Search Results (60,570)

The Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) mission provides valuable three-dimensional precipitation structure data on a global scale and has been widely used in hydrometeorological research. However, due to its spatial resolution limitations and inherent algorithmic assumptions, the accuracy of GPM precipitation estimates can exhibit systematic biases, especially under complex terrain conditions or in the presence of variable precipitation structures, such as light stratiform rain or intense convective storms. In this study, we evaluated the near-surface precipitation rate estimates from the GPM-DPR Level 2A product using over 1440 min of disdrometer observations collected across China from 2021 to 2023. Based on three years of stable stratiform precipitation data from the Jinghe station, we developed a least squares linear correction model for radar reflectivity. Independent validation using national disdrometer data from 2023 demonstrated that the corrected reflectivity significantly improved rainfall estimates under light precipitation conditions, although improvements were limited for convective events or in complex terrain. To further enhance retrieval accuracy, we introduced a regionally adaptive R–Z relationship scheme stratified by precipitation type and terrain category. Applying these localized relationships to the corrected reflectivity yielded more consistent rainfall estimates across diverse conditions, highlighting the importance of incorporating regional microphysical characteristics into satellite retrieval algorithms. The results indicate that the accuracy of GPM precipitation retrievals is more significantly influenced by precipitation type than by terrain complexity. Under stratiform precipitation conditions, the GPM-estimated precipitation data demonstrate the highest reliability. The correction framework proposed in this study is grounded on ground-based observations and integrates regional precipitation types with terrain characteristics. It effectively enhances the applicability of GPM-DPR products across diverse environmental conditions in China and offers a methodological reference for correcting satellite precipitation biases in other regions. Full article

Monitoring water quality parameters in coastal and estuarine environments is critical for assessing their ecological status and addressing environmental challenges. However, traditional in situ sampling programs are often constrained by limited spatial and temporal coverage, making it difficult to capture the complex variability in these dynamic systems. This study introduces a novel upscaling framework that leverages limited in situ measurements and airborne hyperspectral data to generate multiple conditional realizations of water quality parameter fields. These pseudo-measurements are statistically consistent with the original data and are used to calibrate inversion algorithms that relate satellite-derived reflectance data to water quality parameters. The approach was applied to Kaštela Bay, a semi-enclosed coastal area in the eastern Adriatic Sea, to map seasonal variations in water quality parameters such as Chlorophyll-a. The upscaling framework captured spatial patterns that were absent in sparse in situ observations and enabled regional mapping using Sentinel-2A satellite data at the appropriate spatial scale. By generating realistic pseudo-measurements, the method improved the stability and performance of satellite-based retrieval algorithms, particularly in periods of high productivity. Overall, this methodology addresses data scarcity challenges in coastal water monitoring and its application could benefit the implementation of European water quality directives through enhanced regional-scale mapping capabilities. Full article

In arid regions with limited water resources, improving cropland water-use efficiency (WUEc) is crucial for maintaining crop production. This study aims to investigate how changes in meteorological and vegetation factors affect WUEc in drylands and to identify its primary drivers, which are essential for understanding how cropland ecosystems respond to complex environmental changes. Using remote sensing data, we analyzed the spatiotemporal patterns of WUEc in Xinjiang from 2002 to 2022 by applying STL decomposition, Sen’s slope combined with the Mann–Kendall test, and an XGBoost–SHAP model, quantifying its key controlling factors. The results indicate that from 2002 to 2022, WUEc in Xinjiang showed an overall declining trend. Prior to 2007, WUEc increased at 0.05 gC·m⁻¹·m⁻²·a⁻¹, after which it fluctuated downward at −0.01 gC·m⁻¹·m⁻²·a⁻¹. Intra-annual peaks consistently occurred in May and during September–October. Spatially, WUEc exhibited significant heterogeneity, increasing from south to north, with 53.26% of the region showing declines. Temperature (T) and leaf area index (LAI) emerged as the primary meteorological and vegetation drivers, respectively, influencing WUEc change in 45.7% and 17.6% of the area. Both variables were negatively correlated with WUEc, with negative correlations covering 60% of the region for T and 83% for LAI. These findings provide scientific guidance for optimizing crop structure and water-resource management strategies in arid regions. Full article

