Search Results (2,173)

Excessive nitrogen (N) fertilizer usage in agriculture has prompted the exploration of sustainable strategies to enhance nitrogen use efficiency (NUE) while maintaining crop yield and quality. Processed tomatoes (Solanum lycopersicum L.) were grown for two years (2023 and 2024) following a two-way factorial randomized complete block (RCBD) design, considering three biostimulants and three N regimes as two factors, to assess their morphophysiological, biochemical, anatomical and yield performances. Nitrogen application significantly influenced biomass accumulation, the leaf area index (LAI), nitrogen uptake and yield with notable comparable values between reduced and optimal nitrogen dose, indicating improved nitrogen use efficiency. Biostimulants showed limited effects alone but enhanced plant performance under reduced nitrogen conditions, particularly improving chlorophyll content, crop growth, N uptake, yield and anatomical adaptations. Moreover, compared to 2024, biostimulant application enhanced tomato growth more evidently in 2023 due to environmental variations, likely due to the occurrence of stress conditions. Importantly, biostimulants, together with N regimes, i.e., optimal and reduced doses, showed improved anatomical traits, especially regarding leaf thickness and thickness between the two epidermises, indicating adaptive responses that may support sustained productivity under N-limited conditions. Among the biostimulants used, the processed tomatoes responded better to protein hydrolysate and endophytic N-fixing bacteria than to seaweed extract. These findings suggest that although biostimulants alone were not affected, integrating them with reduced N fertilization provides a viable strategy for optimizing tomato production, conserving resources and minimizing the environmental impact without compromising yield or quality. Full article

To investigate the spatiotemporal evolution characteristics and primary driving factors of Gross Primary Productivity (GPP) on the Qinghai-Tibet Plateau, we employed an enhanced MODIS-PSN model. Utilizing the fifth-generation global climate reanalysis dataset (ECMWF ERA5), we generated GPP remote sensing products by integrating six natural factors. Through correlation analysis and geographical detector modeling, we quantitatively analyzed the spatiotemporal dynamics and key drivers of vegetation GPP across the Qinghai-Tibet Plateau from 2001 to 2022. The results demonstrate that GPP changes across the Qinghai-Tibet Plateau display pronounced spatial heterogeneity. The humid northeastern and southeastern regions exhibit significantly positive change rates, primarily distributed across wetland and forest ecosystems, with a maximum mean annual change rate of 12.40 gC/m²/year. In contrast, the central and southern regions display a decreasing trend, with the minimum change rate reaching −1.61 gC/m²/year, predominantly concentrated in alpine grasslands and desert areas. Vegetation GPP on the Qinghai-Tibet Plateau shows significant correlations with temperature, vapor pressure deficit (VPD), evapotranspiration (ET), leaf area index (LAI), precipitation, and radiation. Among the factors analyzed, LAI demonstrates the strongest explanatory power for spatial variations in vegetation GPP across the Qinghai-Tibet Plateau. The dominant factors influencing vegetation GPP on the Qinghai-Tibet Plateau are LAI, ET, and precipitation. The pairwise interactions between these factors exhibit linear enhancement effects, demonstrating synergistic multifactor interactions. This study systematically analyzed the response mechanisms and variations of vegetation GPP to multiple driving factors across the Qinghai-Tibet Plateau from a spatial heterogeneity perspective. The findings provide both a critical theoretical framework and practical insights for better understanding ecosystem response dynamics and drought conditions on the plateau. Full article

