termite attacks; these properties can be an important starting point for its use on a larger scale by the panel industries. Full article

The high water demand of rice cultivation is mainly due to flood irrigation, which requires large volumes not only to meet evapotranspiration needs, but also due to losses from percolation, lateral seepage, and surface runoff. In addition to lowering water use efficiency, surface runoff may transport nutrients. This study aimed to calibrate and validate a daily water balance model to estimate surface runoff losses across three rice-growing seasons. During the first two seasons, different model components were calibrated by comparing simulated and observed water depths. In the final season, the calibrated model was validated using direct runoff measurements obtained from weirs and flowmeters. Results showed strong agreement between model estimates and direct measurements of water depth and surface runoff. Linear regression models showed good fit, with coefficients of determination (R²) above 0.80 for water depth and 0.79 for runoff. A validated daily water balance model, combined with periodic monitoring of water depth, proved to be a reliable tool for estimating surface runoff during the rice-growing season. Total runoff—from irrigation, rainfall, and final drainage—represented between 7.5% and 18% of the total water input. This approach offers a practical tool for improving irrigation water management and understanding runoff-driven nutrient transport. Full article

Soil mulching and irrigation are critical practices for alleviating water scarcity and enhancing crop yields in arid and semi-arid regions by regulating soil moisture and soil temperature. Clarifying the effects of various irrigation depths on soil moisture and temperature under mulched condition is essential for optimizing irrigation strategies. This study investigated the effects of four irrigation depths based on crop evapotranspiration (ET_c): 60, 80, 100, and 120% (W_0.6, W_0.8, W_1.0, and W_1.2, respectively) on the soil moisture content (SMC), soil temperature and seed cotton yield in mulched cotton fields. Results revealed that when the irrigation depth increased from 60%ET_c to 120%ET_c, seed cotton yield increased by 12.04% in 2018 and 17.00% in 2019 at the cost of irrigation water use efficiency (IWUE), which decreased from 2.53 kg m⁻³ to 1.54 kg m⁻³ in 2018 and 2.60 kg m⁻³ to 1.58 kg m⁻³ in 2019. Soil temperature exhibited a temporal trend of initial increase followed by decline, and it was positively affected by soil mulching. Notably, W_0.6 treatment maintained significantly higher soil temperature than other treatments. Soil moisture content was positively affected by irrigation depth, while soil water storage first decreased and then increased over time, reaching the minimum at the flowering and boll setting stages during the two growing seasons. Higher irrigation amount reduced the total spatial variability (C₀ + C) of soil but did not significantly alter the distribution characteristics of soil moisture, as indicated by stable coefficients of variation (CVs) and stratification ratios (SRs). The variability of soil moisture diminished with soil depth with the lowest CV obtained at a 60 cm soil layer across the growth stages. Correlation analysis results showed that the seed cotton yield was mainly affected by irrigation depth and soil water storage. Soil temperature at the flowering and boll setting stage negatively affected seed cotton yield and was inversely correlated with soil water storage. The structural equation model (SEM) further indicated that both soil water storage and soil temperature primarily influenced seed cotton yield boll weight rather than boll number. Furthermore, 100%ET_c (W_1.0) can be considered as the recommended irrigation depth based on the soil moisture and temperature, seed cotton yield and water use efficiency in this region. Full article

Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. Furthermore, K exhibits a highly specific response in near-infrared (NIR) spectroscopy compared to elements such as nitrogen (N) and phosphorus (P). Given its fundamental impact on fruit quality parameters, the development of rapid and non-destructive techniques for K determination is of significant importance for precision fertilization management. By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R²) exceeding 0.86 under field conditions.” We systematically collected a total of 9000 leaf samples from Korla fragrant pear orchards and acquired spectral data using a benchtop near-infrared spectrometer. After preprocessing and feature extraction, we determined the optimal modeling method for prediction accuracy through comparative analysis of multiple models. Multiplicative scatter correction (MSC) and first derivative (FD) are synergistically employed for preprocessing to eliminate scattering interference and enhance the resolution of characteristic peaks. Competitive adaptive reweighted sampling (CARS) is then utilized to screen five potassium-sensitive bands, specifically in the regions of 4003.5–4034.35 nm, 4458.62–4562.75 nm, and 5145.15–5249.29 nm, among others, which are associated with O-H stretching vibration and changes in water status. A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R²) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. The model facilitates rapid and non-destructive detection of leaf potassium content throughout the entire growth period of Korla fragrant pears, supporting precision fertilization in orchards. Moreover, it elucidates the physiological mechanism by which potassium influences spectral response through the regulation of water metabolism. Full article

