Search Results (1,681)

Sugarcane (Saccharum officinarum L.) is one of the world’s most important agro-industrial crops, and the technological quality of its juice directly determines the efficiency of sucrose extraction and recovery processes. In tropical soils with low P availability, conventional fertilization is often inefficient due to nutrient immobilization, which increases production costs and environmental risks. In this regard, plant growth-promoting bacteria (PGPBs) have emerged as a sustainable alternative to improve nutrient use efficiency. This study evaluated the effect of inoculation with A. brasilense, P. fluorescens, and B. subtilis (single strains and consortia), combined with two levels of residual p (160 and 225 kg P₂O₅·ha⁻¹), on the technological quality of the juice and the microbial dynamics of the rhizosphere. The experiment was conducted under tropical field conditions using a randomized complete block design with split plots and five replications. A highly significant interaction between phosphorus and inoculation (p < 0.001) was observed for °Brix, Pol, purity, and sucrose. The B. subtilis + P. fluorescens consortium under reduced phosphorus (160 kg P₂O₅·ha⁻¹) achieved the highest values for sucrose (17.26%), °Brix (20.32), and purity (87.03%). A linear regression model showed that rhizosphere microbial density explained a large proportion of the variability in sucrose (R² = 0.96; β = 2.02; p < 0.001). Principal component analysis explained 91.8% of the total variance, clearly separating the consortia from the individual strains and the controls. These results indicate that PGPB consortia, combined with moderate pH fertilization, can improve the technological quality of sugarcane while enhancing rhizosphere functionality, representing a promising strategy for more sustainable production systems in tropical environments. Full article

Landslide mapping and susceptibility assessment are essential for hazard identification, infrastructure protection, and risk management. The middle and lower reaches of the Nujiang River have high relief, rapid geomorphic change, and fragile landscape conditions, which increase landslide susceptibility and hinder timely detection. To improve the spatiotemporal characterization of landslide activity, we developed a multi-source Earth observation framework for annual landslide mapping and susceptibility assessment. First, interannual embedding-change intensity maps were generated to guide the visual interpretation of landslide-related surface disturbances. Second, annual landslide and non-landslide samples were collected through field validation and visual interpretation. Third, annual 10 m landslide maps for 2017–2025 were generated using random forest on Google Earth Engine. Finally, 24 multidimensional environmental factors were incorporated into landslide susceptibility modeling. Landslides were concentrated mainly along the Nujiang River corridor and adjacent high-relief canyon slopes, with marked interannual variability but relatively stable hotspot regions. SHAP analysis further identified BSI_mean as the most important predictor, with a mean absolute SHAP value of 0.116, followed by NDVI_mean and terrain-related variables, indicating that bare-surface exposure, vegetation condition, and terrain dissection were strongly associated with mapped landslide occurrence. This study provides annual landslide inventories and susceptibility information for hazard mitigation and infrastructure planning. Full article

High-throughput and non-destructive phenotyping approaches are increasingly needed to support precision agriculture and plant breeding. This study evaluates the use of unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning to estimate anthocyanin content in red lettuce genotypes under field conditions. High-resolution RGB and multispectral images were acquired using a low-cost UAV platform, and vegetation indices sensitive to pigment variation were extracted at the plot scale. Ridge regression, decision tree, and random forest models were trained using 80% of the dataset and validated with the remaining 20%. Random forest achieved the highest performance for anthocyanin estimation, with coefficients of determination reaching R² = 0.84 and lower prediction errors than linear approaches. Overall, the results demonstrate that UAV-based multispectral sensing integrated with machine learning provides a robust, scalable, and cost-effective solution for non-destructive pigment phenotyping, with direct applications in biofortification-oriented breeding and precision agriculture. Full article