The rhizosphere microbiome plays an important role in carbon- and nitrogen-cycling in soil and in the stress response of plants. It also affects the function of the ammonium transporter (AmtB) that senses nitrogen levels inside and outside the cells of the associative nitrogen-fixing bacterium GXGL-4A. However, the potential mechanism of the interaction between the AmtB deletion mutant of GXGL-4A (∆amtB) and microorganisms in the rhizosphere of plants under low-nitrogen stress is still unclear. As revealed by transcriptome analyses, mutation of the amtB gene in GXGL-4A resulted in a significant up-regulation of many functional genes associated with nitrogen fixation and transportation at transcription level. The application of ∆amtB changed the nitrogen level in the rhizosphere of cucumber seedlings and reshaped the microbial community structure in the rhizosphere, enriching the relative abundance of Actinobacteriota and Gemmatimonadota. Based on bacterial functional prediction analyses, the metabolic capacities of rhizobacteria were improved after inoculation of cucumber seedlings with the original strain GXGL-4A or the ∆amtB mutant, resulting in the enhancement of amino acids, lipids, and carbohydrates in the cucumber rhizosphere, which promoted the growth of cucumber plants under a low-nitrogen stress condition. The results contribute to understanding the biological function of gene amtB, revealing the regulatory role of the strain GXGL-4A on cucumber rhizosphere nitrogen metabolism and laying a theoretical foundation for the development of efficient nitrogen-fixing bacterial agents for sustainable agricultural production. Full article

The cessation of traditional mountain grazing has emerged as a principal driver of habitat degradation and the local extinction of Parnassius apollo (Linnaeus, 1758) in Serbia. While previous studies have cited multiple contributing factors, our research provides evidence that the abandonment of extensive livestock grazing has triggered vegetation succession, the disappearance of the larval host plant (Sedum album), and a reduction in microhabitat heterogeneity—conditions essential for the persistence of this stenophagous butterfly species. Through satellite-based analysis of vegetation dynamics (2015–2024), we identified clear structural differences between habitats that currently support populations and those where the species is no longer present. Occupied sites were characterized by low levels of exposed soil, moderate grass coverage, and consistently high shrub and tree density, whereas unoccupied sites exhibited dense encroachment of grasses and woody vegetation, leading to structural instability. Furthermore, MODIS-derived indices (2010–2024) revealed a consistent decline in vegetation productivity (GPP, FPAR, LAI) in succession-affected areas, alongside significant correlations between elevated land surface temperatures (LST), thermal stress (TCI), and reduced photosynthetic capacity. A wildfire event on Mount Stol in 2024 further exacerbated habitat degradation, as confirmed by remote sensing indices (BAI, NBR, NBR2), which documented extensive burn scars and post-fire vegetation loss. Collectively, these findings indicate that the decline of P. apollo is driven not only by ecological succession and climatic stressors, but also by the abandonment of land-use practices that historically maintained suitable habitat conditions. Our results underscore the necessity of restoring traditional grazing regimes and integrating ecological, climatic, and landscape management approaches to prevent further biodiversity loss in montane environments. Full article

Over the years, macromycete fungi have been used as a source of food, part of religious rites and rituals, and as a medicinal remedy. Species with strong health-promoting potential include Hericium erinaceus, Cordyceps militaris, Ganoderma lucidum, Pleurotus ostreatus, Flammulina velutipes, and Inonotus obliquus. These species contain many bioactive compounds, including β-glucans, endo- and exogenous amino acids, polyphenols, terpenoids, sterols, B vitamins, minerals, and lovastatin. The level of some biologically active substances is species-specific, e.g., hericenones and erinacines, which have neuroprotective properties, and supporting the production of nerve growth factor in the brain for Hericium erinaceus. Due to their high health-promoting potential, mushrooms and substances isolated from them have found applications in livestock nutrition, improving their welfare and productivity. This phenomenon may be of particular importance in the nutrition of laying hens and broiler chickens, where an increase in pathogen resistance to antibiotics has been observed in recent years. Gallus gallus domesticus is a key farm animal for meat and egg production, so the search for new compounds to support bird health is important for food safety. Studies conducted to date indicate that feed supplementation with mushrooms has a beneficial effect on, among other things, bird weight gain; bone mineralisation; and meat and egg quality, including the lipid profile and protein content and shell thickness, and promotes the development of beneficial microbiota, thereby increasing immunity. 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

