Search Results (1,649)

The Bilbao parish, located on the slopes of the Tungurahua volcano (Ecuador), was heavily impacted by ashfall during eruptions between 1999 and 2016. Volcanic ash may contain toxic metals such as Pb, Cd, Hg, As, and Se, which are linked to neurological, renal, skeletal, pulmonary, and dermatological disorders. This study evaluated metal concentrations in soil (40–50 cm depth, corresponding to the rooting zone of forage grasses), forage (English ryegrass and Kikuyu grass), and raw milk to assess potential risks to livestock and human health. Sixteen georeferenced sites were selected using a simple random probabilistic sampling method considering geological variability, vegetation cover, accessibility, and cattle presence. Samples were digested and analyzed with a SpectrAA 220 atomic absorption spectrophotometer (Varian Inc., Victoria, Australia). Soils (Andisols) contained Hg (1.82 mg/kg), Cd (0.36 mg/kg), As (1.36 mg/kg), Pb (1.62 mg/kg), and Se (1.39 mg/kg); all were below the Ecuadorian limits, except for Hg and Se. Forage exceeded FAO thresholds for Pb, Cd, As, Hg, and Se. Milk contained Pb, Cd, and Hg below detection limits, while Se averaged 0.047 mg/kg, exceeding water safety guidelines. Findings suggest soils act as sources with significant bioaccumulation in forage but limited transfer to milk. Although immediate consumer risk is low, forage contamination highlights long-term hazards, emphasizing the need for monitoring, soil management, and farmer guidance. Full article

This article presents the development of a new pneumatic device for the precise application of mineral fertilizers, designed for use in precision agriculture systems involving farming robots. The proposed device is mounted on an autonomous agricultural platform and utilizes a machine vision system to determine plant coordinates. Its operating principle is based on accumulating a single dose of fertilizer in a chamber and delivering it precisely to the plant’s root zone using a directed airflow. The study includes a theoretical investigation of fertilizer movement inside the applicator tube under the influence of airflow and rotational motion of the tube. A mathematical model has been developed to describe both the relative and translational motion of the fertilizer. The equations, which account for frictional forces, inertia, and air pressure, enable the determination of optimal structural and kinematic parameters of the device depending on operating conditions and the properties of the applied material. The use of numerical methods to solve the developed mathematical model allows for synchronization of the device’s operating time parameters with the movement of the agricultural robot along the crop rows. The obtained results and the developed device improve the accuracy and speed of fertilizer application, minimize fertilizer consumption, and reduce soil impact, making the proposed device a promising solution for precision agriculture. Full article

Diabrotica virgifera virgifera Le Conte, 1868 (Coleoptera: Chrysomelidae), known as the western corn rootworm, is one of the most important alien insect pests affecting maize crops globally. It causes significant economic losses by feeding on the roots, which affects plant stability and nutrient absorption, as well as by attacking essential aerial organs (leaves, silk, pollen). Since its accidental introduction into Europe, the species has expanded its range across maize-growing regions, raising concerns about future distribution under climate change. This study aimed to estimate the risk of pest establishment across Europe over three future time frames (2034, 2054, 2074) based on geographic coordinates, climate data, and maize distribution. Spatial simulations were performed in QGIS using national centroid datasets, risk classification criteria, and temperature anomaly maps derived from Copernicus and ECA&D databases for 1992–2024. The results indicate consistently high risk in southern and southeastern regions, with projected expansion toward central and western areas by 2074. Risk zones showed clear spatial aggregation and directional spread correlated with warming trends and maize availability. The pest’s high reproductive potential, thermal tolerance, and capacity for human-assisted dispersal further support these predictions. The model emphasizes the need for expanded surveillance in at-risk zones and targeted policies in areas where D. v. virgifera has not yet established. Future work should refine spatial predictions using field validation, genetic monitoring, and dispersal modeling. The results contribute to anticipatory pest management planning and can support sustainable maize production across changing agroclimatic zones in Europe. Full article

