Search Results (151)

Artificial reservoirs in arid regions provide unique ecological environments for studying the spatial and functional dynamics of plankton communities under the combined stressors of climate change and anthropogenic activities. This study conducted a systematic investigation of the phytoplankton community structure and its environmental drivers in 17 artificial reservoirs in the Ili region of Xinjiang in August and October 2024. The Ili region is located in the temperate continental arid zone of northwestern China. A total of 209 phytoplankton species were identified, with Bacillariophyta, Chlorophyta, and Cyanobacteria comprising over 92% of the community, indicating an oligarchic dominance pattern. The decoupling between numerical dominance (diatoms) and biomass dominance (cyanobacteria) revealed functional differentiation and ecological complementarity among major taxa. Through multivariate analyses, including Mantel tests, principal component analysis (PCA), and redundancy analysis (RDA), we found that phytoplankton community structures at different ecological levels responded distinctly to environmental gradients. Oxidation-reduction potential (ORP), dissolved oxygen (DO), and mineralization parameters (EC, TDS) were key drivers of morphological operational taxonomic unit (MOTU). In contrast, dominant species (SP) were more responsive to salinity and pH. A seasonal analysis demonstrated significant shifts in correlation structures between summer and autumn, reflecting the regulatory influence of the climate on redox conditions and nutrient solubility. Machine learning using the random forest model effectively identified core taxa (e.g., MOTU1 and SP1) with strong discriminatory power, confirming their potential as bioindicators for water quality assessments and the early warning of ecological shifts. These core taxa exhibited wide spatial distribution and stable dominance, while localized dominant species showed high sensitivity to site-specific environmental conditions. Our findings underscore the need to integrate taxonomic resolution with functional and spatial analyses to reveal ecological response mechanisms in arid-zone reservoirs. This study provides a scientific foundation for environmental monitoring, water resource management, and resilience assessments in climate-sensitive freshwater ecosystems. Full article

Fluids that exhibit self-rewetting properties, such as aqueous long-chain alcohol solutions, display a unique quadratic relationship between surface tension and temperature and are marked by a positive gradient. This characteristic leads to distinctive patterns of thermocapillary convection and associated interfacial dynamics, setting self-rewetting fluids apart from normal fluids (NFs). The potential to improve heat transfer using self-rewetting fluids (SRFs) is garnering interest for use in various technologies, including low-gravity conditions and microfluidic systems. Our research aims to shed light on the contrasting behaviors of SRFs in comparison to NFs regarding interfacial transport phenomena. This study focuses on the thermocapillary convection in SRF layers with a deformable interface enclosed inside a closed container modeled as a square cavity, which is subject to nonuniform heating, represented using a Gaussian profile for the heat flux variation on one of its sides, in the absence of gravity. To achieve this, we have enhanced a central-moment-based lattice Boltzmann method (LBM) utilizing three distribution functions for tracking interfaces, computing two-fluid motions with temperature-dependent surface tension and energy transport, respectively. Through numerical simulations, the impacts of several characteristic parameters, including the viscosity and thermal conductivity ratios, as well as the surface tension–temperature sensitivity parameters, on the distribution and magnitude of the thermocapillary-driven motion are examined. In contrast to that in NFs, the counter-rotating pair of vortices generated in the SRF layers, due to the surface tension gradient at the interface, is found to be directed toward the SRF layers’ hotter zones. Significant interfacial deformations are observed, especially when there are contrasts in the viscosities of the SRF layers. The thermocapillary convection is found to be enhanced if the bottom SRF layer has a higher thermal conductivity or viscosity than that of the top layer or when distributed, rather than localized, heating is applied. Furthermore, the higher the magnitude of the effect of the dimensionless quadratic surface tension sensitivity coefficient on the temperature, or of the effect of the imposed heat flux, the greater the peak interfacial velocity current generated due to the Marangoni stresses. In addition, an examination of the Nusselt number profiles reveals significant redistribution of the heat transfer rates in the SRF layers due to concomitant nonlinear thermocapillary effects. Full article