As critical underground engineering structures, wellbores may suffer complex structural deterioration and hidden safety hazards may be encountered during drilling. Multi-source sensor monitoring data provides an effective data basis for structural health perception and early warnings for wellbore structures at risk. The inherent diversity of formation conditions and the dynamic disturbances during drilling jointly lead to the differentiated presentation of drilling loss types, among which fractured, permeable, and vuggy losses are the most typical. This paper focuses on fractured wellbore leakage, regards wellbore leakage as an important structural failure form of underground drilling engineering structures. In-depth analysis and research on the structural deterioration mechanism of wellbore leakage were conducted, and we propose a wellbore leakage prediction method based on the improved sparrow search algorithm (ISSA) optimized gradient boosting decision tree (XGBoost). First, the Sobol sequence is adopted to replace the random initialization strategy, combined with the opposition-based learning mechanism; then, an adaptive Levy flight search mechanism is introduced to dynamically adjust the population ratio of discoverers and vigilantes; finally, intelligent optimization technologies are integrated to reconstruct the position update strategies of discoverers, followers, and vigilantes, enhancing the optimization adaptability of the algorithm. Relying on multi-field sensor monitoring datasets collected from actual drilling engineering, this paper compares the proposed model with wellbore leakage prediction models built by classical machine learning algorithms, and verifies its generalization ability on different datasets. Experimental data indicate that the improved algorithm exhibits significant advantages in optimization accuracy, enabling the proposed model to achieve an AUC improvement of 4.46%, along with accuracy (95.1%), precision (94.9%), recall (94.7%), and F1-score (94.2%). On this basis, the ISSA was applied to the hyperparameter optimization of XGBoost, constructing the ISSA-XGBoost prediction model. The method has high accuracy and good generalization ability in fractured wellbore leakage prediction, and it can realize intelligent health monitoring of underground wellbore structures, including early warnings. This study provides a reliable sensing data analysis scheme and technical support for structural health monitoring and hazard prevention in drilling engineering. Full article

Yield estimation in sugarcane systems remains a major challenge in tropical regions due to the reliance on destructive, labor-intensive, and spatially limited field measurements. Although remote sensing has been widely used for crop monitoring, its predictive performance is often constrained when spectral information is used in isolation. This study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions. A commercial field was monitored throughout the 2022–2023 growing season, and machine learning models, including random forest (RF), support vector machine (SVM), and multiple linear regression (MLR), were developed to estimate stem, foliage, and total biomass. To reduce potential spatial data leakage caused by spatial autocorrelation within the field, model performance was evaluated using Spatial Block Cross-Validation. Results showed that integrating spectral and meteorological data consistently improved predictive performance compared to spectral-only and weather-only scenarios. Spectral bands exhibited stronger relationships with biomass than derived vegetation indices, while maximum temperature and solar radiation were identified as key drivers of biomass variability. RF combined with spectral–weather fusion achieved the highest predictive performance, reaching R² values up to 0.95, RMSE values as low as 5296.35, and rRMSE values close to 18% for stem biomass, consistently outperforming SVM and MLR. In contrast, spectral-only scenarios produced lower predictive accuracy and higher prediction errors across all biomass variables. This study provides one of the first field-scale implementations under humid tropical conditions in southeastern Mexico, where georeferenced yield data remain scarce. Full article

Cold stress is a major environmental constraint limiting the growth, physiological performance, and productivity of African marigold (Tagetes erecta L.) under open-field conditions. This study evaluated the comparative effectiveness of salicylic acid (SA), silicon (Si), and methyl jasmonate (MeJA) in alleviating cold-induced damage and enhancing stress tolerance. Field experiments were conducted under naturally occurring cold stress using foliar applications of SA (0, 0.1, 0.5, and 1 mM), Si (0, 1, 5, and 10 mM), and MeJA (0, 10, and 50 µM) in a complete randomized block design with three replications. Moderate concentrations of all three regulators significantly (p < 0.05) improved plant growth and physiological stability relative to untreated controls. Salicylic acid at 0.5 mM produced the most consistent protective response, increasing biomass accumulation, chlorophyll content, and relative water content while reducing membrane damage, as indicated by a 42.3% decrease in leaf electrolyte leakage at 2 °C. Silicon at 10 mM enhanced membrane integrity, plant water status, and vegetative growth under low-temperature conditions, while methyl jasmonate at 10 µM mitigated cold-induced membrane damage and improved physiological tolerance, whereas higher concentrations (50 µM) were less effective. At their optimal doses, SA, Si, and MeJA increased plant dry mass by 39.7%, 30.1%, and 38.5%, respectively. Correlation analysis confirmed these results, revealing strong positive relationships among growth, chlorophyll, and relative water content. Conversely, electrolyte leakage was negatively correlated with biomass and water status, identifying membrane stability as a key determinant of cold tolerance. Overall, 0.5 mM SA, 5–10 mM Si, and 10 μM MeJA improved growth and key physiological responses in African marigold under cold stress under field conditions. Future studies should integrate mechanistic and economic analyses to refine growth-regulator-based cold-stress management strategies. Full article

