Search Results (5,368)

Nanofertilizers (NFs) and engineered nanoparticles (NPs) are increasingly used in agriculture, yet their environmental safety remains poorly understood. This study evaluated the comparative phytotoxicity of zinc oxide (ZnO), titanium dioxide (TiO₂), and clinoptilolite nanoparticles, three commercial nanofertilizers, and potassium dichromate (K₂Cr₂O₇) using Lactuca sativa seeds under adapted OECD-208 protocol conditions. Seeds were exposed to varying concentrations of each xenobiotic material (0.5–3% for NFs; 10–50% for NPs), with systematic assessment of seedling survival, root and hypocotyl length, dry biomass, germination index (GI), and median effective concentration (EC₅₀) values. Nanofertilizers demonstrated significantly greater phytotoxicity than engineered nanoparticles despite lower application concentrations. The toxicity ranking was established as NF1 > NF3 > NF2 > NM2 > NM1 > NM3, with NF1 being most toxic (EC₅₀ = 1.2%). Nanofertilizers caused 45–78% reductions in root length and 30–65% decreases in dry biomass compared with controls. GI values dropped to ≤70% in NF1 and NF3 treatments, indicating concentration-dependent growth inhibition. While nanofertilizers offer agricultural benefits, their elevated phytotoxicity compared with conventional nanoparticles necessitates rigorous pre-application safety assessment. These findings emphasize the critical need for standardized evaluation protocols incorporating both physiological and ecotoxicological endpoints to ensure safe xenobiotic nanomaterial deployment in agricultural systems. Full article

Common bean (Phaseolus vulgaris L.) is a critical protein-rich legume supporting food and nutritional security globally. However, Fusarium wilt, caused by Fusarium solani, remains a major constraint to production, with yield losses reaching up to 84%. While biocontrol strategies have been explored, most microbial agents are sourced from mesophilic environments and show limited effectiveness under abiotic stress. Here, we report the isolation and characterization of extremophilic Bacillus spp. from the hypersaline Lake Bogoria, Kenya, and their biocontrol potential against F. solani. From 30 isolates obtained via serial dilution, 9 exhibited antagonistic activity in vitro, with mycelial inhibition ranging from 1.07-1.93 cm 16S rRNA sequencing revealed taxonomic diversity within the Bacillus genus, including unique extremotolerant strains. Molecular screening identified genes associated with the biosynthesis of antifungal metabolites such as 2,4-diacetylphloroglucinol, pyrrolnitrin, and hydrogen cyanide. Enzyme assays confirmed substantial production of chitinase (1.33–3160 U/mL) and chitosanase (10.62–28.33 mm), supporting a cell wall-targeted antagonism mechanism. In planta assays with the lead isolate (B7) significantly reduced disease incidence (8–35%) and wilt severity (1–5 affected plants), while enhancing root colonization under pathogen pressure. These findings demonstrate that extremophile-derived Bacillus spp. possess robust antifungal traits and highlight their potential as climate-resilient biocontrol agents for sustainable bean production in arid and semi-arid agroecosystems. Full article

Prolonged inundations are altering coastal forest ecosystems of the southeastern US, causing extensive tree die-offs and the development of ghost forests. This hydrological stressor also alters carbon fluxes, threatening the stability of coastal carbon sinks. This study was conducted to investigate the interactions between hydrological drivers and ecosystem responses by analyzing daily eddy covariance flux data from a wetland forest in North Carolina, USA, spanning 2009–2019. We analyzed temporal patterns of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE) under both flooded and non-flooded conditions and evaluated their relationships with observed tree mortality. Generalized Additive Modeling (GAM) revealed that groundwater table depth (GWT), leaf area index (LAI), NEE, and net radiation (Rn) were key predictors of mortality transitions (R² = 0.98). Elevated GWT induces root anoxia; declining LAI reduces productivity; elevated NEE signals physiological breakdown; and higher Rn may amplify evapotranspiration stress. Receiver Operating Characteristic (ROC) analysis revealed critical early warning thresholds for tree mortality: GWT = 2.23 cm, LAI = 2.99, NEE = 1.27 g C m⁻² d⁻¹, and Rn = 167.54 W m⁻². These values offer a basis for forecasting forest mortality risk and guiding early warning systems. Our findings highlight the dominant role of hydrological variability in ecosystem degradation and offer a threshold-based framework for early detection of mortality risks. This approach provides insights into managing coastal forest resilience amid accelerating sea level rise. Full article