This study investigates pre-seismic ionospheric anomalies preceding the 2023 Jishishan Ms6.2 earthquake using total electron content (TEC) data derived from BDS geostationary orbit (GEO) satellites. Multi-scale analysis integrating Butterworth filtering and wavelet transforms resolved TEC disturbances into three distinct frequency regimes: (1) high-frequency perturbations (0.56–3.33 mHz) showed localized disturbances (amplitude ≤ 4 TECU, range < 300 km), potentially associated with near-field acoustic waves from crustal stress adjustments; (2) mid-frequency signals (0.28–0.56 mHz) exhibited anisotropic propagation (>1200 km) with azimuth-dependent N-shaped waveforms, consistent with the characteristics of acoustic–gravity waves (AGWs); and (3) low-frequency components (0.18–0.28 mHz) demonstrated phase reversal and power-law amplitude attenuation, suggesting possible lithosphere–atmosphere–ionosphere (LAI) coupling oscillations. The stark contrast between near-field residuals and far-field weak fluctuations highlighted the dominance of large-scale atmospheric gravity waves over localized acoustic disturbances. Geometry-based velocity inversion revealed incoherent high-frequency dynamics (5–30 min) versus anisotropic mid/low-frequency traveling ionospheric disturbance (TID) propagation (30–90 min) at 175–270 m/s, aligning with theoretical AGW behavior. During concurrent G1-class geomagnetic storm activity, spatial attenuation gradients and velocity anisotropy appear primarily consistent with seismogenic sources, providing insights for precursor discrimination and contributing to understanding multi-scale coupling in seismo-ionospheric systems. Full article

Multimodal image fusion and semantic segmentation play pivotal roles in autonomous driving and robotic systems, yet their inherent interdependence remains underexplored. To address this gap and overcome performance bottlenecks, we propose SC-CoSF, a novel coupled framework that jointly optimizes these tasks through synergistic learning. Our approach replaces traditional duplex encoders with a weight-sharing CNN encoder, implicitly aligning multimodal features while reducing parameter overhead. The core innovation lies in our Self-correction and Collaboration Fusion Module (Sc-CFM), which integrates (1) a Self-correction Long-Range Relationship Branch (Sc-LRB) to strengthen global semantic modeling, (2) a Self-correction Fine-Grained Branch (Sc-FGB) for enhanced visual detail retention through local feature aggregation, and (3) a Dual-branch Collaborative Recalibration (DCR) mechanism for cross-task feature refinement. This design preserves critical edge textures and color contrasts for segmentation while leveraging segmentation-derived spatial priors to guide fusion. We further introduce the Interactive Context Recovery Mamba Decoder (ICRM) to restore lost long-range dependencies during the upsampling process; meanwhile, we propose the Region Adaptive Weighted Reconstruction Decoder (ReAW), which is mainly used to reduce feature redundancy in image fusion tasks. End-to-end joint training enables gradient propagation across all task branches via shared parameters, exploiting inter-task consistency for superior performance. Experiments demonstrate significant improvements over independently optimized baselines in both fusion quality and segmentation accuracy. Full article

Detecting small targets in infrared search and track (IRST) systems in complex backgrounds poses a significant challenge. This study introduces a novel detection framework that integrates directional derivative correlation filtering (DDCF) with a local relative intensity contrast measure (LRICM) to effectively handle diverse background disturbances, including cloud edges and structural corners. This approach involves converting the original infrared image into an infrared gradient vector field (IGVF) using a facet model. Exploiting the distinctive characteristics of small targets in second-order derivative computations, four directional filters are designed to emphasize target features while suppressing edge clutter. The DDCF map is then constructed by merging the results of the second-order derivative filters applied in four distinct orientations. Subsequently, the LRICM is determined by analyzing the gray-level contrast between the target and its immediate surroundings, effectively minimizing interference from background elements like corners. The final detection step involves fusing the DDCF and LRICM maps to generate a comprehensive saliency representation, which is then processed using an adaptive thresholding technique to extract small targets accurately. Experimental evaluations across multiple datasets verify that the proposed method substantially improves the signal-to-clutter ratio (SCR). Compared to existing advanced techniques, the proposed approach demonstrates superior detection reliability in challenging environments, including ground surfaces, cloudy conditions, forested areas, and urban structures. Moreover, the framework maintains low computational complexity, achieving a favorable balance between detection accuracy and efficiency, thereby demonstrating promising potential for deployment in practical IRST scenarios. Full article