Among the various renewable-powered pathways for green hydrogen production, solar photovoltaic (PV) technology represents a particularly promising option due to its environmental sustainability, widespread availability, and declining costs. However, the inherent intermittency of solar irradiance presents operational challenges for electrolyzers, particularly in terms of stability and efficiency. This study presents a MATLAB-based dynamic model of an off-grid, DC-coupled solar PV-Anion Exchange Membrane (AEM) electrolyzer system, with a specific focus on realistically estimating hydrogen output. The model incorporates thermal energy management strategies, including electrolyte pre-heating during startup, and accounts for performance degradation due to load cycling. The model is designed for a comprehensive analysis of hydrogen production by employing a 10-year time series of irradiance and ambient temperature profiles as inputs. The results are compared with two simplified scenarios: one that does not consider the equipment response time to variable supply and another that assumes a fixed start temperature to evaluate their impact on productivity. Furthermore, to limit the effects of degradation, the algorithm has been modified to allow the non-sequential activation of the stacks, resulting in an improvement of the single stack efficiency over the lifetime and a slight increase in overall hydrogen production. Full article

Planting patterns and irrigation amounts are key factors affecting maize yield. This study adopted a two-factor experimental design, with planting pattern as the main plot and irrigation amount as the subplot, to investigate the effects of irrigation levels under different planting patterns (including uniform row spacing and alternating wide-narrow row spacing) on spring maize yield and water use efficiency in Xinjiang. Through this approach, the study examined the mechanisms by which planting pattern and irrigation amount influence maize growth, yield formation, and water use efficiency. Experiments conducted at the Agricultural Science Research Institute of the Ninth Division of Xinjiang Production and Construction Corps demonstrated that alternating wide-narrow row spacing combined with moderate irrigation (5400 m³/hm²) significantly optimized maize root distribution, improved water use efficiency, and increased leaf area index and net photosynthetic rate, thereby promoting dry matter accumulation and yield enhancement. In contrast, uniform row spacing under high irrigation levels increased yield but resulted in lower water use efficiency. The study also found that alternating wide-narrow row spacing enhanced maize nutrient absorption from the soil, particularly phosphorus utilization efficiency, by improving canopy structure and root expansion. This pattern exhibited comprehensive advantages in resource utilization, providing a theoretical basis and technical pathway for achieving water-saving and high-yield maize production in arid regions, which holds significant importance for promoting sustainable agricultural development. Full article

This study investigates the potential of residual clays from a traditional ceramic-producing region in southern Portugal as raw materials for red ceramic applications. This work aims to support more sustainable ceramic practices through the local valorization of naturally available, underutilized clay resources. A multidisciplinary approach was employed to characterize clays, integrating mineralogical (XRD), chemical (XRF), granulometric, and thermal analyses (TGA/DTA/TD), as well as technological tests on plasticity, extrusion moisture, shrinkage, and flexural strength. These assessments were designed to capture both the intrinsic properties of the clays and their behavior across key ceramic processing stages, such as shaping, drying, and firing. The results revealed a broad diversity in mineral composition, particularly in the proportions of kaolinite, smectite, and illite, which strongly influenced plasticity, water demand, and thermal stability. Clays with higher fine fractions and smectitic content exhibited excellent plasticity and workability, though with increased sensitivity to drying and firing conditions. Others, with coarser textures and illitic or feldspathic composition, demonstrated improved dimensional stability and lower shrinkage. Thermal analyses confirmed expected dehydroxylation and sintering behavior, with the formation of mullite and spinel-type phases contributing to densification and strength in fired bodies. This study highlights that residual clays from varied geological settings can offer distinct advantages when matched appropriately to ceramic product requirements. Some materials showed strong potential for direct application in structural ceramics, while others may serve as additives or tempering agents in formulations. These findings reinforce the value of integrated characterization for optimizing raw material use and support a more circular, resource-conscious approach to ceramic production. Full article

