Search Results (6,837)

Wildfire and drought are key drivers of shrubland expansion in southwestern US landscapes. Stand-replacing fires in conifer forests induce shrub-dominated stages, and changing climatic patterns may cause a long-term shift to deciduous shrubland. We assessed change in deciduous fractional shrub cover (DFSC) in the eastern Jemez Mountains from 2019 to 2023 using topographic and Sentinel-2 satellite data and evaluated the impact of spatial scale on model performance. First, we built a 10 m and a 20 m random forest model. The 20 m model outperformed the 10 m model, achieving an R-squared value of 0.82 and an RMSE of 7.85, compared to the 10 m model (0.76 and 9.99, respectively). We projected the 20 m model to the other years of the study using imagery from the respective years, yielding yearly DFSC predictions. DFSC decreased from 2019 to 2022, coinciding with severe drought and a 2022 fire, followed by an increase in 2023, particularly within the 2022 fire footprint. Overall, DFSC trends showed an increase, with elevation being a key variable influencing these trends. This framework revealed vegetation dynamics in a semi-arid system and provided a close look at post-fire regeneration in deciduous resprouting shrubs and could be applied to similar systems. Full article

Sea buckthorn is a vital woody oil species valued for its role in soil conservation and its bioactive seed oil, which is rich in unsaturated fatty acids and other compounds. However, low seed oil content and small seed size are the main bottlenecks restricting the development and utilization of sea buckthorn. In this study, we tested the seed oil content and seed size of 12 sea buckthorn cultivars and identified the key genes and transcription factors involved in seed development and lipid biosynthesis via the integration of UID RNA-seq (Unique Identifiers, UID), WGCNA (weighted gene co-expression network analysis) and qRT-PCR (quantitative real-time PCR) analysis. The results revealed five cultivars (CY02, CY11, CY201309, CY18, CY21) with significantly higher oil contents and five cultivars (CY10, CY201309, CY18, CY21, CY27) with significantly heavier seeds. A total of 10,873 genes were significantly differentially expressed between the S1 and S2 seed developmental stages of the 12 cultivars. WGCNA was used to identify five modules related to seed oil content and seed weight/size, and 417 candidate genes were screened from these modules. Among them, multiple hub genes and transcription factors were identified; for instance, ATP synthase, ATP synthase subunit D and Acyl carrier protein 1 were related to seed development; plastid–lipid-associated protein, acyltransferase-like protein, and glycerol-3-phosphate 2-O-acyltransferase 6 were involved in lipid biosynthesis; and transcription factors DOF1.2, BHLH137 and ERF4 were associated with seed enlargement and development. These findings provide crucial insights into the genetic regulation of seed traits in sea buckthorn, offering targets for future breeding efforts aimed at improving oil yield and quality. Full article

The kinetics of insulin aggregation and fibril formation were studied in vitro using Scanning Electron Microscopy (SEM) and Fourier Transform Infrared (FTIR) spectroscopy. Our investigation centered on the protein’s morphological and structural changes to better understand the transient molecular configurations that occur during the lag phase. SEM images showed that, already at early incubation stages, a network of disordered pseudo-filaments, ranging in length between 200 and 500 nanometers, develops on the surface of large aggregates. At later stages, fibrils catalyzed by protein aggregates were observed. Principal Component Analysis (PCA) of the FTIR data identified signatures of intramolecular β-sheet secondary structures forming during the lag phase and at the onset of the exponential growth phase. These absorption bands are linked to secondary nucleation mechanisms due to their transient nature. This interpretation is further supported by a chemical equilibrium model, which yielded a reliable secondary nucleation rate constant, K₂, on the order of 10⁴ M⁻² s⁻¹. Full article