Background: Lower phylogenetic species are known to rebuild cut-off caudal parts with regeneration of the central nervous system (CNS). In contrast, CNS regeneration in higher vertebrates is often attributed to immaturity, although this has never been conclusively demonstrated. The emergence of stem cells and their effective medical applications has intensified research into spinal cord regeneration. However, despite these advances, the impact of clinical trials involving spinal cord-injured (SCI) patients remains disappointingly low. Long-distance regeneration has yet to be proven. Methods: Our study involved a microsurgical dorsal myelotomy in fetal rats. The development of pioneering long primary afferent axons during early gestation was examined long after birth. Results: A single cut triggered the intrinsic ability of the dorsal root ganglion (DRG) neurons to reprogram. Susceptibility to hypoxia caused the axons to stop developing. However, the residual axonal outgrowth sheds light on the intriguing temporal and spatial events that reveal long-distance CNS regeneration. The altered phenotypes displayed axons of varying lengths and different features, which remained visible throughout life. The previously designed developmental blueprint was crucial for interpreting these enigmatic features. Conclusions: This research into immaturity enabled the exploration of the previously impenetrable domain of early life and the identification of a potential missing link in CNS regeneration research. Central axon regeneration appeared to occur much faster than is generally believed. The paradigm provides a challenging approach for exhaustive intrauterine reprogramming. When the results demonstrate pre-clinical effectiveness in CNS regeneration research, the transformational impact may ultimately lead to improved outcomes for patients with spinal cord injuries. Full article

This study investigates the foundational causes of unsustainability that obstruct efforts to address global challenges such as climate change, environmental degradation, water crises, and public health deterioration. Using qualitative research with in-depth expert interviews from education, environmental studies, and business, it finds that these global challenges, while visible on the surface, are deeply rooted in worldviews that shape human behavior, societal structures, and policies. Building on this insight, the thematic analysis manifests three interrelated systemic paradigms as the fundamental drivers of unsustainability: a crisis of wholeness, reflected in fragmented identities and collective disorientation; a disconnection from nature, shaped by human-centered perspectives; and the influence of dominant political-economic systems which prioritize growth logics over ecological and social concerns. These paradigms underlie both structural and cognitive barriers to systemic transformation, which influence the design and implementation of education for sustainability. By clarifying a body of knowledge and systemic paradigms regarding unsustainability, this paper calls for transformative education that promotes a holistic, value-based approach, eco-empathy, and critical thinking, aiming to equip future generations with the tools to challenge and transform unsustainable systems. Full article

In order to improve the inversion accuracy of electrical resistivity tomography (ERT) and overcome the limitations of traditional linear methods, this paper proposes an improved shuffled frog leaping algorithm (SFLA). First, an equilibrium grouping strategy is designed to balance the contribution weight of each subgroup to the global optimal solution, suppressing the local optimum traps caused by the dominance of high-quality groups. Second, an adaptive movement operator is constructed to dynamically regulate the step size of the search, enhancing the guiding effect of the optimal solution. In synthetic data tests of three typical electrical models, including a high-resistivity anomaly with 5% random noise, a normal fault, and a reverse fault, the improved algorithm shows an approximately 2.3 times higher accuracy in boundary identification of the anomaly body compared to the least squares (LS) method and standard SFLA. Additionally, the root mean square error is reduced by 57%. In the engineering validation at the Baota Mountain mining area in Jurong, the improved SFLA inversion clearly reveals the undulating bedrock morphology. At a measuring point 55 m along the profile, the bedrock depth is 14.05 m (ZK3 verification value 12.0 m, error 17%), and at 96 m, the depth is 6.9 m (ZK2 verification value 6.7 m, error 3.0%). The characteristic of deeper bedrock to the south and shallower to the north is highly consistent with the terrain and drilling data (RMSE = 1.053). This algorithm provides reliable technical support for precise detection of complex geological structures using ERT. Full article

