Search Results (26,384)

This article uses a water model with a ratio of 1:5.75 to study the influence of factors such as the position and flow rate of top and bottom composite argon blowing on the mixing time of molten steel in a 300t ladle at a certain factory. Using engine oil to simulate steel slag, the mass transfer velocity of molten steel under different bottom and top blowing positions and flow rates of the ladle was compared. At the same time, numerical simulation was used to analyze the changes in the flow field of molten steel under different ladle blowing modes. The optimal ladle composite bottom argon process was proposed and industrial experiments were conducted on site. The research results show that the stirring effect of top–bottom composite argon blowing in the ladle is significantly better than that of the pure bottom blowing mode. When the top blowing gun is located 300 mm at the bottom of the ladle, the mixing time of the molten steel is shortest and the stirring efficiency is highest. The higher the insertion depth of the top blowing gun, the faster the flow rate of the molten steel, and the smaller the proportion of dead zones. Top and bottom blowing can improve the mass transfer rate between steel slag and promote the formation of refined slag. Through industrial experiments, it was found that the S content in the molten steel decreased by approximately 22.3% and the total oxygen content decreased by 25% before and after 10 min of composite argon blowing at the top and bottom of the ladle. Full article

Summer foxtail millet (Setaria italica L.) is a crucial crop in the arid and semi-arid regions of the North China Plain. Therefore, adopting effective irrigation management strategies is essential for conserving water resources while sustaining millet production in these water-limited areas. A two-year field experiment was conducted in Hengshui in 2020 and 2021 to determine the optimal irrigation amount for foxtail millet and evaluate the critical role of root distribution across various soil depths in determining yield and water productivity. Grain yield, yield-related traits, water use efficiency, and root traits were measured under six irrigation regimes (I0, I1, I2, I3, I4, and I5). Grain yield significantly increased with irrigation, but no further significant yield improvement was observed between the I3 and I5 treatments. The highest water productivity was observed under I3 in 2020 and I2 in 2021. Biomass, thousand grain weight, abortive grain rate, panicle dry weight, and water use efficiency under I3 were similar to those under I4 and I5 treatments. Root traits, including total root length, surface area, volume, and dry weight, did not significantly differ between I3, I4, and I5. Grey relational analysis indicated that total water content in the shallow soil layer (0–40 cm) had the greatest impact on yield. Overall, the I3 treatment (150 mm) is recommended as the optimal irrigation amount for increasing foxtail millet production and water use efficiency. Full article

Soils in alpine mining areas suffer from severe heavy metal contamination and infertility, yet little is known about the effects of different materials on soil improvement in such regions. In this study, a field experiment was conducted in farmlands contaminated by the Lanping lead–zinc mine in Yunnan, China, to compare the effects of four materials (biochar, organic fertilizer, lime, and sepiolite) on soil properties, heavy metal (lead (Pb), cadmium (Cd), copper (Cu), and zinc (Zn) fractions and their availability, and the growth of Phaseolus coccineus L. Results showed that biochar and organic fertilizer significantly enhanced soil nutrient content and enzyme activities. Lime, biochar, and sepiolite effectively reduced heavy metal bioavailability by promoting their transition to residual fractions. Notably, biochar outperformed other materials by substantially increasing grain yield (by 82%), improving nutritional quality (sugars, protein, and starch contents raised by 20–88%), and reducing heavy metal accumulation in grains (by 36–50%). A comprehensive evaluation based on subordinate function values confirmed biochar as the most effective amendment. Structural equation modeling further revealed that biochar promoted plant growth and grain quality primarily by enhancing soil available nutrients and immobilizing heavy metals. These findings demonstrate the strong potential of biochar for remediating heavy metal-contaminated farmlands in alpine lead–zinc mining regions. Full article