Eucalyptus has become a major plantation crop in southern China, with a carbon sequestration capacity significantly higher than that of other species. However, its long-term carbon sequestration capacity and regional-scale potential remain highly uncertain due to commonly applied short-rotation management practices. The InTEC (Integrated Terrestrial Ecosystem Carbon) model is a process-based biogeochemical model that simulates carbon dynamics in terrestrial ecosystems by integrating physiological processes, environmental drivers, and management practices. In this study, the InTEC model was enhanced with an optimized eucalyptus module (InTEC_euc) and a data assimilation module (InTEC_DA), and driven by multiple remote sensing products (Net Primary Productivity (NPP) and carbon density) to simulate the carbon budgets of eucalyptus plantations from 2003 to 2023. The results indicated notable improvements in the performance of the InTEC_euc model when driven by different datasets: carbon density simulation showed improvements in R² (0.07–0.56), reductions in MAE (5.99–28.51 Mg C ha⁻¹), reductions in RMSE (8.1–31.85 Mg C ha⁻¹), and improvements in rRMSE (12.37–51.82%), excluding NPP_Lin. The carbon density-driven InTEC_euc model outperformed the NPP-driven model, with improvements in R² (0.28), MAE (−8.15 Mg C ha⁻¹), RMSE (−9.43 Mg C ha⁻¹), and rRMSE (−15.34%). When the InTEC_DA model was employed, R² values for carbon density improved by 0–0.03 (excluding ACD_Yan), with MAE reductions between 0.17 and 7.22 Mg C ha⁻¹, RMSE reductions between 0.33 and 12.94 Mg C ha⁻¹ and rRMSE improvements ranging from 0.51 to 20.22%. The carbon density-driven InTEC_DA model enabled the production of high-resolution and accurate carbon budget estimates for eucalyptus plantations from 2003 to 2023, with average NPP, Net Ecosystem Productivity (NEP), and Net Biome Productivity (NBP) values of 17.80, 10.09, and 9.32 Mg C ha⁻¹ yr⁻¹, respectively, offering scientific insights and technical support for the management of eucalyptus plantations in alignment with carbon neutrality targets. Full article

Detecting steel defects is a vital process in industrial production, but traditional methods suffer from poor feature extraction and low detection accuracy. To address these issues, this research introduces an improved model, EB-YOLOv8, based on YOLOv8. First, the multi-scale attention mechanism EMA is integrated into the backbone and neck sections to reduce noise during gradient descent and enhance model stability by encoding global information and weighting model parameters. Second, the weighted fusion splicing module, Concat_BiFPN, is used in the neck network to facilitate multi-scale feature detection and fusion. This improves detection precision. The results show that the EB-YOLOv8 model increases detection accuracy on the NEU-DET dataset by 3.1%, reaching 80.2%, compared to YOLOv8. Additionally, the average precision on the Severstal steel defect dataset improves from 65.4% to 66.1%. Overall, the proposed model demonstrates superior recognition performance. Full article

Infrastructure as code (IaC) is essential for modern cloud development, enabling teams to define, deploy, and manage infrastructure in a consistent and repeatable manner. As organizations migrate to Azure, selecting the right approach is crucial for managing complexity, minimizing errors, and supporting DevOps practices. This paper examines the use of Azure Bicep with GitHub Actions to automate infrastructure deployment for an application in the Azure cloud. It explains how Bicep improves readability, modularity, and integration compared to traditional ARM templates and other automation tools. The solution utilizes a modular Bicep design to deploy resources, including virtual networks, managed identities, container apps, databases, and AI services, with environment-specific parameters for development, QA, and production. GitHub Actions workflows automate the building, deployment, and tearing down of infrastructure, ensuring consistent deployments across environments. Security considerations include managed identities, private networking, and secret management in continuous integration (CI) and continuous delivery (CD) pipelines. This paper provides a detailed architectural overview, workflow analysis, and implementation guidance to help teams adopt a robust, automated approach to Azure infrastructure deployment. By leveraging automation tooling and modern DevOps practices, organizations can streamline delivery and maintain secure, maintainable cloud environments. Full article