This study explores the revival of cotton (Gossypium spp. L.) farming in Italy through sustainable practices, addressing economic and water-related challenges by integrating cultivar selection, arbuscular mycorrhizal fungi (AMF) inoculation, and deficit irrigation under organic farming. Field trials evaluated two widely grown Mediterranean cultivars (Armonia and ST-318) under three irrigation levels (I-100: 100% crop water requirement; I-70: 70%; I-30: 30%) across two Sicilian soil types (sandy loam vs. clay-rich). Under I-100, lint yields reached 0.99 t ha⁻¹, while severe deficit (I-30) yielded only 0.40 t ha⁻¹. However, moderate deficit (I-70) maintained 75–79% of full yields, proving a viable strategy. AMF inoculation significantly enhanced plant height (68.52 cm vs. 65.85 cm), boll number (+22.1%), and seed yield (+12.5%) (p < 0.001). Cultivar responses differed: Armonia performed better under water stress, while ST-318 thrived with full irrigation. Site 1, with higher organic matter, required 31–38% less water and achieved superior irrigation water productivity (1.43 kg m⁻³). Water stress also shortened phenological stages, allowing earlier harvests—important for avoiding autumn rains. These results highlight the potential of combining adaptive irrigation, resilient cultivars, and AMF to restore sustainable cotton production in the Mediterranean, emphasizing the importance of soil-specific management. Full article

Ongoing changes in the distribution and abundance of several tick species of medical relevance in Canada have prompted the development of the eTick platform—an image-based crowd-sourcing public surveillance tool for Canada enabling rapid tick species identification by trained personnel, and public health guidance based on tick species and province of residence of the submitter. Considering that more than 100,000 images from over 73,500 identified records representing 25 tick species have been submitted to eTick since the public launch in 2018, a partial automation of the image processing workflow could save substantial human resources, especially as submission numbers have been steadily increasing since 2021. In this study, we evaluate an end-to-end artificial intelligence (AI) pipeline to support tick identification from eTick user-submitted images, characterized by heterogeneous quality and uncontrolled acquisition conditions. Our framework integrates (i) tick localization using a fine-tuned YOLOv7 object detection model, (ii) resolution enhancement of cropped images via super-resolution Generative Adversarial Networks (RealESRGAN and SwinIR), and (iii) image classification using deep convolutional (ResNet-50) and transformer-based (ViT) architectures across three datasets (12, 6, and 3 classes) of decreasing granularities in terms of taxonomic resolution, tick life stage, and specimen viewing angle. ViT consistently outperformed ResNet-50, especially in complex classification settings. The configuration yielding the best performance—relying on object detection without incorporating super-resolution—achieved a macro-averaged F1-score exceeding 86% in the 3-class model (Dermacentor sp., other species, bad images), with minimal critical misclassifications (0.7% of “other species” misclassified as Dermacentor). Given that Dermacentor ticks represent more than 60% of tick volume submitted on the eTick platform, the integration of a low granularity model in the processing workflow could save significant time while maintaining very high standards of identification accuracy. Our findings highlight the potential of combining modern AI methods to facilitate efficient and accurate tick image processing in community science platforms, while emphasizing the need to adapt model complexity and class resolution to task-specific constraints. Full article

In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system. Full article

Background: this research presents an integrated approach to enhancing the sustainability of plastic bag supply chains in Indonesia by addressing critical issues related to ineffective post-consumer waste management and low recycling rates. The objective of this study is to develop a combined framework of material flow analysis (MFA) and sustainable supply chain planning to improve demand forecasting and inflow management across the plastic bag lifecycle. Method: the research adopts a quantitative method using the XGBoost algorithm for forecasting and is supported by a polymer-based MFA framework that maps material flows from production to end-of-life stages. Result: the findings indicate that while production processes achieve high efficiency with a yield of 89%, more than 60% of plastic bag waste remains unmanaged after use. Moreover, scenario analysis demonstrates that single interventions are insufficient to achieve circularity targets, whereas integrated strategies (e.g., reducing export volumes, enhancing waste collection, and improving recycling performance) are more effective in increasing recycling rates beyond 35%. Additionally, the study reveals that increasing domestic recycling capacity and minimizing dependency on exports can significantly reduce environmental leakage and strengthen local waste management systems. Conclusions: the study’s novelty lies in demonstrating how machine learning and material flow data can be synergized to inform circular supply chain decisions and regulatory planning. Full article