Bioavailable phosphorus is essential for sustaining high crop productivity, yet excessive inorganic P fertilization often leads to P accumulation in stable soil forms, reducing utilization efficiency. Straw serves as an organic P source and enhances P availability by stimulating microbial activity. However, systematic studies on how organic P inputs (straw returning) and inorganic P fertilizers regulate soil bioavailable P through microbial and enzymatic processes remain limited. A 16-year field experiment was carried out in a rice–wheat rotation system, including five fertilization treatments: no fertilization (CK), optimized fertilization (OPT), increased N (OPTN), increased P (OPTP), and optimized fertilization combined with straw mulching/returning (OPTM). This study evaluates the impacts of long-term organic and inorganic P sources on soil P fractions, extracellular enzyme activities, and the composition of microbial communities, alongside their collective contributions to crop yield. In this study, based on soil samples collected in 2023, we found that fertilization led to significant increases in Citrate-P and HCl-P, enhanced the activities of β-1,4-glucosidase (BG), β-D-cellobiosidase (CBH), and β-1,4-N-acetylglucosaminidase (NAG), and altered both microbial diversity and co-occurrence network complexity. The OPTM treatment showed the highest yield and improved microbial diversity and network complexity, with Enzyme-P, Citrate-P, and HCl-P increasing by 62.64%, 11.24%, and 9.49%, and BG, CBH, and NAG activities rising by 22.74%, 40.90%, and 18.09% compared to OPT. Mantel tests and random forest analyses revealed significant associations between microbial community and biochemical properties, while partial least squares path modeling (PLS-PM) indicated that inorganic P source enhanced yield primarily through altering soil P dynamics and enzymatic processes, while microbial communities under organic P source acted as key mediators to increase crop productivity. These findings deepen insights into how microbial communities and enzymatic stoichiometry synergistically regulate phosphorus bioavailability and wheat yield, providing a theoretical basis for sustainable fertilization practices in rice–wheat rotation systems. Full article

Magnetic nanoparticles (MNPs) perturb magnetic field homogeneity, influencing transverse relaxation and the full width at half maximum (FWHM) of nuclear magnetic resonance (NMR) spectra. In Nuclear Magnetic Resonance (NMR), this appears as decay of the free induction decay (FID) signal, whose relaxation rate determines spectral FWHM. In D₂O containing MNPs, both nanoparticles and solvent molecules undergo Brownian motion and diffusion. Under a vertical main field ($B_0$), MNPs respond to their magnetization behavior, evolving toward a dynamic steady state in which the time-averaged distribution of local field fluctuations remains stable. The resulting spatial magnetic field can thus characterize field homogeneity. Within this framework, Monte Carlo simulations of spatial field distributions approximate the dynamic environment experienced by nuclear spins. NMR experiments confirm that increasing MNP concentration and particle size significantly broadens FWHM, while stronger $B_0$ enhances sensitivity to MNP-induced inhomogeneities. Full article

Archaeological excavations today generate extensive datasets across survey, excavation, and analysis activities, especially when they are conducted in collaborative structures such as field schools. Working across such activities, data archivists contribute to the goals and research outcomes of the dig by establishing data practices that are participatory and educational (two pillars of data literacy) as they permanently record information about the archaeological results. At the Venus Pompeiana Project (VPP), a collaborative archaeological investigation of the Sanctuary of Venus in Pompeii, both provenance and provenience data are recorded into a database at the trenches’ edge, which optimises the accuracy of the data by allowing direct input and review by the data creators and archaeological site experts. When legacy data about work conducted decades or even centuries earlier are brought into the data picture, scholars stand to gain a deeper understanding of the geographic locations of key interest over time. Yet, the integration of analogue legacy and digital archival datasets is collaborative and longitudinal work. In this paper, we bring together experiential reflections on data archiving conducted at both the excavation site and in the physical archives of the Pompeii Archaeological Park. We then provide an integrative analysis of the outcomes of such data curation, highlighting what each data archiving contributor “discovered” about the site as a whole or a specific artefact, feature, or data category. Our findings contribute deeper insights into what data archiving and format-specific curation activities are most effective for learning experiences, archaeological scholarship, and professional practices. Full article