This study investigated the Yongning Fortress ruins in Taiyuan through a comprehensive analytical approach employing scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), laser particle size analysis, X-ray diffraction (XRD), X-ray fluorescence spectroscopy (XRF), and ion chromatography (IC). The research focused on elucidating the disturbance mechanisms and environmental impacts induced by the root systems of five representative herbaceous species on rammed earth structures. The results demonstrated distinct, species-specific disturbance patterns. Melica roots created three-dimensional network damage, Artemisia capillaris primarily caused deep root penetration, Fallopia aubertii exhibited coupled physical–chemical effects, Convolvulus arvensis induced shallow horizontal expansion damage, while Cirsium formed a heterogeneous structure characterized by dense taproots and loose lateral roots. Environmental conditions, particularly moisture content, significantly influenced disturbance intensity. All root activities led to common deterioration processes, including particle rounding, gradation degradation, and formation of organic–mineral composites. Notably, vegetation markedly altered soluble salt distribution patterns, with Cirsium increasing total salt content to 3.7 times that of undisturbed rammed earth (0.48%), while sulfate ion concentration (1.16 × 10⁻³) approached hazardous thresholds. The study established a theoretical framework linking plant traits, disturbance mechanisms, and environmental response, and proposed risk-based zoning strategies for preservation. These outcomes provide significant theoretical foundations and practical guidance for the scientific conservation of rammed earth heritage sites. Full article

Natural geogenic effects lead to alterations in soil heavy metal concentrations. This study assesses the presence of elevated trace-element concentrations in the Oued Irriri watershed in southeastern Morocco. ASTER satellite imagery, geochemical, and aeromagnetic data are combined to determine the origin of these anomalies. Processing of ASTER images delineated alteration zones coinciding with areas of high heavy metal anomalies by detecting hydrothermal alteration minerals, including muscovite, montmorillonite, illite, hematite, jarosite, chlorite, and epidote. Principal Component Analysis (PCA) of geochemical data distribution in soils enabled the characterization of variations in trace-element concentrations, the extraction of geochemical anomalies, and the identification of potential sources of contamination. Comparing satellite image processing results with geochemical analyses facilitated the production of a geogenic enrichment map. The study results indicate high enrichment levels of zinc, Molybdenum, and bismuth in the western basin, of purely lithological origin. Hydrothermal alteration surfaces intersect geochemical anomaly zones in the north and northeast, primarily showing the impact of fault rooting on the surface deposition of Cu, Ba, Hg, and Pb-rich deposits. This study developed a geogenic enrichment map indicating naturally affected areas, identifying potential risks to eco-environmental systems, and better preventing the effects of geogenic enrichment. Full article

The growing energy demand has increased the number of wind turbines, raising the need to monitor blade health. Since blades are prone to damage that can cause severe failures, early detection is crucial. Machine learning-based monitoring systems can identify and locate cracks without interrupting energy production, enabling timely maintenance. This study provides a comparative analysis and approach to the application and effectiveness of different vibration-based machine learning algorithms to detect the presence of cracks, identify the cracked blade, and locate the zone where the crack occurs in rotating blades of a small wind turbine. The datasets comprise root vibration signals, derived from healthy and cracked blades of a wind turbine in operational conditions. In this study, the blades are not considered identical. The sampling set dimension and the number of features were variables considered during the development and assessment of different models based on decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), and multilayer perceptron algorithms (MLP). Overall, the KNN models are the clear winners in terms of training efficiency, even as the sample size increases. DT is the most efficient algorithm in terms of test speed, followed by SVM, MLP, and KNN. Full article

Soil secondary salinization is a major limiting factor of sustainable agricultural production in arid and semi-arid irrigation zones, yet predictive tools for regional water–salt dynamics remain limited. The Yichang Irrigation District, located within the Hetao Irrigation Area, has experienced persistent salinity challenges due to shallow groundwater tables and intensive irrigation. In this study, we aimed to simulate long-term soil water–salt dynamics in the Yichang Irrigation District and evaluate the effectiveness of different engineering and management scenarios using the SaltMod model. Field monitoring of soil salinity and groundwater levels during summer and fall (2022–2024) was used to calibrate and validate SaltMod parameters, ensuring accurate reproduction of seasonal soil salinity fluctuations. Based on the calibrated model, ten-year scenario simulations were conducted to assess the effects of changes in soil texture, irrigation water quantity, water quality, rainfall, and groundwater table depth on root-zone salinity. Our results show that under baseline management, soil salinity is projected to decline by 5% over the next decade. Increasing fall autumn leaching irrigation further reduces salinity by 5–10% while conserving 50–300 m³·ha⁻¹ of water. Sensitivity analysis indicated groundwater depth and irrigation water salinity as key drivers. Among the engineering strategies, drainage system improvement and groundwater regulation achieved the highest salinity reduction (15–20%), while irrigation regime optimization provided moderate benefits (~10%). This study offers a quantitative basis for integrated water–salt management in the Hetao Irrigation District and similar regions. Full article