Microbes perform diverse and vital functions in animals, plants, and humans, and among them, plant-associated microbiomes, especially endophytes, have attracted growing scientific interest in recent years. Numerous plant species thriving in diverse environments have been shown to host endophytic microbes. While endophytic bacteria commonly colonize plant tissues such as stems, roots, and leaves, seed-associated endophytes generally exhibit lower diversity compared to those in other plant compartments. Nevertheless, seed-borne microbes are of particular importance, as they represent the initial microbial inoculum that influences a plant’s critical early developmental stages. The seed endophytic microbiome is of particular interest due to its potential for vertical transmission and its capacity to produce a broad array of phytohormones, enzymes, antimicrobial compounds, and other secondary metabolites. Collectively, these functions contribute to enhanced plant biomass and yield, especially under abiotic and biotic stress conditions. Despite their multifaceted roles, seed microbiomes remain underexplored in plant ecology, and their potential benefits are not yet fully understood. This review highlights recent advances in our understanding of the diversity, community composition, mechanisms of action, and agricultural significance of seed endophytic microbes. Furthermore, it synthesizes current insights into how seed endophytes promote plant health and productivity and proposes future research directions to fully harness their potential in sustainable agriculture. Full article

Achieving food security for a growing population under a changing climate is a key concern in Senegal, where agriculture employs 77% of the workforce with a majority of small farmers who rely on the production of crops for their subsistence and for income generation. Moreover, due to the underproductive soils and variable rainfall, Senegal depends on imports to fulfil 70% of its food requirements. In this research, we considered four crops that are crucial for Senegalese agriculture: millet, sorghum, peanuts and rice. We used crop simulation models to explore existing yield gaps and optimal agronomic practices. Improving the N fertilizer management in sorghum and millet resulted in 40–100% increases in grain yields. Improved N symbiotic fixation in peanuts resulted in yield increases of 20–100% with highest impact in wetter locations. Optimizing irrigation management and N fertilizer use resulted in 20–40% gains. The best N fertilizer strategy for sorghum and millet included applying low rates at sowing and in early development stages and adjusting a third application, considering the expected rainfall. Peanut yields of the variety 73-33 were higher than Fleur-11 in all locations, and irrigation showed no clear economic advantage. The best N fertilizer management for rainfed rice included applying 30 kg N/ha at sowing, 25 days after sowing (DAS) and 45 DAS. The best combination of sowing dates for a possible double rice crop depended on irrigation costs, with a first crop planted in January or March and a second crop planted in July. Our work confirmed results obtained in field research experiments and identified management practices for increasing productivity and reducing yield variability. Those crop management practices can be implemented in pilot experiments to further validate the results and to disseminate best management practices for farmers in Senegal. Full article

Slow-release nitrogen fertilizers enhance crop production and reduce environmental pollution, but their slow nitrogen release may cause insufficient nitrogen supply in the early stages of crop growth. Mixed nitrogen fertilization (MNF), combining slow-release nitrogen fertilizer with urea, is an effective way to increase yield and income and improve nitrogen fertilizer efficiency. This study used urea alone (Urea) and slow-release nitrogen fertilizer alone (C/SRF) as controls and employed meta-analysis and a random forest model to assess MNF effects on crop yield and nitrogen partial factor productivity (PFPN), and to identify key influencing factors. Results showed that compared with urea, MNF increased crop yield by 7.42% and PFPN by 8.20%, with higher improvement rates in Northwest China, regions with an average annual temperature ≤ 20 °C, and elevations of 750–1050 m; in soils with a pH of 5.5–6.5, where 150–240 kg·ha⁻¹ nitrogen with 25–35% content and an 80–100 day release period was applied, and the blending ratio was ≥0.3; and when planting rapeseed, maize, and cotton for 1–2 years. The top three influencing factors were crop type, nitrogen rate, and soil pH. Compared with C/SRF, MNF increased crop yield by 2.44% and had a non-significant increase in PFPN, with higher improvement rates in Northwest China, regions with an average annual temperature ≤ 5 °C, average annual precipitation ≤ 400 mm, and elevations of 300–900 m; in sandy soils with pH > 7.5, where 150–270 kg·ha⁻¹ nitrogen with 25–30% content and a 40–80 day release period was applied, and the blending ratio was 0.4–0.7; and when planting potatoes and rapeseed for 3 years. The top three influencing factors were nitrogen rate, crop type, and average annual precipitation. In conclusion, MNF should comprehensively consider crops, regions, soil, and management. This study provides a scientific basis for optimizing slow-release nitrogen fertilizers and promoting the large-scale application of MNF in farmland. Full article