In the current era, Indian agriculture faces a significant demand for increased food production, which has led to the integration of advanced technologies to enhance efficiency and productivity. Drones have emerged as transformative tools for enhancing precision agriculture, reducing costs, and improving sustainability. This study provides a comprehensive review of drone adoption in Indian agriculture by examining its effects on precision farming, crop monitoring, and pesticide application. This research evaluates technological advancements, regulatory frameworks, infrastructure, farmers’ perceptions, and the financial accessibility of drone technology in the Indian agricultural context. Key findings indicate that, while drone adoption enhances efficiency and sustainability, challenges such as high costs, lack of training, and regulatory barriers hinder widespread implementation. This paper also explores the growing market for agricultural drones in India, highlighting key industry players and projected market growth. Furthermore, it addresses regional differences in adoption rates and emphasizes the increasing social acceptance of drones among Indian farmers. To bridge the gap between potential and practice, the study proposes several policy and institutional recommendations, including government-led financial incentives, training programs, and public–private partnerships to facilitate drone integration. Moreover, this review article also highlights technological advancements, such as AI and IoT, in agriculture. Finally, open issues and future research directions for drones are discussed. Full article

To develop sustainable strategies for mitigating ruminal methanogenesis and improving nitrogen efficiency in dairy systems, this study investigated how low-dose tannic acid (T), tea polyphenols (TP), and their combination (T+TP; 50:50) modulate rumen microbiota and function. A sample of Holstein cows were given four dietary treatments: (1) control (basal diet); (2) T (basal diet + 0.4% DM tannic acid); (3) TP (basal diet + 0.4% DM tea polyphenols); and (4) T+TP (basal diet + 0.2% DM tannic acid + 0.2% DM tea polyphenols). We comprehensively analyzed rumen fermentation, methane production, nutrient digestibility, milk parameters, and microbiota dynamics. Compared with the control group, all diets supplemented with additives significantly reduced enteric methane production (13.68% for T, 11.40% for TP, and 10.89% for T+TP) and significantly increased milk protein yield. The crude protein digestibility significantly increased in the T group versus control. The results did not impair rumen health or fiber digestion. Critically, microbiota analysis revealed treatment-specific modulation: the T group showed decreased Ruminococcus flavefaciens abundance, while all tannin treatments reduced abundances of Ruminococcus albus and total methanogens. These microbial shifts corresponded with functional outcomes—most notably, the T+TP synergy drove the largest reductions in rumen ammonia-N (34.5%) and milk urea nitrogen (21.1%). Supplementation at 0.4% DM, particularly the T+TP combination, effectively enhances nitrogen efficiency and milk protein synthesis while reducing methane emissions through targeted modulation of key rumen microbiota populations, suggesting potential sustainability benefits linked to altered rumen fermentation. Full article

Large Language Models (LLMs) are reshaping recommendation systems through enhanced language understanding, reasoning, and integration with structured data. This systematic review analyzes 88 studies published between 2023 and 2025, categorized into three thematic areas: data processing, technical identification, and LLM-based recommendation architectures. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review highlights key trends such as the use of knowledge graphs, Retrieval-Augmented Generation (RAG), domain-specific fine-tuning, and robustness improvements. Findings reveal that while LLMs significantly advance semantic reasoning and personalization, challenges remain in hallucination mitigation, fairness, and domain adaptation. Technical innovations, including graph-augmented retrieval methods and human-in-the-loop validation, show promise in addressing these limitations. The review also considers the broader macroeconomic implications associated with the deployment of LLM-based systems, particularly as they relate to scalability, labor dynamics, and resource-intensive implementation in real-world recommendation contexts, emphasizing both productivity gains and potential labor market shifts. This work provides a structured overview of current methods and outlines future directions for developing reliable and efficient LLM-based recommendation systems. Full article