Three-dimensional fragment assembly technology has significant application value in fields such as cultural relic restoration, medical image analysis, and industrial quality inspection. To address the common challenges of limited feature representation ability and insufficient assembling accuracy in existing methods, this paper proposes a geometry-aware hierarchical fragment assembly framework (GeoAssemble). The core contributions of our work are threefold: first, the framework utilizes DGCNN to extract local geometric features while integrating centroid relative positions to construct a multi-dimensional feature representation, thereby enhancing the identification quality of fracture points; secondly, it designs a two-stage matching strategy that combines global shape similarity coarse matching with local geometric affinity fine matching to effectively reduce matching ambiguity; finally, we propose an auxiliary transformation estimation mechanism based on the geometric center of fracture point clouds to robustly initialize pose parameters, thereby improving both alignment accuracy and convergence stability. Experiments conducted on both synthetic and real-world fragment datasets demonstrate that this method significantly outperforms baseline methods in matching accuracy and exhibits higher robustness in multi-fragment scenarios. Full article

Aquaculture lagoons must reconcile visitor access with biodiversity protection. This study integrates results of a large survey of the attitudes of tour operators with field observations of fish populations to test whether operator choices can align tourism and conservation. Using data from 801 guided-tour participants in Taiwan’s Cigu Lagoon, a sequential experience hierarchy was validated whereby environmental knowledge enhanced attitudes, strengthened perceived guide professionalism, induced flow, and ultimately increased conservation intention (R² = 0.523). Experiential service quality exerted stronger effects than functional quality (β = 0.287 vs. 0.156; both p < 0.001). Parallel underwater monitoring indicated that electric, low-vibration motors were associated with richer fish assemblages and larger fish body sizes; fish abundance is 61% higher and mean body length 38% greater, with community composition differing significantly by motor type (PERMANOVA, p < 0.001). Together, these results link training and technology adoption to measurable ecological gains and pro-conservation motivation, indicating that electrified propulsion and interpretive practice are mutually reinforcing levers for biodiversity-positive tourism. The framework offers directly actionable criteria—motor choice, guide development, and safety/facility context—for transitioning small-scale fisheries and recreation toward low-impact marine experiences. Full article

The accurate monitoring of crop water status is critical for optimizing irrigation strategies in winter wheat. Compared with satellite remote sensing, unmanned aerial vehicle (UAV) technology offers superior spatial resolution, temporal flexibility, and controllable data acquisition, making it an ideal choice for the small-scale monitoring of crop water status. During 2023–2025, field experiments were conducted to predict crop water status using UAV images in the North China Plain (NCP). Thirteen vegetation indices were calculated and their correlations with observed crop water content (CWC) and equivalent water thickness (EWT) were analyzed. Four machine learning (ML) models, namely, random forest (RF), decision tree (DT), LightGBM, and CatBoost, were evaluated for their inversion accuracy with regard to CWC and EWT in the 2024–2025 growing season of winter wheat. The results show that the ratio vegetation index (RVI, NIR/R) exhibited the strongest correlation with CWC (R = 0.97) during critical growth stages. Among the ML models, CatBoost demonstrated superior performance, achieving R² values of 0.992 (CWC) and 0.962 (EWT) in training datasets, with corresponding RMSE values of 0.012% and 0.1907 g cm⁻², respectively. The model maintained robust performance in testing (R² = 0.893 for CWC, and R² = 0.961 for EWT), outperforming conventional approaches like RF and DT. High-resolution (5 cm) inversion maps successfully identified spatial variability in crop water status across experimental plots. The CatBoost-RVI framework proved particularly effective during the booting and flowering stages, providing reliable references for precision irrigation management in the NCP. Full article