The study presents data on the role of lithological factors in the divergence of soil formation in forest–steppe and steppe ecosystems in a region of the East European Plain characterized by extremely contrasting geogenic conditions. Soils from different lithologic–geomorphologic combinations in the Samara region were chosen as the study object. It was shown that, in some cases, bioclimatogenic conditions are less decisive in the formation of the morphological organization and basic chemical parameters of the solum than the lithological characteristics of a particular locality. These lithological factors can transform soil morphology and affect the taxonomic position of soils at the subtype level and below. In landscapes marked by spatial and lithological contrasts at meso- and macro-levels, the use of a bioclimatic classification approach becomes inadequate, because it fails to highlight individual soil features. Thus, the development of lithological taxonomic and diagnostic criteria is necessary for the protection, proper use, and mapping of soils in complex geogenic, particularly lithological, conditions. Within one soil climatic zone, there can exist a large number of lithological soil subtypes, genera, and varieties. In such cases, the lithological framework has a stronger influence on soil spatial distribution than climatic gradients and associated vegetation ecotones. Full article

Climate change poses a significant threat to agricultural productivity, particularly in regions vulnerable to extreme temperatures and water scarcity, such as Irbid, Jordan. This study assesses the future impacts of projected shifts in precipitation and temperature on wheat yields, using the Decision Support System for Agrotechnology Transfer (DSSAT) model for calibrating and validating under local agro-environmental conditions. Two shared socioeconomic pathways (SSP3-7.0 and SSP5-8.5), representing high-emission and fossil-fuel-intensive futures, were evaluated across mid- and late-century periods (2030–2060 and 2070–2100). The DSSAT model was calibrated using local field data to simulate crop phenology, biomass accumulation, and nitrogen dynamics, showing strong agreement with observed grain yield and harvest index, thereby confirming its reliability for climate impact assessments. Yield projections under each scenario were further analyzed using machine learning algorithms—random forest and gradient boosting regression—to quantify the influence of individual climate variables. The results showed that under SSP5-8.5 (2030–2060), precipitation was the dominant factor influencing yield variability, underscoring the critical role of water availability. In contrast, under SSP3-7.0 (2070–2100), rising maximum temperatures became the primary constraint, highlighting the growing risk of heat stress. Predictive accuracy was higher in precipitation-dominated scenarios (R² = 0.81) than in temperature-dominated cases (R² = 0.65–0.73), reflecting greater complexity under extreme warming. These findings emphasize the value of integrating well-calibrated crop models with climate projections and machine learning tools to support climate-resilient agricultural planning. Moreover, practical adaptation strategies, such as adjusting planting dates, using heat-tolerant varieties, and optimizing irrigation, are recommended to enhance resilience. Emerging techniques such as seed priming show promise and merit integration into future crop models. The findings support SDG 2 and SDG 13 by informing climate-resilient food production strategies. Full article