The vibration serviceability of large-span footbridges under crowd loading has become a governing design criterion. However, the significant divergence among existing load models introduces substantial uncertainty into response prediction. This study presents a comparative evaluation of ten representative crowd walking load models from international codes and the relevant literature. It objectively evaluates their theoretical mechanisms regarding crowd synchronization and structural damping. Initial parametric sensitivity analyses are conducted utilizing single-degree-of-freedom systems. Subsequently, the predictive capabilities of these models are evaluated against field measurements from four footbridges under resonant and off-resonant conditions. The investigation reveals that response-based amplification models (e.g., M1–M3) assume high synchronization and thus overestimate accelerations under natural unrestricted resonant flows. However, these models perform reasonably well under off-resonant high-frequency conditions. In contrast, load-based models that incorporate the square-root growth law and explicit damping terms (e.g., M8–M10) better represent uncorrelated crowd flows under resonant conditions. These observations, while derived from a limited set of validation cases, provide indicative guidance and illustrate that accounting for phase randomness and structural damping is important for serviceability assessment. Full article

The rise in anthelmintic resistance in small ruminants has driven the search for sustainable control alternatives. Among these, entomopathogenic fungi such as Metarhizium robertsii stand out for their potential to reduce the free-living stages of gastrointestinal nematodes (GINs). This study evaluated the field efficacy of M. robertsii (LCM S15) under climatic conditions in the Recôncavo region of Bahia, Brazil. The experiment was conducted between November 2022 and July 2023 in a completely randomized design with four groups (n = 8): aqueous control, oil control, aqueous suspension, and oil formulation of M. robertsii. Egg counts per gram of feces (EPG) and L3 larval recovery were assessed by coproculture and the Baermann technique. Efficacy ranged from 15.23% to 27.34%, with the oil formulation showing higher performance. Haemonchus sp. and Trichostrongylus sp. were the most prevalent genera. These findings suggest the potential of M. robertsii (LCM S15) as a biological control agent under field conditions. Full article

Background/Objectives: Machine learning has emerged as a transformative force in drug discovery, revolutionizing traditional research paradigms and profoundly improving the efficiency, cost-effectiveness, and speed of the drug development cycle for novel drugs. Colorectal cancer is one of the most prevalent malignant tumors and imposes a heavy burden on global public health due to its high morbidity, mortality, and poor prognosis. Cyclooxygenase-2 (COX-2) is a key therapeutic target of colorectal cancer and has been extensively applied in the development of novel anti-colorectal cancer drugs. Methods: In this study, we systematically compared the performance of Random Forest Classifier (RFC), deep learning (DL), and graph neural network (GNN) models, including GAT (Graph Attention Network), GCN (Graph Convolutional Network), and MPNN (Message Passing Neural Network), with diverse features in the classification task of COX-2 inhibitors, based on a custom COX-2 inhibitors dataset and a Traditional Chinese Medicine (TCM)-derived compound library. The optimal model was subsequently used to screen for potential COX-2 inhibitors. Additionally, the key substructures governing COX-2 inhibitory activity were also identified and analyzed. Finally, the prioritized candidate compounds underwent experimental validation. Results: Both RFC and DL models outperformed GNN models. Through further comparative analysis of models’ predictive performance, the RFC model was ultimately verified as the optimal model for activity screening of TCM-derived compounds. The molecular interactions and binding affinities between predicted candidate compounds and COX-2 were further investigated. Finally, the selected lead compound, dehydrocostus lactone, was experimentally confirmed to possess potent COX-2 inhibitory activity. Conclusions: This study highlights that the RFC model is highly effective in screening bioactive components from TCM under small-dataset conditions, providing a solid foundation for subsequent related research in this field. Full article