Effective spam detection systems are essential in online social media networks (OSNs) and cybersecurity, and they directly influence the quality of decision-making pertaining to security. With today’s digital communications, unsolicited spam degrades user experiences and threatens platform security. Machine learning-based spam detection systems offer an automated defense. Despite their effectiveness, such methods are frequently hindered by the “black box” problem, an interpretability deficiency that constrains their deployment in security applications, which, in order to comprehend the rationale of classification processes, is crucial for efficient threat evaluation and response strategies. However, their effectiveness hinges on selecting an optimal feature subset. To address these issues, we propose a lightweight, explainable spam detection model that integrates a nature-inspired optimizer. The approach employs clean data with data preprocessing and feature selection using a swarm-based, nature-inspired meta-heuristic Rüppell’s Fox Optimization (RFO) algorithm. To the best of our knowledge, this is the first time the algorithm has been adapted to the field of cybersecurity. The resulting minimal feature set is used to train a supervised classifier that achieves high detection rates and accuracy with respect to spam accounts. For the interpretation of model predictions, Shapley values are computed and illustrated through swarm and summary charts. The proposed system was empirically assessed using two datasets, achieving accuracies of 99.10%, 98.77%, 96.57%, and 92.24% on Dataset 1 using RFO with DT, KNN, AdaBoost, and LR and 98.94%, 98.67%, 95.04%, and 94.52% on Dataset 2, respectively. The results validate the efficacy of the suggested approach, providing an accurate and understandable model for spam account identification. This study represents notable progress in the field, offering a thorough and dependable resolution for spam account detection issues. Full article

In practical Synthetic Aperture Radar (SAR) applications, new-class objects can appear at any time as the rapid accumulation of large-scale and high-quantity SAR imagery and are usually supported by limited instances in most cooperative scenarios. Hence, powering advanced deep-learning (DL)-based SAR Automatic Target Recognition (SAR ATR) systems with the ability to continuously learn new concepts from few-shot samples without forgetting the old ones is important. In this paper, we tackle the Few-Shot Class-Incremental Learning (FSCIL) problem in the SAR ATR field and propose a Forward-Compatible Prototype Classifier (FCPC) by emphasizing the model’s forward compatibility to incoming targets before and after deployment. Specifically, the classifier’s sensitivity to diversified cues of emerging targets is improved in advance by a Virtual-class Semantic Synthesizer (VSS), considering the class-agnostic scattering parts of targets in SAR imagery and semantic patterns of the DL paradigm. After deploying the classifier in dynamic worlds, since novel target patterns from few-shot samples are highly biased and unstable, the model’s representability to general patterns and its adaptability to class-discriminative ones are balanced by a Decoupled Margin Adaptation (DMA) strategy, in which only the model’s high-level semantic parameters are timely tuned by improving the similarity of few-shot boundary samples to class prototypes and the dissimilarity to interclass ones. For inference, a Nearest-Class-Mean (NCM) classifier is adopted for prediction by comparing the semantics of unknown targets with prototypes of all classes based on the cosine criterion. In experiments, contributions of the proposed modules are verified by ablation studies, and our method achieves considerable performance on three FSCIL of SAR ATR datasets, i.e., SAR-AIRcraft-FSCIL, MSTAR-FSCIL, and FUSAR-FSCIL, compared with numerous benchmarks, demonstrating its superiority and effectiveness in dealing with the FSCIL of SAR ATR. Full article