This study combines field and laboratory analyses from seven shallow wells (ZK1 to ZK7) positioned perpendicular to the coastline to investigate groundwater discharge and dynamics in the coastal unconfined aquifer of the intertidal zone at Yazhou Bay, Sanya, Hainan Province. The research highlights spatial variations in N₂O concentration, temperature, electrical conductivity (EC), pH, and the distribution of CFCs and CCl₄ in shallow groundwater, utilizing samples from wells ZK1 to ZK7 and seawater collected near ZK1. Key findings indicate that groundwater temperature decreases toward the ocean, while EC exhibits a stepwise increase from land to sea, with a sharp transition near ZK3 marking the freshwater–saltwater mixing zone. pH values are lowest in ZK3 and ZK4, gradually rising both inland and seaward. N₂O concentrations in the shallow wells (ZK1–ZK7) are divided into two distinct groups: higher concentrations (9.69–57.77 nmol/kg) in ZK5–ZK7 and lower concentrations (6.63–23.03 nmol/kg) in ZK1–ZK4. Wells ZK3 and ZK4 show minimal variation in CFC-11 and CFC-113 concentrations, suggesting they represent a transition zone that likely delineates groundwater flow paths. In contrast, significant concentration differences in wells ZK5–ZK7 (north) and ZK1–ZK2 (south) reflect the influence of aquifer structure variability, recharge sources, and local hydrogeochemical conditions. CFC-12 concentrations exhibit a clear freshwater–saltwater mixing gradient between ZK3 and ZK1, with higher concentrations in freshwater-dominated areas (ZK3–ZK7) and lower concentrations near seawater (ZK1). CCl₄ concentrations at ZK7 and ZK3 differ markedly from other wells, indicating unique hydrogeochemical conditions or localized anthropogenic influences. A model for the formation of upper saline plumes (USP) under tidal forcing at the low tidal line was established previously. Here, we establish a new model that accounts for the absence of USP driven by hydrological processes influenced by artificial sandy beach topography, and a fresh groundwater wedge is identified, which can serve as a significant fast-flow pathway for terrestrial water and nutrients to the ocean. Full article

Firefighting foam is widely recognized for its excellent fire suppression performance. However, research on the effect of foam expansion ratio on the suppression efficiency of forest surface fires remains limited. In this study, the expansion ratio was adjusted by varying the air-to-liquid ratio in a compressed air foam system, and laboratory-scale foam suppression experiments were conducted. Key performance indicators, including extinguishing coverage time, internal cooling rate, and resistance to reignition, were systematically measured. The effects of expansion ratio on the diffusion and penetration behavior of foam on the fuel bed surface were then investigated to understand how these characteristics influence suppression performance. The results indicate that both excessively low and high expansion ratios can weaken fire suppression effectiveness. Low-expansion foam, characterized by low viscosity and high water content, exhibits strong local penetration and cooling capabilities. However, it struggles to rapidly cover the fuel bed surface and isolate oxygen, thereby reducing the overall suppression efficiency. In contrast, high-expansion foam has greater viscosity, allowing it to spread across the fuel bed surface under pressure gradient forces and form a stable coverage layer, effectively limiting the oxygen supply required for combustion. However, its limited depth penetration and lower water content reduce internal cooling efficiency, increasing the risk of reignition. The optimal expansion ratio was determined to be 15.1. Additionally, increasing the liquid supply flow rate significantly improved suppression performance; however, this improvement plateaued when the flow rate exceeded 10 L/min. Full article

The southern slope of the Tianshan Mountains features complex terrain and an arid climate, yet paradoxically experiences frequent extreme precipitation events (EPEs), which pose significant challenges for weather forecasting. This study investigates an EPE that occurred from 20 to 21 August 2019 using multi-source data to examine circulation patterns, mesoscale characteristics, moisture dynamics, and energy-instability mechanisms. The results reveal distinct spatiotemporal variability in precipitation, prompting a two-stage analytical framework: stage 1 (western plains), dominated by localized convective cells, and stage 2 (northeastern mountains), characterized by orographically enhanced precipitation clusters. The event was associated with a “two ridges and one trough” circulation pattern at 500 hPa and a dual-core structure of the South Asian high at 200 hPa. Dynamic forcing stemmed from cyclonic convergence, vertical wind shear, low-level convergence lines, water vapor (WV) transport, and jet-induced upper-level divergence. A stronger vorticity, divergence, and vertical velocity in stage 1 resulted in more intense precipitation. The thermodynamic analysis showed enhanced low-level cold advection in the plains before the event. Sounding data revealed increases in precipitable water and convective available potential energy (CAPE) in both stages. WV tracing showed vertical differences in moisture sources: at 3000 m, ~70% originated from Central Asia via the Caspian and Black Seas; at 5000 m, source and path differences emerged between stages. In stage 1, specific humidity along each vapor track was higher than in stage 2 during the EPE, with a 12 h pre-event enhancement. Both stages featured rapid convective cloud growth, with decreases in total black body temperature (TBB) associated with precipitation intensification. During stage 1, the EPE center aligned with a large TBB gradient at the edge of a cold cloud zone, where vigorous convection occurred. In contrast to typical northern events, which are linked to colder cloud tops and vigorous convection, the afternoon EPE in stage 2 formed near cloud edges with lesser negative TBB values. These findings advance the understanding of multi-scale extreme precipitation mechanisms in arid mountains, aiding improved forecasting in complex terrains. Full article