Ceramic pastes used in additive manufacturing offer several advantages, including low production costs due to the availability of raw materials and efficient processing methods, as well as a reduced environmental footprint through minimized material waste, optimized resource use, and the inclusion of recyclable or sustainably sourced components. This study evaluates the effect of diatomite content in a ceramic paste composed of carboxymethyl cellulose, kaolinite, and feldspar on its extrusion behavior and thermal conductivity, with additional analysis of its implications for microstructure, mechanical properties, and thermal performance. Four ceramic pastes were prepared with diatomite additions of 0, 10, 30, and 60% by weight. Thermal conductivity, extrusion behavior, morphology, and distribution were examined using scanning electron microscopy (SEM), while thermal degradation was assessed through thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The results show that increasing diatomite content leads to a reduction in thermal conductivity, which ranged from 0.719 W/(m·°C) for the control sample to 0.515 W/(m·°C) for the 60% diatomite sample, as well as an improvement in extrusion behavior. The ceramic paste demonstrated adequate extrusion performance for 3D printing at diatomite contents above 30%. These findings lay the groundwork for future research and optimization in the development of functional ceramic pastes for advanced manufacturing applications. Full article

Recently, deep learning algorithms have been increasingly applied in construction for activity recognition, particularly for excavators, to automate processes and enhance safety and productivity through continuous monitoring of earthmoving activities. These deep learning algorithms analyze construction videos to classify excavator activities for earthmoving purposes. However, previous studies have solely focused on single-source external videos, which limits the activity recognition capabilities of the deep learning algorithm. This paper introduces a novel multi-modal deep learning-based methodology for recognizing excavator activities, utilizing multi-stream input data. It processes point clouds and RGB images using the two-stream long short-term memory convolutional neural network (CNN-LSTM) method to extract spatiotemporal features, enabling the recognition of excavator activities. A comprehensive dataset comprising 495,000 video frames of synchronized RGB and point cloud data was collected across multiple construction sites under varying conditions. The dataset encompasses five key excavator activities: Approach, Digging, Dumping, Idle, and Leveling. To assess the effectiveness of the proposed method, the performance of the two-stream CNN-LSTM architecture is compared with that of single-stream CNN-LSTM models on the same RGB and point cloud datasets, separately. The results demonstrate that the proposed multi-stream approach achieved an accuracy of 94.67%, outperforming existing state-of-the-art single-stream models, which achieved 90.67% accuracy for the RGB-based model and 92.00% for the point cloud-based model. These findings underscore the potential of the proposed activity recognition method, making it highly effective for automatic real-time monitoring of excavator activities, thereby laying the groundwork for future integration into digital twin systems for proactive maintenance and intelligent equipment management. Full article

This study evaluated the production performance and egg quality of Lohmann Brown laying hens fed diets containing different levels of Corn Bran with Solubles (CBS). A total of 144 hens aged 44 weeks were assigned to three treatments in a completely randomized design, with eight replicates per treatment and six birds per replicate. The experimental treatments included diets with CBS inclusion levels of 0%, 5%, and 10%. The experiment lasted 84 days (44 to 55 weeks of age). Data were analyzed via one-way ANOVA, with mean differences evaluated using Tukey’s HSD test (α = 0.05). No significant effects were observed for laying rate, feed intake, feed conversion ratio, or egg mass (p > 0.05). However, egg quality parameters such as shell percentage, shell weight per unit surface area (SWUSA), and yolk color were influenced by the treatments (p < 0.05). Hens fed diets with 5% CBS exhibited higher shell percentage and SWUSA compared to those on the 0% CBS diet. Yolk color intensity increased with higher CBS inclusion levels. In conclusion, incorporating up to 10% CBS in corn–soybean meal diets for laying hens can enhance egg yolk pigmentation. Notably, including 5% CBS improves eggshell quality. Full article