Host selectivity or preference plays a critical role in enabling parasitic plants to identify suitable hosts and influence plant community dynamics. Phosphorus (P) is known to affect the growth of root hemiparasitic plants and their interaction with single host species, but its role in shaping host selectivity across multiple hosts is unclear. In a pot experiment, we used a grass–legume co-culture design and evaluated whether the root hemiparasitic plant Pedicularis kansuensis exhibits selective parasitism on legumes (Medicago sativa) versus grasses (Elymus nutans) and assessed the impact of soil P availability on this preference. The results showed that P. kansuensis inhibited the growth of both host species, but the magnitude of suppression varied with P availability. Under low P conditions, P. kansuensis preferentially parasitized the tender M. sativa, causing a greater biomass reduction in the legume. In contrast, at high P levels, P. kansuensis decreased its foraging on legumes, shifting its parasitism towards the dominant E. nutans, which potentially led to stronger suppression of grass growth. Our findings demonstrate that soil P availability modulates host selectivity in P. kansuensis, emphasizing the influence of soil nutrient conditions on parasite–host dynamics. This research provides insights into managing the impacts of parasitic plants on plant community structure through nutrient interventions. Full article

This work focuses on the possibilities of generating synthetic data for machine learning in hazard prediction in area-based speed monitoring systems. The purpose of the research conducted was to develop a methodology for generating realistic synthetic data to support the design of a continuous vehicle speed monitoring system to minimize the risk of traffic accidents caused by speeding. The SUMO traffic simulator was used to model driver behavior in the analyzed area and within a given road network. Data from OpenStreetMap and field measurements from over a dozen speed detectors were integrated. Preliminary tests were carried out to record vehicle speeds. Based on these data, several simulation scenarios were run and compared to real-world observations using average speed, the percentage of speed limit violations, root mean square error (RMSE), and percentage compliance. A new metric, the Combined Speed Accuracy Score (CSAS), has been introduced to assess the consistency of simulation results with real-world data. For this study, a basic hazard prediction model was developed using LoRaWAN sensor network data and environmental contextual variables, including time, weather, location, and accident history. The research results in a method for evaluating and selecting the simulation scenario that best represents reality and drivers’ propensities to exceed speed limits. The results and findings demonstrate that it is possible to produce synthetic data with a level of agreement exceeding 90% with real data. Thus, it was shown that it is possible to generate synthetic data for machine learning in hazard prediction for area-based speed control systems using traffic simulators. Full article

Focusing on the problem of insufficient detection accuracy caused by blurred target boundaries, variable scales, and severe noise interference in side-scan sonar images, this paper proposes a high-precision detection network named ESL-YOLO, which integrates edge perception and adaptive quality assessment. Firstly, an Edge Fusion Module (EFM) is designed, which integrates the Sobel operator into depthwise separable convolution. Through a dual-branch structure, it realizes effective fusion of edge features and spatial features, significantly enhancing the ability to recognize targets with blurred boundaries. Secondly, a Self-Calibrated Dual Attention (SCDA) Module is constructed. By means of feature cross-calibration and multi-scale channel attention fusion mechanisms, it achieves adaptive fusion of shallow details and deep-rooted semantic content, improving the detection accuracy for small-sized targets and targets with elaborate shapes. Finally, a Location Quality Estimator (LQE) is introduced, which quantifies localization quality using the statistical characteristics of bounding box distribution, effectively reducing false detections and missed detections. Experiments on the SIMD dataset show that the mAP@0.5 of ESL-YOLO reaches 84.65%. The precision and recall rate reach 87.67% and 75.63%, respectively. Generalization experiments on additional sonar datasets further validate the effectiveness of the proposed method across different data distributions and target types, providing an effective technical solution for side-scan sonar image target detection. Full article