The economic and strategic value of high-latitude maritime regions is increasingly significant, yet traditional Global Navigation Satellite Systems remain constrained by unfavorable geometric configurations and slow convergence speeds at high latitudes, failing to meet the growing demand for real-time centimeter-level high-precision positioning in these areas. Benefiting from their rapid motion and superior coverage over high-latitude zones, Low Earth Orbit (LEO) satellites offer an effective means to enhance positioning performance in such regions. This paper uses the real BDS data collected by an unmanned surface vessel in the high-latitude waters of the Southern Hemisphere, jointly simulates polar and medium-inclination LEO constellations, and systematically assess the enhancement effects of LEO augmentation on Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) techniques. The results demonstrate that the polar-orbiting constellation markedly improves the observation environment, increasing the number of visible satellites by 70.2% and reducing the Position Dilution of Precision from 2.4 to 1.7, whereas the medium-inclination orbit constellation offered negligible improvement due to insufficient visibility. The rapid geometric change brought by LEO constellations is the core key to achieving fast convergence. Incorporating LEO observations drastically shortened the BDS PPP convergence time from 45.3 min to under 1 min, achieving a reduction of over 97%. Simultaneously, it improved the three-dimensional Root Mean Square accuracy by 54.7%, from 0.086 m to 0.039 m. Convergence within one minute was consistently achieved when at least 5.4 LEO satellites were included in the solution. Moreover, the addition of LEO signals increased the fixed solution rate of short-baseline RTK from 96.5% to 100%, while improving horizontal and vertical accuracy by 31.5% and 12.3%, respectively. This study confirms that LEO constellations, especially those in polar orbits, can substantially enhance BDS precise positioning performance in high-latitude maritime environments, thereby providing critical technical support for related navigation applications. Full article

Calocybe gambosa (Fr.) Donk is an edible mushroom, highly appreciated especially in Italy. It forms fairy rings (FRs) characterized by a zone of dead vegetation corresponding to the underground-extending mycelial front, followed by a “greener belt” where vegetation is thriving. To better understand this particular phenomenon, the effect of C. gambosa mycelium on plants were studied both in situ, across different zones of FRs (external area—EX, fungal front—FF, greener belt—GB, internal area—IN) of three fairy rings, and ex situ on Poa trivialis L. Plant community analysis revealed significant changes in plant species composition across the zones, characterized by a decline in diversity and a vegetation shift, from dicotyledons to monocotyledons, progressing from the EX toward the IN, where vegetation gradually begins to reestablish its original composition. Molecular and morphological analyses showed the endophytic colonization of C. gambosa mycelium within the herbaceous plants growing at the FF. Ex situ studies indicated pathogenic behavior of C. gambosa. After root colonization, it caused growth reduction in P. trivialis plants (79% reduction in root length, 76% reduction in leaf length), leaf yellowing, decreased photosynthetic pigments, and root necrosis. The cellulase (endo-1,4-β-glucanase), xylanase, polygalacturonase, and polymethylgalacturonase enzymatic activities of C. gambosa support its pathogenic effects. Conversely, volatile organic compounds (VOCs) produced by C. gambosa mycelium stimulated shoot development in P. trivialis (17% increase in shoot length), which accounts for the formation of the flourishing vegetation zone behind the FF. In contrast, soluble substances produced by C. gambosa mycelium did not affect the growth of P. trivialis. Our results suggest that C. gambosa plays a dual ecological role in regulating plant community dynamics within FRs: it acts as a pathogen by colonizing herbaceous plant roots and, at the same time, promotes vegetation growth through VOC production. Full article