As large language models (LLMs) increasingly enter specialized domains, inference without external resources often leads to knowledge gaps, opaque reasoning, and hallucinations. To address these challenges in power equipment defect grading, we propose a zero-shot question-answering framework that requires no task-specific examples. Our system performs two-stage retrieval—first using a Sentence-BERT model fine-tuned on power equipment maintenance texts for coarse filtering, then combining TF-IDF and semantic re-ranking for fine-grained selection of the most relevant knowledge snippets. We embed both the user query and the retrieved evidence into a Chain-of-Thought (CoT) prompt, guiding the pre-trained LLM through multi-step reasoning with self-validation and without any model fine-tuning. Experimental results show that on a held-out test set of 218 inspection records, our method achieves a grading accuracy of 54.2%, which is 6.0 percentage points higher than the fine-tuned BERT baseline at 48.2%; an Explanation Coherence Score (ECS) of 4.2 compared to 3.1 for the baseline; a mean retrieval latency of 28.3 ms; and an average LLM inference time of 5.46 s. Ablation and sensitivity analyses demonstrate that a fine-stage retrieval pool size of k = 30 offers the optimal trade-off between accuracy and latency; human expert evaluation by six senior engineers yields average Usefulness and Trustworthiness scores of 4.1 and 4.3, respectively. Case studies across representative defect scenarios further highlight the system’s robust zero-shot performance. Full article

Oil palm kernel shell is an abundant agricultural waste generated by the palm oil industry. To achieve sustainable use of this waste, oil palm kernel shells were converted into valuable resources as active biocarbons. A two-stage preparation method involving N₂ pyrolysis, followed by CO₂ activation, was used to produce the active biocarbon. The optimum pyrolysis conditions that produced the largest BET surface area of 519.1 m²/g were a temperature of 600 °C, a hold time of 2 h, a nitrogen flow rate of 150 cm³/min, and a heating rate of 10 °C/min. The optimum activation conditions to prepare the active biocarbon with the largest micropore surface area or the best micropore/BET surface area combination were a temperature of 950 °C, a CO₂ flow rate of 300 cm³/min, a heating rate of 10 °C/min, and a hold time of 3 h, yielding BET and micropore surface areas of 1232.3 and 941.0 m²/g, respectively, and consisting of 76.36% of micropores for the experimental optimisation technique adopted here. This study underscores the importance of optimising both the pyrolysis and activation conditions to produce an active biocarbon with a maximum micropore surface area for gaseous adsorption applications, especially to capture CO₂ greenhouse gas, to mitigate global warming and climate change. Such a comprehensive and detailed study on the conversion of oil palm kernel shell into active biocarbon is lacking in the open literature. The research results provide a practical blueprint on the process parameters and technical know-how for the industrial production of highly microporous active biocarbons prepared from oil palm kernel shells. Full article