An accurate 3D pose estimation of spherical objects remains challenging in industrial inspections and robotics due to their geometric symmetries and limited feature discriminability. This study proposes a texture-optimized simulation framework to enhance pose prediction accuracy through optimizing the surface texture features of the design samples. A hierarchical texture design strategy was developed, incorporating complexity gradients (low to high) and color contrast principles, and implemented via VTK-based 3D modeling with automated Euler angle annotations. The framework generated 2297 synthetic images across six texture variants, which were used to train a MobileNet model. The validation tests demonstrated that the high-complexity color textures achieved superior performance, reducing the mean absolute pose error by 64.8% compared to the low-complexity designs. While color improved the validation accuracy universally, the test set analyses revealed its dual role: complex textures leveraged chromatic contrast for robustness, whereas simple textures suffered color-induced noise (a 35.5% error increase). These findings establish texture complexity and color complementarity as critical design criteria for synthetic datasets, offering a scalable solution for vision-based pose estimation. Physical experiments confirmed the practical feasibility, yielding 2.7–3.3° mean errors. This work bridges the simulation-to-reality gaps in symmetric object localization, with implications for robotic manipulation and industrial metrology, while highlighting the need for material-aware texture adaptations in future research. Full article

To investigate the spatiotemporal distribution of early-age hydration heat-induced temperature fields, this study integrates distributed fiber optic sensing (DFOS) technology with a thermal parameter finite element model (FEM). First, a high-precision DFOS system and traditional point-type semiconductor sensors were deployed to continuously monitor the temperature of a 50 m large-volume concrete box girder (LVBG) over 100 h. Experimental results show that full-field LVBG temperature changes can be measured by DFOS compared to traditional point sensors. DFOS, leveraging its full-scale spatial coverage capability, revealed a three-stage temperature evolution: rapid heating (peak temperature of 79.4 °C at 40 h), sustained high temperatures (>75 °C for 20 h), and gradual cooling (rate: 0.45 °C/h). In contrast, conventional point sensors may miss localized hotspots due to insufficient spatial coverage. Second, a FEM was developed on the ABAQUS 2021 (finite element analysis software) platform, incorporating a UMATHT (user material thermal) subroutine to update temperature-dependent thermal conductivity and specific heat in real time during hydration heat transfer simulations. The proposed model significantly improved prediction accuracy by integrating parameter mechanisms (equivalent age), and it improved prediction accuracy by about 40% compared to static-parameter models. The FEM results exhibited strong consistency with DFOS-measured data, validating the model’s reliability in capturing thermal gradients in geometrically complex structures. This validated framework offers a robust tool for optimizing thermal management strategies in large-scale infrastructure projects. The research results of this paper can serve as a reference for the temperature measurement and prediction of large-volume concrete. Full article