This study conducted field experiments in 2024 in Meidaizhao Town, Tumed Right Banner, Baotou City, Inner Mongolia Autonomous Region, adopting a plant-level sampling design with 10 maize plots selected as sampling areas (20 plants per plot). At four critical growth stages—jointing, heading, filling, and maturity—multimodal data, including that covering leaf spectra, root-zone soil spectra, and leaf chlorophyll and nitrogen content, were synchronously collected from each plant. In response to the prevalent limitations of the existing yield prediction methods, such as insufficient accuracy and limited generalization ability due to reliance on single-modal data, this study takes the acquired multimodal maize data as the research object and innovatively proposes a multimodal fusion prediction network. First, to handle the heterogeneous nature of multimodal data, a parallel feature extraction architecture is designed, utilizing independent feature extraction branches—leaf spectral branch, soil spectral branch, and biochemical parameter branch—to preserve the distinct characteristics of each modality. Subsequently, a dual-path feature fusion method, enhanced by a cross-attention mechanism, is introduced to enable dynamic interaction and adaptive weight allocation between cross-modal features, specifically between leaf spectra–soil spectra and leaf spectra–biochemical parameters, thereby significantly improving maize yield prediction accuracy. The experimental results demonstrate that the proposed model outperforms single-modal approaches by effectively leveraging complementary information from multimodal data, achieving an R² of 0.951, an RMSE of 8.68, an RPD of 4.50, and an MAE of 5.28. Furthermore, the study reveals that deep fusion between soil spectra, leaf biochemical parameters, and leaf spectral data substantially enhances prediction accuracy. This work not only validates the effectiveness of multimodal data fusion in maize yield prediction but also provides valuable insights for accurate and non-destructive yield prediction. Full article

The root system is an important determinant affecting the growth, adaptivity and stress resistance of citrus plants. Currently, the genetic regulatory network underlying root growth and development in citrus remains largely unknown. We report that a PtrAUX/IAA-ARF complex mediates the growth and development of roots in citrus through regulating the transcription of PtrSAUR. The auxin signaling pathway plays an essential role in regulating the growth and development of roots. In this study, we found that in citrus Poncirus trifoliata, PtrIAA12, encoding a canonical Aux/IAA protein, was highly expressed in the meristem and elongation zone of the root. Functional characterization showed that overexpression and silence of PtrIAA12 significantly enhanced and suppressed the elongation of primary roots, respectively. Further analysis revealed that PtrIAA12 could interact with some members of PtrARFs, of which, PtrARF8 was identified to be the transcriptional factor of PtrSAUR17. Investigation of PtrSAUR17 transgenic plants verified that PtrSAUR17 is a key gene regulating the growth of roots in citrus. In conclusion, PtrIAA12 and PtrARF8 are the key members of the AUX/IAA-ARF complex in citrus controlling the growth and development of roots through regulating the transcription of PtrSAUR17. Full article

Urban green spaces play a crucial role in mitigating the biodiversity loss caused by dense development and land-use transformation. This study explores the ecological and spatial potential of Fort Augustówka, a neglected military fortification in Warsaw, Poland, as a multifunctional green space that enhances local biodiversity. Through field surveys, vegetation assessments, SWOT analysis, and user profiling, we identified key ecological features and constraints of the site, located within a Vistula River riparian zone. This study employed phytosociological analysis (Braun–Blanquet method), spatial mapping (using AutoCAD and SketchUp), and stakeholder observations to assess the value of semi-natural habitats including ruderal vegetation, meadows, and aquatic zones, as well as urban tree stands and conventionally managed greenery. Our results show that semi-natural habitats, including meadows and reed beds, achieved higher ecological value scores than conventionally managed greenery, while invasive species significantly reduced biodiversity in several zones. Based on these findings, we propose a spatial revitalisation model grounded in native species restoration, ecological connectivity, and low-impact recreational design. This study highlights an innovative approach that integrates existing vegetation, historical structures, and human well-being, creating a design concept beneficial for residents and visitors alike. This work also demonstrates how post-military landscapes can support biodiversity in metropolitan areas and offers a transferable model for ecological urban design rooted in place-based analysis. The findings contribute to broader discussions on nature-based solutions and urban rewilding in post-socialist urban contexts. Full article