Due to the lack of sophisticated instruments for monitoring sludge dewatering performance in certain wastewater treatment plants, there is an urgent need to develop cost-effective and rapidly deployable technologies for assessing sludge dewaterability. This study proposed an image-based approach to evaluate sludge dewaterability. Flocculation images of sludge were captured using a smartphone under controlled conditions and processed via MATLAB for grayscale adjustment, contrast enhancement, and size standardization. Fractal image analysis was employed to justify the selection of floc area (rather than floc equivalent diameter) for downstream analyses. Significant correlations were observed between the number of different sludge floc area range and key dewaterability parameters: The number of flocs in area range of 10⁻⁶–10⁻⁵ cm² showed a negative correlation with capillary suction time (CST) (regression coefficient (R) = −0.511, probability (p) < 0.05) and a positive correlation with median particle size (R = 0.470, p < 0.05); the number of flocs in area range of 10⁻⁵–10⁻⁴ cm² exhibited a stronger negative correlation with CST (R = −0.538, p < 0.05) and a positive correlation with median particle size (R = 0.480, p < 0.05). Further validation experiments using a laboratory-scale diaphragm filter press demonstrated that when the proportion of the number of flocs in area range of 10⁻⁵–10⁻⁴ cm² relative to the total number of flocs for conditioned sludge fell below 70%, the dewatered sludge cake achieved a water content of less than 60%. This study highlights the feasibility of using commercially available smartphones as a practical tool for evaluating sludge dewaterability. Full article

The use of Google Earth Engine (GEE), a cloud-based computing platform, in spatio-temporal evaluation studies has increased rapidly in natural sciences such as forestry. In this study, Sentinel-2 satellite imagery and Shuttle Radar Topography Mission (SRTM) elevation data and image classification algorithms based on two machine learning techniques were examined. Random Forest (RF) and Classification and Regression Trees (CART) were used to classify land use and land cover (LULC) in western Oregon (USA). To classify the LULC from the spectral bands of satellite images, a composition consisting of vegetation difference indices NDVI, NDWI, EVI, and BSI, and a digital elevation model (DEM) were used. The study area was selected due to a diversity of land cover types including research forest, botanical gardens, recreation area, and agricultural lands covered with diverse plant species. Five land classes (forest, agriculture, soil, water, and settlement) were delineated for LULC classification testing. Different spatial points (totaling 75, 150, 300, and 2500) were used as training and test data. The most successful model performance was RF, with an accuracy of 98% and a kappa value of 0.97, while the accuracy and kappa values for CART were 95% and 0.94, respectively. The accuracy of the generated LULC maps was evaluated using 500 independent reference points, in addition to the training and testing datasets. Based on this assessment, the RF classifier that included elevation data achieved an overall accuracy of 92% and a kappa coefficient of 0.90. The combination of vegetation difference indices with elevation data was successful in determining the areas where clear-cutting occurred in the forest. Our results present a promising technique for the detection of forests and forest openings, which was helpful in identifying clear-cut sites. In addition, the GEE and RF classifier can help identify and map storm damage, wind damage, insect defoliation, fire, and management activities in forest areas. Full article

Background/Objectives: A critical part of the maize life cycle takes place during the summer, and due to climate change, its growth and development are increasingly exposed to the irregular and unpredictable effects of drought stress. Developing and using new cultivars with increased drought tolerance for farmers is the easiest and cheapest solution. One of the concepts to screen for drought tolerance is to expose germplasm to various growth scenarios (environments), expecting that random drought will occur in some of them. Methods: In the present study, thirty-two maize hybrids belonging to four FAO maturity groups were tested for grain yield at six locations over two consecutive years. In parallel, data of the basic meteorological elements such as air temperature, relative humidity and precipitation were collected and used to compute two indices, scPDSI (Self-calibrating Palmer Drought Severity Index) and VPD (Vapor Pressure Deficit), that were assessed as indicators of drought (water deficit) severity during the vegetation period. Practical implementation of these indices was carried out indirectly by first analyzing yield data using a factor analytic model to detect latent environmental variables affecting yield and then correlating those latent variables with drought indices. Results: The first latent variable, which explained 47.97% of the total variability, was correlated with VPD (r = −0.58); the second latent variable explained 9.57% of the total variability and was correlated with scPDSI (r = −0.74). Furthermore, latent regression coefficients (i.e., genotypic sensitivities to latent environmental variables) were correlated with genotypic drought tolerance. Conclusions: This could be considered an indication that there were two different acting mechanisms in which drought affected yield. Full article