Cadmium (Cd) contamination poses severe threats to agricultural productivity and ecosystem health. Biochar has shown promise in immobilizing Cd and enhancing microbial functions, yet its pH-dependent mechanisms remain underexplored. This study aimed to elucidate pH-dependent variations in biochar-mediated cadmium (Cd) immobilization efficiency, nitrification activity, and bacterial community diversity across soils of contrasting pH levels, with mechanistic insights into the synergistic interplay between biochar properties and soil pH. Real-time quantitative PCR (qPCR) and high-throughput sequencing were used to investigate the effects of a 1% (w/w) biochar amendment on ammonia-oxidizing microorganism abundance and microbial diversity in neutral Shandong soil (SD, pH 7.46) and acidic Yunnan soil (YN, pH 5.88). In neutral SD soil, available Cd decreased from 0.22 mg kg⁻¹ (day 0) to 0.1 mg kg⁻¹ (day 56) and stabilized, accompanied by insignificant changes in ammonia-oxidizing bacteria (AOB) abundance. However, nitrification activity was enhanced through the enrichment of Nitrospira (nitrite-oxidizing bacteria within Nitrospirales and Nitrospiraceae). In acidic YN soil, biochar reduced available Cd by 53.37% over 56 days, concurrent with a 34.28% increase in AOB amoA gene abundance (predominantly Nitrosomonadales), driving pH-dependent nitrification enhancement. These findings demonstrated that biochar efficacy was critically modulated by soil pH; the acidic soils require higher biochar dosages (>1% w/w, adjusted to local soil properties and agronomic conditions) for optimal Cd immobilization. Meanwhile, pH-specific nitrifier taxa (Nitrosomonadales in acidic vs. Nitrospira in neutral soils) underpinned biochar-induced nitrification dynamics. The study provided a mechanistic framework for tailoring biochar remediation strategies to soil pH gradients, emphasizing the synergistic regulation of Cd immobilization and microbial nitrogen cycling. Full article

A GPU-accelerated shallow water model with a local time-step (LTS) is employed in this work to examine how the Coriolis forces affect the tidal level difference and, consequently, the water–sediment exchange between Hangzhou Bay (HZB) and the Yangtze River Estuary. The model is applied to both idealized and realistic estuary configurations to analyze tidal level gradients between the two neighboring estuaries under different flow conditions and with and without the Coriolis force condition. The model’s accuracy in predicting tidal levels and currents was validated against field data. It is shown that the tidal level gradient is negative during flood tide, indicating a mass transfer trend from south to north, whereas the tidal level gradient is positive during ebb tide, indicating a north-to-south mass transfer. Considering sediment originates mainly from the riverine side, the sediment mass transfer may occur mainly during ebb tide, and the direction is from the Yangtze River to the HZB. This finding provides numerical evidence for previous recognition that sediment in HZB mainly comes from the Yangtze River Estuary. A comparison of the idealized and realistic estuary configurations further indicates that the contrasting bed topography enhances tidal level gradients. The findings show that by causing tidal phase changes and asymmetric tidal range modifications, the Coriolis force increases lateral water level gradients (up to 0.7 m) between the Yangtze Estuary and Hangzhou Bay. Idealized modeling further demonstrates that higher Coriolis coefficients promote sediment exchange and exacerbate water level fluctuations across estuaries. Without the Coriolis effect, the tide level distribution in adjacent estuaries is symmetrical. In the Northern and Southern Hemispheres, the tide level distribution in adjacent estuaries is the opposite. In addition, this study has shown that changes in river flow have a limited effect on water levels at stations farther from the estuary’s flow intake and therefore have a negligible effect on the water level gradient in adjacent estuaries farther away. However, topography differences have a significant effect on water level gradients in neighboring estuaries. These studies emphasize the significance of the Coriolis force in regulating sediment transport pathways in estuaries. Full article

In agricultural production, lettuce growth, yield, and quality are impacted by nutrient deficiencies caused by both environmental and human factors. Traditional nutrient detection methods face challenges such as long processing times, potential sample damage, and low automation, limiting their effectiveness in diagnosing and managing crop nutrition. To address these issues, this study developed a lettuce nutrient deficiency detection system using multi-dimensional image analysis and Field-Programmable Gate Arrays (FPGA). The system first applied a dynamic window histogram median filtering algorithm to denoise captured lettuce images. An adaptive algorithm integrating global and local contrast enhancement was then used to improve image detail and contrast. Additionally, a multi-dimensional image analysis algorithm combining threshold segmentation, improved Canny edge detection, and gradient-guided adaptive threshold segmentation enabled precise segmentation of healthy and nutrient-deficient tissues. The system quantitatively assessed nutrient deficiency by analyzing the proportion of nutrient-deficient tissue in the images. Experimental results showed that the system achieved an average precision of 0.944, a recall rate of 0.943, and an F1 score of 0.943 across different lettuce growth stages, demonstrating significant improvements in automation, accuracy, and detection efficiency while minimizing sample interference. This provides a reliable method for the rapid diagnosis of nutrient deficiencies in lettuce. Full article