Under the combined action of bidirectional (longitudinal and transverse) shear loads and corrosive environments, the shear performance of stud connectors in steel–concrete composite track beams of straddle-type monorail transit systems is susceptible to degradation, thereby posing a potential risk to the structural safety of the track girders. This study employs push-out tests and numerical simulations to investigate the influence of bidirectional shear loads and stud corrosion on the shear performance of stud connectors. The results showed that both transverse shear loads and stud corrosion lead to a reduction in the shear capacity of stud connectors, with their coupling effect amplifying the degradation. Transverse shear loads induce an accelerated decay trend in the load-bearing capacity of stud connectors, while an increase in corrosion depth results in a linear degradation of the load-bearing capacity. The corrosion depth at the stud root exerts a more pronounced influence on shear performance compared to the corrosion height. Furthermore, the dominant failure mode of stud connectors manifests as root fracture, while transverse shear loads induce alterations in the concrete damage zone. Based on the verified FE model, a shear capacity reduction factor accounting for the coupling effects of bidirectional shear and stud corrosion was established to improve the Oehlers model. This research provides critical theoretical support for the safe design and durability assessment of monorail track girders. Full article

Understanding the regulatory mechanisms of water–nitrogen coupling effects on soil–plant–microbe systems in arid regions is crucial for sustainable agricultural development. This study systematically investigated the interactive effects of field capacity (75% vs. 45%) and nitrogen application rates (100 vs. 300 kg ha⁻¹) combined with different enhanced-efficiency nitrogen fertilizers (EENFs) on rhizosphere soil fertility and microbial community structure of Jujube (Ziziphus jujuba Mill.) seedlings through a two-year pot experiment. Two-year-old jujube seedlings were employed with five treatments: NS (urea), NM (urease inhibitor), XH (nitrification inhibitor), W (microbial fertilizer), and CK (control), to analyze soil physicochemical properties and microbial community responses. Soil available N accumulated under high-N/adequate moisture but declined under drought. NM curbed NH₃ volatilization by 32.38–43.22%, while XH increased NH₄⁺-N by 35.76%. Drought raised microbial α-diversity (bacteria + 33.88–37.5%, fungi + 43.62–68.75%). NM demonstrated optimal performance in ammonia volatilization (32.38–43.22% reduction), while XH showed notable efficacy in ammonium-N regulation (35.76% enhancement). Microbial α-diversity exhibited enhanced responses under drought stress, with bacterial and fungal community improvements reaching 33.88–37.5% and 43.62–68.75%. Redundancy analysis showed environmental factors explained more community variance under water stress (bacteria: 79.19→88.76%; fungi: 64.64→92.52%). These findings provide theoretical support for jujube cultivation in arid zones, demonstrate the potential of targeted EENFs, and offer new insights for precision water–fertilizer and microbial management. Full article

Geophysical and remote sensing observations offer powerful means to monitor large-scale hydrological changes, particularly in regions where in situ data are scarce. In this study, we integrate satellite-derived water storage from the Gravity Recovery and Climate Experiment (GRACE) with land surface variables from the Global Land Data Assimilation System (GLDAS) to assess and forecast groundwater storage (GWS) dynamics across eight major regions in Sudan. Missing GRACE observations of terrestrial water storage (TWS) were first reconstructed using a Random Forest machine learning model, after which GWS anomalies were estimated by subtracting GLDAS-based surface and root-zone components from TWS. The resulting GWS time series was decomposed into trend, seasonal, and residual components, and the trend signals were used to train a bootstrapped Bidirectional Long Short-Term Memory (BiLSTM) model. This framework generated probabilistic forecasts accompanied by confidence intervals, which were generally narrow and consistent with the historical range. The forecasted GWS anomalies indicate positive recovery across all regions, with Sen’s slope values ranging from 0.014 to 0.051 per month. The strongest recoveries are evident in the southern and southwestern regions, while northern and eastern areas display more modest gains. This work represents one of the first applications of deep learning with uncertainty quantification for GRACE-based groundwater analysis in Sudan, demonstrating the potential of such an integrated approach to support informed and sustainable groundwater management in data-limited environments. Full article