Banana fruits are susceptible to chilling injury (CI) under field conditions, which significantly impairs fruit quality. Cold tolerance varies among genotypes; however, only a limited number of cultivars have been identified as tolerant and are commercially cultivated. This study aimed to investigate the physiological responses and quality attributes of banana cultivars exposed to natural cold fronts during development, compared with fruits developed under summer conditions. Furthermore, it evaluated whether the B genome confers greater cold tolerance, driven by a more efficient antioxidant mechanism, thereby supporting its recommendation for cultivation in regions prone to low temperatures. Bunches were harvested in winter following five natural cold fronts, during which air temperatures fell below 12 °C (137 h). The experimental design followed a completely randomized design in a factorial arrangement. Consecutive cold fronts intensified CI symptoms up to the fourth exposure event. CI severity was highest in ‘Grande Naine’ (AAA), which exhibited lower L*, a*, and b* values at the ripe stage compared to ‘BRS Princesa’ (AAAB) and ‘Prata Catarina’ (AAB), along with greater deviations relative to summer-harvested fruits. Malondialdehyde (MDA), total phenolic content, and antioxidant enzyme activities (SOD, CAT, APX, and POD) in the peel of unripe fruits were significantly higher during winter, particularly in ‘BRS Princesa’ and ‘Prata Catarina’, compared to ‘Grande Naine’. Proline accumulation followed a similar pattern, with the highest levels observed in ‘BRS Princesa’, followed by ‘Prata Catarina’ and ‘Grande Naine’. The findings indicate that ‘BRS Princesa’ exhibits greater tolerance to cold stress and highlights of the contribution of the B genome. Phenolic content was identified as a consistent marker of seasonal variation across cultivars. Full article

Weak underwater acoustic signal detection is fundamentally challenged by low signal-to-noise ratio (SNR), colored ocean noise, multipath distortion, and environmental mismatch. Existing weak-signal detectors have mainly focused on spectral enhancement, time-frequency tracking, or fixed-environment model matching, while environmentally robust Bayesian methods have been developed primarily for localization, matched-field processing, and channel estimation rather than weak passive detection itself. To bridge this gap, this paper proposes a hierarchical Bayesian detector for weak underwater acoustic signal detection under environmental mismatch. The received observation is modeled by jointly incorporating structured weak-signal coefficients, target-related parameters, and uncertain environmental parameters into a unified Bayesian hypothesis-testing framework. In particular, the acoustic environment is treated as a latent random variable rather than a fixed nominal condition so that robustness can be achieved through environmental marginalization. Since the resulting marginal likelihood is analytically intractable, a variational Bayesian approximation is developed to derive a tractable evidence-based detection statistic. Numerical simulations under low-SNR, multipath-distorted, and environmentally uncertain underwater conditions demonstrate that the proposed detector achieves consistently strong performance under both matched and mismatched scenarios. Ablation results in controlled simulations further indicate that environmental marginalization provides the largest observed robustness gain, whereas the structured weak-signal prior offers an additional improvement in weak-signal discrimination. These results provide controlled simulation-based evidence for the potential of hierarchical Bayesian inference in robust passive underwater acoustic detection under prescribed environmental uncertainty models. Full article

Maize (Zea mays L.) is one of the most important cereal crops worldwide, with yield being a complex quantitative trait controlled by multiple genetic factors. The aim of this study was to identify molecular markers associated with maize yield using next-generation sequencing (NGS), association mapping, and physical mapping approaches. A total of 122 maize hybrids were evaluated under field conditions in a randomized complete block design with three replications. Phenotypic data were collected for grain yield, while genotypic data were obtained using DArTseq technology, resulting in the identification of 60,436 SilicoDArT and 32,178 SNP markers. After quality filtering, 25,078 markers were used for further analyses. Analysis of variance revealed statistically significant differences among hybrids in terms of yield (p < 0.001), with values ranging from 12.67 to 18.52 kg/10 m². Genetic similarity among hybrids ranged from 0.434 to 0.957, indicating substantial genetic diversity. Cluster analyses based on phenotypic and genotypic data showed a lack of correspondence between yield performance and genetic similarity. Genome-wide association studies (GWAS) identified 2478 markers significantly associated with yield, including 47 highly significant markers (Logarithm of the Odds − LOD > 4.0). Individual markers explained between 2.4% and 18.7% of yield variation. Ten markers with the highest contribution to yield variability (13.30–18.70%) were selected as the most promising candidates for further breeding applications. These markers represent promising candidates for marker-assisted selection and genomic selection (GS) of high-yielding maize genotypes. These are some of the first positive results. The integration of phenotypic evaluation with high-throughput genotyping and association mapping provides valuable insights into the genetic architecture of yield and offers practical tools for the development of high-yielding maize cultivars. Full article