Root rot, mainly caused by Fusarium oxysporum, is one of the most destructive diseases and leads to significant economic loss of Astragalus membranaceus. To develop an effective strategy for the management of this serious disease, a bacterial strain 2-12 was screened from A. membranaceus rhizosphere soil and identified as Bacillus paralicheniformis based on the phylogenetic analyses of gyrase subunit B gene (gyrB) and RNA polymerase gene (rpoB) sequences. Interestingly, the volatile organic compounds (VOCs) produced by B. paralicheniformis 2-12 exhibited potent antifungal activities against F. oxysporum, as well as fifteen other plant pathogens. Under scanning electron microscopy observation, hyphae treated with the VOCs exhibited abnormal variation such as distortion, twist, and vesiculation, leading to distinctive protoplasm shrinkage. After treatment with B. paralicheniformis 2-12 VOCs, the lesion diameter and disease incidence both reduced significantly compared to control (p < 0.05), thus demonstrating prominent biological efficiency. Moreover, B. paralicheniformis 2-12 VOCs were composed of 17 VOCs, including 9 alkanes, 3 alcohols, 3 acids and esters, 1 aromatic compound, and 1 alkyne compound. A total of 1945 DEGs, including 1001 up-regulated and 944 down-regulated genes, were screened via transcriptome analysis. These DEGs were mainly associated with membranes and membrane parts, amino acid metabolism, and lipid metabolism. The findings in this work strongly suggested that B. paralicheniformis 2-12 VOCs could be applied as a new candidate for the control of A. membranaceus root rot. Full article

Tornado outbreaks can cause substantial damage, injuries, and fatalities, highlighting the need to understand their characteristics for assessing present and future risks. However, global climate models (GCMs) lack the resolution to explicitly simulate tornado outbreaks. As an alternative, researchers examine large-scale atmospheric ingredients that approximate tornado-conducive environments. Building on this approach, we tested whether patterns of covariability between WMAXSHEAR and 500-hPa geopotential height anomalies, previously identified in ERA5 reanalysis, could approximate major U.S. May tornado outbreaks in a GCM. We developed a proxy-based methodology by systematically testing pairs of thresholds for both variables to identify the combination that best reproduced the leading pattern selected for analysis. These thresholds were then applied to simulations from the high-resolution MPI-ESM1.2-HR model to assess its ability to reproduce the original pattern. Results show that the model closely mirrored the observed tornado outbreak pattern, as indicated by a low normalized root mean square error, high spatial correlation, and similar distributions. This study demonstrates a replicable approach for approximating tornado outbreak patterns, applied here to the leading pattern, within a GCM, providing a foundation for future research on how such environments might evolve in a warming climate. Full article

Vicia unijuga, an important forage legume on China’s Qinghai–Tibetan Plateau, exhibited dark-brown sunken lesions on their stems at the Qingyang Experimental Station of Lanzhou University. The fungus isolated from the diseased tissues was identified as Colletotrichum tofieldiae via a multi-locus phylogeny (ITS-ACT-Tub2-CHS-1-GADPH-HIS3). The pathogenicity was confirmed by Koch’s postulates. The inoculated plants showed significantly reduced (p < 0.05) growth parameters (height, root length, and biomass), photosynthetic indices (net rate, transpiration, and stomatal conductance), and nutritional quality (crude protein, crude fat, crude ash, and crude fiber) compared to the controls. C. tofieldiae additionally infected six legume species (V. sativa, Medicago sativa, Onobrychis viciifolia, Astragalus adsurgens, Trifolium pratense, and T. repens). Optimal in vitro growth occurred on oatmeal agar (mycelium) and cornmeal agar (spores), with D-sucrose and D-peptone as the best carbon and nitrogen sources. This first report of C. tofieldiae causing V. unijuga anthracnose advances the understanding of legume anthracnose pathogens. Full article