Arsenic (As) contamination has significantly increased in recent decades due to anthropogenic activities. This is a serious challenge for human health, environmental quality, and crop productivity. Biochar (BC) is an important practice used globally to remediate polluted soils. Likewise, melatonin (MT) has also shown tremendous results in mitigating metal toxicity and improving crop productivity. Nevertheless, the mechanism of combined BC and MT in alleviating As toxicity in rice (Oryza sativa L.) remains unexplored. In this study, we investigated how As affected rice and how the combined BC and MT facilitated As tolerance. The study comprised a control, As stress (100 mg kg⁻¹), As stress (100 mg kg⁻¹) + BC (2%), As stress (100 mg kg⁻¹) + MT (100 µM) and As stress (100 mg kg⁻¹) + BC (2%) + MT (100 µM). Arsenic significantly decreased rice growth and yield by increasing electrolyte leakage (EL), malondialdehyde (MDA), and hydrogen peroxide (H₂O₂). Co-applying BC and MT substantially enhanced rice growth and yield by increasing chlorophyll synthesis (48.12–92.42%) leaf water contents (40%), antioxidant activities (ascorbate peroxide: 56.43%, catalase: 55.14%, peroxidase: 57.77% and superoxide dismutase: 57.52%), proline synthesis (41.35%), MT synthesis (91.53%), and phytochelatins synthesis (125%) nutrient accumulation in rice seedlings and soil nutrient availability. The increased rice yield with BC + MT was also linked with reduced H₂O₂ production, As accumulation, soil As availability, and an increase in OsAPx6, OsCAT, OsPOD, OsSOD OsASMT1, and OsASMT2 and a decrease in expression of OsABCC1. Biochar + MT enhanced residual OM- and Fe, ((Fe₂As) and Mn (Mn₃(AsO₄)₂) bound forms of As leading to a substantial increase in rice growth and yield. Thus, the combination of BC and MT is an eco-friendly approach to mitigate As toxicity and improve rice productivity. Full article

Waste activated sludge (WAS), a byproduct of livestock wastewater treatment, poses significant disposal challenges due to its low biodegradability and potential environmental impact. Anaerobic digestion (AD) offers a sustainable approach for methane recovery and sludge stabilization. This study evaluates the biomethane potential (BMP) of WAS and its co-digestion with swine slurry (SS), water lily (Nymphaea spp.), and lotus (Nelumbo nucifera) shoot biomass to enhance methane yield. Batch BMP assays were conducted at substrate-to-inoculum (S/I) ratios of 1.0 and 0.5, with methane production kinetics analyzed using the modified Gompertz model. Mono-digestion of WAS yielded 259.35–460.88 NmL CH₄/g VS_added, while co-digestion with SS, water lily, and lotus increased yields by 14.89%, 10.97%, and 16.89%, respectively, surpassing 500 NmL CH₄/g VS_added. All co-digestion combinations exhibited synergistic effects (α > 1), enhancing methane production beyond individual substrate contributions. Lower S/I ratios improved methane yields and biodegradability, highlighting the role of inoculum availability. Co-digestion reduced the lag phase limitations of WAS and plant biomass, improving process efficiency. These findings demonstrate that co-digesting WAS with nutrient-rich co-substrates optimizes biogas production, supporting sustainable sludge management and renewable energy recovery in livestock wastewater treatment systems. Full article

Hydrophobins are small, surface-active protein biosurfactants secreted by filamentous fungi with potential applications in industries such as pharmaceuticals, sanitation, and biomaterials. Additionally, hydrophobins are known to stabilize enzymatic processing of biomass for improved catalytic efficiency. In this study, Pichia pastoris was used to recombinantly express hydrophobin HFBI from Trichoderma reesei, a well-characterized fungal system used industrially for bioethanol production. Iterative optimization was performed on both the induction and purification of HFBI, ultimately producing yields of 86.6 mg/L HFBI and elution concentrations of 48 μM HFBI determined pure by SDS-PAGE, over a five-day methanol-fed batch shake flask induction regiment followed by a single unit operation multimodal cation exchange purification. This final purified material represents an improvement over prior approaches to enable a wider range of potential applications for biosurfactants. Full article