Furunculosis caused by Aeromonas salmonicida poses a significant challenge to the sustainable production of maraena whitefish (Coregonus maraena). This case report outlines a multi-year disease management strategy at a European whitefish facility with two production departments, each specialising in different life-cycle stages. Recurrent outbreaks of A. salmonicida necessitated the development of effective vaccination protocols. Herd-specific immersion vaccines failed to confer protection, while injectable formulations with plant-based adjuvants caused severe adverse reactions and mortality rates exceeding 30%. In contrast, the bivalent vaccine Alpha Ject 3000, containing inactivated A. salmonicida and Vibrio anguillarum with a mineral oil adjuvant, yielded high tolerability and durable protection in over one million whitefish. Post-vaccination mortality remained low (3.3%), aligning with industry benchmarks, and furunculosis-related losses were fully prevented in both departments. Transcriptomic profiling of immune-relevant tissues revealed distinct gene expression signatures depending on vaccine type and time post-vaccination. Both the herd-specific vaccine and Alpha Ject 3000 induced the expression of immunoglobulin and inflammatory markers in the spleen, contrasted by reduced immunoglobulin transcript levels in the gills and head kidney together with the downregulated expression of B-cell markers. These results demonstrate that an optimised injectable vaccination strategy can significantly improve health outcomes and disease resilience in maraena whitefish aquaculture. Full article

Cassiterite polymetallic sulfide ore exhibits a complex mineral composition and significant variations in mineral properties, which frequently lead to issues such as the over-grinding of cassiterite and under-grinding of sulfide minerals during the grinding process. These issues consequently impair liberation performance in subsequent beneficiation stages. Among these factors, the grinding media ratios stand as one of the critical factors influencing grinding efficiency. Based on these, the paper adopts the single-factor test method to systematically study the influence law of factors such as grinding time, mill rotational rate, and mill filling rate on the particle size composition of ore grinding products and the grinding technology efficiency under different media conditions; in addition, it is compared with the influence law of different conditions of media ratios on the grinding efficiency of ore. The results show that the optimal parameters of the grinding operation are obtained at the grinding time of 4 min, the mill rotational rate of 60%, and the filling rate of 35%. The grinding time and mill filling rate have a relatively more significant effect on the product particle size distribution, while the effect of the mill rotational rate is relatively less significant. When the parameters of grinding operations are optimal, the yield of qualified particle size and grinding technical efficiency are used as the evaluation indices, respectively. Overall, the order of the grinding effect of different media conditions was as follows: steel ball combination of Φ20 mm and Φ25 mm > steel balls of three single sizes > steel ball combination of Φ20 mm and Φ30 mm. The optimal grinding media ratios are Φ20 mm and Φ25 mm (the percentage of the Φ20 mm ball is 90%). The reasonable media ratios will effectively coordinate the optimal grinding effect between different media. The research results can provide the necessary basic data for the subsequent grinding optimization of cassiterite polymetallic sulfide ores. Full article

Synthetic Aperture Radar (SAR) images and optical imagery have high potential for extracting digital elevation models (DEMs). The two main approaches for deriving elevation models from SAR data are interferometry (InSAR) and radargrammetry. Adapted from photogrammetric principles, radargrammetry relies on disparity model estimation as its core component. Matching strategies in radargrammetry typically follow local, global, or semi-global methodologies. Local methods, while having higher accuracy, especially in low-texture SAR images, require larger kernel sizes, leading to quadratic computational complexity. Conversely, global and semi-global models produce more consistent and higher-quality disparity maps but are computationally more intensive than local methods with small kernels and require more memory (RAM). In this study, inspired by the advantages of local matching algorithms, a computationally efficient and novel model is proposed for extracting corresponding pixels in SAR-intensity stereo images. To enhance accuracy, the proposed two-stage algorithm operates without an image pyramid structure. Notably, unlike traditional local and global models, the computational complexity of the proposed approach remains stable as the input size or kernel dimensions increase while memory consumption stays low. Compared to a pyramid-based local normalized cross-correlation (NCC) algorithm and adaptive semi-global matching (SGM) models, the proposed method maintains good accuracy comparable to adaptive SGM while reducing processing time by up to 50% relative to pyramid SGM and achieving a 35-fold speedup over the local NCC algorithm with an optimal kernel size. Validated on a Sentinel-1 stereo pair with a 10 m ground-pixel size, the proposed algorithm yields a DEM with an average accuracy of 34.1 m. Full article