Oregano (Origanum vulgare) cultivation is of great economic importance in Peru. Tacna stands out as its main producer. However, the presence of phytopathogenic fungi represents a challenge for its production. This study aimed to characterize both the morphological and molecular levels of the causal agent of crown and root rot in a crop field in the Camilaca district, Candarave, Tacna. To this end, systematic sampling was carried out using the five-gold method, collecting plants with typical symptoms. Fungi were isolated from diseased roots and characterized using macroscopic and microscopic morphological analysis as well as sequencing and multilocus phylogenetic analysis (ITS, 28S, HIS3, TEF1, TUB2). In addition, pathogenicity tests were performed on healthy plants to confirm the infectivity of the isolates. The results demonstrated that root rot was caused by a complex of phytopathogenic fungi through phylogenetic analysis of Dactylonectria torresensis, Fusarium oxysporum, F. iranicum, and F. redolens. These findings represent the first report of these species as causal agents of oregano root rot in Peru, highlighting the need for integrated management strategies that reduce the economic impact of these diseases and contribute to the sustainability of the crop in key producing regions such as Tacna. Full article

During high-current vacuum arcing, asymmetric arcing with off-center plasma columns may occur due to stochastic discharge initiation and mechanical motion, receiving less research attention than symmetric arcing. The objective of this paper is to numerically analyze the influence law of asymmetric arc ignition on arc parameters. For 60 mm diameter contacts, three arc conditions of symmetric arcing, 33% arc offset, and 67% arc offset were modeled. The results show that the arc offset causes asymmetry in the arc’s distribution. For 33% offset, the pressure and number density on the side away from the root of the arc is about 50% of root values, while these parameters fall below 20% for the 67% offset. Simultaneously, arc offset elevates peak parameter values: under 33% offset, maxima for ion pressure, ion density, ion temperature, electron temperature, and current density rise 12%, 11%, 6%, 6%, and 14% versus symmetric arcing; during 67% offset, these escalate significantly to 67%, 61%, 12%, 18%, and 47%. This study contributes to providing reference for the analysis of vacuum interruption processes under asymmetric arcing conditions. Full article

With the accelerated digital transformation of the power industry, critical infrastructures such as power grids are increasingly migrating to cloud-native architectures, leading to unprecedented growth in service scale and complexity. Traditional operation and maintenance (O&M) methods struggle to meet the demands for real-time monitoring, accuracy, and scalability in such environments. This paper proposes a novel service performance anomaly detection system based on large language models (LLMs) and multi-agent systems (MAS). By integrating the semantic understanding capabilities of LLMs with the distributed collaboration advantages of MAS, we construct a high-precision and robust anomaly detection framework. The system adopts a hierarchical architecture, where lower-layer agents are responsible for tasks such as log parsing and metric monitoring, while an upper-layer coordinating agent performs multimodal feature fusion and global anomaly decision-making. Additionally, the LLM enhances the semantic analysis and causal reasoning capabilities for logs. Experiments conducted on real-world data from the State Grid Corporation of China, covering 1289 service combinations, demonstrate that our proposed system significantly outperforms traditional methods in terms of the F1-score across four platforms, including customer services and grid resources (achieving up to a 10.3% improvement). Notably, the system excels in composite anomaly detection and root cause analysis. This study provides an industrial-grade, scalable, and interpretable solution for intelligent power grid O&M, offering a valuable reference for the practical implementation of AIOps in critical infrastructures. Evaluated on real-world data from the State Grid Corporation of China (SGCC), our system achieves a maximum F1-score of 88.78%, with a precision of 92.16% and recall of 85.63%, outperforming five baseline methods. Full article