While personal thermal comfort is critical for well-being and productivity, it is often overlooked by traditional building management systems that rely on uniform settings. Modern data-driven approaches often fail to capture the complex interactions between various data streams. This pilot study introduces a high-accuracy, interpretable framework for thermal comfort classification, designed to identify the most significant predictors from a comprehensive suite of environmental, physiological, and anthropometric data in a controlled group of young adults. Initially, an XGBoost model using the full 24-feature dataset achieved the best performance at 91% accuracy. However, after using SHAP analysis to identify and select the most influential features, the performance of our ensemble models improved significantly; notably, a Random Forest model’s accuracy rose from 90% to 94%. Our analysis confirmed that for this homogeneous cohort, environmental parameters—specifically temperature, humidity, and CO₂—were the dominant predictors of thermal comfort. The primary strength of this methodology lies in its ability to create a transparent pipeline that objectively identifies the most critical comfort drivers for a given population, forming a crucial evidence base for model design. The analysis also revealed that the predictive value of heart rate variability (HRV) diminished when richer physiological data, such as diastolic blood pressure, were included. For final validation, the optimized Random Forest model, using only the top 10 features, was tested on a hold-out set of 100 samples, achieving a final accuracy of 95% and an F1-score of 0.939, with all misclassifications occurring only between adjacent comfort levels. These findings establish a validated methodology for creating effective, context-aware comfort models that can be embedded into intelligent building management systems. Such adaptive systems enable a shift from static climate control to dynamic, user-centric environments, laying the critical groundwork for future personalized systems while enhancing occupant well-being and offering significant energy savings. Full article

In drought-prone regions without irrigation systems, effective agrotechnologies such as subsoiling are crucial for enhancing soil infiltration and water retention. However, the effects of subsoiling can vary depending on crop type and environmental conditions. Despite previous research, there is limited understanding of the contrasting responses of C3 (sunflower) and C4 (maize) crops to subsoiling under drought stress. This study addresses this knowledge gap by assessing the effectiveness of subsoiling as a drought mitigation practice on Haplic Chernozem in Northern Bulgaria, integrating ground-based and remote sensing data. Soil physical parameters, leaf area index (LAI), canopy temperature, crop water stress index (CWSI), soil moisture, and yield were evaluated under both conventional tillage and subsoiling for the two crops. A variety of optical and radar descriptive remote sensing products derived from Sentinel-1 and Sentinel-2 satellite data were calculated for different crop types. Consequently, the use of machine learning, utilizing all the processed remote sensing products, enabled the reasonable prediction of LAI, achieving a coefficient of determination (R²) after a cross-validation greater than 0.42 and demonstrating good agreement with in situ observations. Results revealed differing responses: subsoiling had a positive effect on sunflower, improving LAI, water status, and slightly increasing yield, while it had no positive effect on maize. These findings highlight the importance of crop-specific responses in evaluating subsoiling practices and demonstrate the added value of integrating unmanned aerial systems (UAS) and satellite-based remote sensing data into agricultural drought monitoring. Full article

This research presents DuSAFNet, a lightweight deep neural network for fine-grained bird audio classification. DuSAFNet combines dual-path feature fusion, spectral–temporal attention, and a multi-band ArcMarginProduct classifier to enhance inter-class separability and capture both local and global spectro–temporal cues. Unlike single-feature approaches, DuSAFNet captures both local spectral textures and long-range temporal dependencies in Mel-spectrogram inputs and explicitly enhances inter-class separability across low, mid, and high frequency bands. On a curated dataset of 17,653 three-second recordings spanning 18 species, DuSAFNet achieves 96.88% accuracy and a 96.83% F1 score using only 6.77 M parameters and 2.275 GFLOPs. Cross-dataset evaluation on Birdsdata yields 93.74% accuracy, demonstrating robust generalization to new recording conditions. Its lightweight design and high performance make DuSAFNet well-suited for edge-device deployment and real-time alerts for rare or threatened species. This work lays the foundation for scalable, automated acoustic monitoring to inform biodiversity assessments and conservation planning. Full article