Drought stress is a major abiotic constraint limiting mung bean (Vigna radiata L.) productivity in arid and semi-arid agroecosystems. This study investigated the individual and synergistic effects of Bradyrhizobium sp. and arbuscular mycorrhizal fungi (AMF) on plant growth, nutrient acquisition, mycorrhizal colonization, and yield of mung bean under contrasting soil moisture regimes. A greenhouse pot experiment was conducted using a factorial completely randomized design with six microbial treatments (uninoculated control, Acaulospora scrobiculata, Claroideoglomus etunicatum, Bradyrhizobium sp., and their respective co-inoculations) and three field capacity levels (50, 75, and 100%). Drought stress was imposed gravimetrically 20 days after sowing. Water limitation significantly reduced growth, biomass accumulation, nutrient uptake, mycorrhizal colonization, and yield in uninoculated plants. In contrast, microbial inoculation markedly mitigated drought-induced adverse effects, with co-inoculation showing the strongest response. Plants receiving combined AMF and Bradyrhizobium inoculation exhibited significantly higher plant height, shoot and root biomass, total dry matter, nitrogen and phosphorus uptake, and yield attributes across all moisture regimes, particularly under severe drought (50% field capacity). Mycorrhizal dependency increased with increasing drought severity, highlighting a greater functional reliance on AM symbiosis under water-limited conditions. Enhanced drought tolerance was closely associated with increased root colonization and improved nutrient acquisition driven by synergistic AMF–Bradyrhizobium interactions. These findings demonstrate that tripartite symbiosis represents a sustainable bio-inoculant strategy to enhance drought resilience and productivity of mung bean under climate change-induced water stress. Full article

In southern Brazil, a large proportion of farmers maintain their fields under fallow conditions during the transition period between summer and winter crops. During this interval, mechanical practices such as chiseling or the introduction of cover crop species may contribute to improving soil management and conservation in no-tillage systems. Therefore, this study aimed to investigate the effects of mechanical soil chiseling and production system intensification on soil physical–hydric properties and soybean performance. The experiment was conducted in São José do Ouro, Rio Grande do Sul, Brazil, from September 2023 to April 2025. The experimental design consisted of three factors: soil management (spring 2023 chiseling, autumn 2024 chiseling, and a no-till control), post-maize cover (millet and fallow conditions), and winter cover crops (black oat, white oat, vetch, and radish) grown either as monocultures or in mixtures. A randomized block design with split plots and three replicates was used. The evaluated variables included dry biomass of winter cover crops, soil bulk density, total porosity, microporosity, macroporosity, soil water content at field capacity, soil penetration resistance, plant gas exchange, leaf area index, thousand-grain weight, and soybean grain yield. The results indicated that soil chiseling altered soil physical properties by reducing soil bulk density, penetration resistance, microporosity, and field capacity, while increasing total porosity and macroporosity. Soil chiseling promoted short-term increases in thousand-grain weight and soybean grain yield, with no persistent effects after 20 months. Production system intensification, through the use of cover crops and millet, did not affect grain yield but increased stomatal conductance and soybean leaf area index. Therefore, occasional tillage in high-clay subtropical Oxisols should be strategically applied and associated with long-term conservation agriculture practices to sustain improvements in soil physical quality. Full article

Accurate grade estimation in heterogeneous porphyry copper deposits is frequently constrained by spatial non-stationarity and the excessive smoothing inherent in traditional geostatistical methods. This study introduces the Geological Distance Field-Machine Learning (GDF-ML) framework, which transforms raw spatial coordinates into a geological coordinate system defined by the structural architecture. By mapping grade distribution within this geologically informed space, the framework enables machine learning models to discern non-linear mineralizing patterns that are typically obscured in traditional Euclidean 3D space. Functioning as an expert-constrained regression architecture rather than a purely data-driven interpolator, the framework estimates grade distributions conditional upon established metallogenic controls. In this context, the achieved spatial separation cross-validation R² of 0.851 quantifies the proportion of grade variance structurally explainable by the geological architecture, highlighting the workflow’s capacity to distinguish continuous structural trends from localized random variability. Industrial reconciliation against high-density production data confirms this performance, demonstrating an average grade bias of only 0.79%, compared to 9.68% achieved by Ordinary Kriging. Furthermore, SHAP analysis verifies that these predictions are systematically driven by the non-linear relationship between structural proximity and mineralization. Consequently, this study suggests that incorporating structural distance metrics into regression workflows offers an alternative approach to evaluate the geometric constraints of geological features alongside the localized variability of porphyry mineralization. Full article