Leaf water content (LWC) and chlorophyll content (CHL) are pivotal physiological indicators for assessing citrus growth and stress responses. However, conventional measurement techniques—such as fresh-to-dry weight ratio and spectrophotometry—are destructive, time-consuming, and limited in spatial and temporal resolution, making them unsuitable for large-scale monitoring. To achieve efficient large-scale monitoring, this study proposes a synergistic inversion framework integrating UAV multispectral remote sensing with intelligent optimization algorithms. Field experiments during the 2024 growing season (April–October) in western Hubei collected 263 ground measurements paired with multispectral images. Sensitive spectral bands and vegetation indices for LWC and CHL were identified through Pearson correlation analysis. Five modeling approaches—Partial Least Squares Regression (PLS); Extreme Learning Machine (ELM); and ELM optimized by Particle Swarm Optimization (PSO-ELM), Artificial Hummingbird Algorithm (AHA-ELM), and Grey Wolf Optimizer (GWO-ELM)—were evaluated. Results demonstrated that (1) VI-based models outperformed raw spectral band models; (2) the PSO-ELM synergistic inversion model using sensitive VIs achieved optimal accuracy (validation R²: 0.790 for LWC, 0.672 for CHL), surpassing PLS by 15.16% (LWC) and 53.78% (CHL), and standard ELM by 20.80% (LWC) and 25.84% (CHL), respectively; and (3) AHA-ELM and GWO-ELM also showed significant enhancements. This research provides a robust technical foundation for precision management of citrus orchards in drought-prone regions. Full article

The present research moves the focus from merely describing epikarst flow to quantifying its natural filtration performance and contaminant retention mechanisms through integrating in situ tracer experiments with controlled laboratory modelling—an approach seldom applied in previous studies. Two field experiments at Peč Cave demonstrated that the epikarst exhibits rapid hydraulic connectivity—evidenced by fast tracer breakthrough with virtual flow speeds between 0.0041 and 0.006 m/s—yet simultaneously provides strong attenuation, as shown by the low tracer recovery and near-complete removal of microbial contaminants as well as nitrogen compounds through retention, degradation, and dilution under natural infiltration conditions, including rainfall and snowmelt. Complementary laboratory simulations further confirmed this duality, with nitrate concentrations reduced by 30–50%. Field data and lab results consistently indicated that the epikarst does not merely transmit water but actively adsorbs and transforms pollutants. Overall, the epikarst on Suva Planina functions as an effective natural filtration layer that substantially improves groundwater quality before it reaches major karst springs, acting as a protective yet vulnerable “skin” of the aquifer. These findings highlight the epikarst’s critical role in Suva planina Mt. karst aquifer protection and results support consideration of epikarst in groundwater management strategies, particularly in regions where springs are used for public water supply. Full article

Laser-accelerated electron beams, in the so-called Very High-Energy Electron (VHEE) energy range, are of great interest for biomedical applications. For instance, laser-driven VHEE beams are envisaged to offer suitable compact accelerators for the promising field of FLASH radiotherapy. Radiobiology experiments carried out using laser-driven beams require the real-time knowledge of the dose delivered to the sample. We have developed an online dose monitoring procedure, using an Integrating Current Transformer (ICT) coupled to a suitable collimator, that allows the estimation of the delivered dose on a shot-to-shot basis under suitable assumptions. The cross-calibration of the measured charge with standard offline dosimetry measurements carried out with RadioChromic Films (RCFs) is discussed, demonstrating excellent correlation between the two measurements. Full article

With the increasing global demand for energy, the development of unconventional resources has become a focal point of research. Among these, shale gas has drawn considerable attention due to its abundant reserves. However, its low permeability and complex fracture networks present substantial challenges. This study investigates the composite fracturing technology combining supercritical CO₂ and slickwater for shale gas extraction, elucidating the mechanisms by which it influences shale fracture roughness and conductivity through an integrated approach of theory, experiments, and numerical modeling. Experimental results demonstrate that the surface roughness of shale fractures increases markedly after supercritical CO₂–slickwater treatment. Moreover, the dynamic evolution of permeability and porosity is governed by roughness strain, adsorption expansion, and corrosion compression strain. Based on fluid–solid coupling theory, a mathematical model was developed and validated via numerical simulations. Sensitivity analysis reveals that fracture density and permeability have a pronounced impact on shale gas field productivity, whereas fracture dip angle exerts a comparatively minor effect. The findings provide a theoretical basis for optimizing composite fracturing technology, thereby enhancing shale gas extraction efficiency and promoting effective resource utilization. Full article