This study aimed to investigate the effects of baicalin and citric acid on egg production performance, egg quality, and the intestinal morphology and function of laying hens. A total of 600 Hy-Line Brown laying hens, 59 weeks old, were randomly allocated to four dietary treatments, with 10 replicates per treatment and 15 hens per replicate. The control group was fed a basal diet, while the other three groups were fed the basal diet supplemented with 150 mg/kg baicalin (B), 2000 mg/kg citric acid (CA), or 150 mg/kg baicalin plus 2000 mg/kg citric acid (B + CA), respectively. The experimental period lasted for 12 weeks, and the results indicated that neither the individual addition nor the combined application of baicalin and citric acid had a significant impact on the laying performance. However, compared with the control group, the baicalin and/or citric acid supplementation significantly increased the eggshell strength and Haugh unit. Additionally, the combination of baicalin and citric acid significantly increased the villus height and the villus height/crypt depth ratio in the duodenum and jejunum. It also enhanced the population of beneficial bacteria, such as Lactobacillus and Bifidobacterium, in the cecum and improved the activity of intestinal digestive enzymes, primarily disaccharidases. Furthermore, the addition of baicalin to the diet significantly increased the content of Secretory Immunoglobulin A in the ileum and jejunum after 12 weeks of feeding. These results suggest that the combination of baicalin and citric acid had a synergistic effect on the improvement of egg quality and intestinal morphology and function in laying hens. Overall, our findings provide important insights into the potential benefits of supplementing baicalin and citric acid in the diet of laying hens and may have practical implications for improving egg quality and poultry health status. Full article

Burkholderia gladioli is a multifaceted bacterium with both pathogenic and beneficial strains, and nonpathogenic Burkholderia species have shown potential as plant growth-promoting rhizobacteria (PGPRs) and biocontrol agents. However, the molecular mechanisms underlying their beneficial functions remain poorly characterized. This study systematically investigated the antimicrobial mechanisms and plant growth-promoting properties of B. gladioli strain ZBSF BH07, isolated from the grape rhizosphere, by combining genomic and functional analyses, including whole-genome sequencing, gene annotation, phylogenetic and comparative genomics, in vitro antifungal assays, and plant growth promotion evaluations. The results showed that ZBSF BH07 exhibited broad-spectrum antifungal activity, inhibiting 14 grape pathogens with an average inhibition rate of 56.58% and showing dual preventive/curative effects against grape white rot, while also significantly promoting grape seedling growth with increases of 54.9% in plant height, 172.9% in root fresh weight, and 231.34% in root dry weight. Genomic analysis revealed an 8.56-Mb genome (two chromosomes and one plasmid) encoding 7431 genes and 26 secondary metabolite biosynthesis clusters (predominantly nonribosomal peptide synthetases), supporting its capacity for antifungal metabolite secretion, and functional analysis confirmed genes for indole-3-acetic acid (IAA) synthesis, phosphate solubilization, and siderophore production. These results demonstrate that ZBSF BH07 suppresses pathogens via antifungal metabolites and enhances grape growth through phytohormone regulation and nutrient acquisition, providing novel insights into the dual mechanisms of B. gladioli as a biocontrol and growth-promoting agent and laying a scientific foundation for developing sustainable grapevine disease management strategies. Full article

With the continuous exploitation of bauxite mineral resources, Guangxi bauxite faces many difficulties in alumina production due to its characteristics of high silicon content, high iron content, and a low Al-Si ratio. In view of this, this study is closely related to the key link of bauxite pre-desiliconization and strives to break free from the status quo to improve the aluminum/silicon ratio and help optimize the subsequent alumina-refining process. In the work presented in this paper, the unique mineralogy of Guangxi bauxite was comprehensively considered, covering its complex mineral composition and fine distribution characteristics. The barium hydroxide pre-desilication technology was first used for in-depth experimental exploration, and the silicon removal efficiency under different working conditions was systematically compared. The system compared the silicon removal effect and the associated aluminum loss under different working conditions. The results of this study will lay a solid foundation for the rational and efficient development of bauxite in Guangxi, which is expected to reduce the cost of alumina production, improve the economic benefits for the Guangxi aluminum industry, simultaneously strengthen the efficiency of resource recycling, accelerate the sustainable development of the industry, and provide a useful reference example for subsequent similar studies. Full article