Search Results (390)

Against the backdrop of intensifying global climate change, reducing carbon emissions has become a shared global objective. Blue carbon, as a significant carbon sink type, still lacks a mature assessment framework. Monitoring carbon fluxes in marine salt marsh wetlands is a core technology for accurately evaluating blue carbon potential. In response, this study independently developed a spatiotemporally coupled carbon flux monitoring system for marine salt marsh wetlands. The system consists of real-time monitoring equipment, a cloud-based intelligent storage and visualization analysis platform, and a terminal assessment system. It enables the real-time monitoring of carbon fluxes across multiple spatial scales and integrates time-series patterns to assess carbon sequestration potential from multiple dimensions. To address the bottleneck of sensor accuracy, a multi-algorithm fusion technology was innovatively developed, significantly enhancing the accuracy of monitoring data. A modular integrated design was employed to construct a land–air integrated monitoring architecture, which is adaptable to the complex environments of salt marsh wetlands. This facilitates long-term automated monitoring while reducing the need for manual intervention. The terminal assessment system processes spatial-scale data using the DeNitrification-DeComposition model (DNDC 9.5) and integrates time-series carbon flux patterns, enabling precise quantification of marine carbon sink potential through spatiotemporal comprehensive analysis. The system first completed performance verification during the experimental phase, acquiring a total of 5760 sets of valid monitoring data, with a data qualification rate of 99.72%. The proposed multi-algorithm fusion method kept monitoring data fluctuations within 0.5%, and the relative error of the spatiotemporal integrated prediction was as low as 0.31%, thereby ensuring the stability and accuracy of long-term in situ monitoring. Based on this, a one-year field validation was conducted in a 100-hectare coastal salt marsh wetland in Dafeng, Yancheng. Using a spatiotemporal coupling assessment, the annual total carbon sequestration of this area was estimated at 1498.4 tons of carbon, with an assessment error of only 5.1%, achieving precise quantification of the blue carbon sink in the salt marsh wetland. This study provides reliable technical support for evaluating the carbon sequestration capacity of coastal salt marsh wetlands, contributing to the implementation of carbon emission reduction strategies. It also offers a scientific basis for global carbon cycle research and carbon sink management decision-making. Full article

Net surface shortwave radiation (NSSR) is a key parameter in the Earth’s energy cycle, greatly affecting global water and heat balance. Currently, a comprehensive comparative analysis regarding the accuracy of different models remains severely lacking, and there is also a notable deficiency in the systematic exploration of seasonal radiative drivers. Therefore, we developed a machine learning-based seasonal NSSR estimation model. By integrating in-situ observational data with multi-source remote sensing datasets, we achieved precise quantification of radiative fluxes. This proposed model framework employed three cutting-edge algorithms, namely Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), to capture the non-linear interactions among radiative drivers across the four seasons. Through mechanistic sensitivity analysis, we quantified the impacts of key variables on NSSR prediction. The results unequivocally demonstrated that the RF algorithm demonstrated the best performance. Its seasonal R² were 0.95 (spring), 0.89 (summer), 0.95 (autumn), and 0.96 (winter). The Solar Zenith Angle (SZA) dominated in spring and winter; its absence reduced R² by 0.23 and raised RMSE by 20.66–26.42 W/m². Meteorological factors mattered most in summer; excluding them cut R² by 0.17 and hiked RMSE by 23.82 W/m². This study provides actionable insights for terrestrial radiation budget research. Full article

Accurate quantification of ecohydrological processes is essential for effective water and carbon management in terrestrial ecosystems. Traditional simulations mainly rely on mechanistic models, yet their accuracy is often limited by inconsistencies in representing physical processes and uncertainties in parameterization. Integrating remote sensing signals offers a promising way to reduce these uncertainties and enhance model applicability. In this study, in-situ observations from a wheat cropland in the Guanzhong Plain were used to simulate gross primary productivity (GPP) and latent heat flux (LE) by comparing a forward model (STEMMUS-SCOPE) with a remote sensing-driven inverse model (STEMMUS-MLR). We further examined the role of solar-induced chlorophyll fluorescence (SIF), an emerging proxy for photosynthesis, as an input to improve mechanistic modeling of GPP and LE. Results show that STEMMUS-MLR outperformed STEMMUS-SCOPE in estimating water and carbon fluxes, demonstrating that incorporating SIF effectively reduces bias associated with uncertainties in parameters and forcing data. The contribution of SIF was quantified using Random Forest regression and Shapley additive explanations (SHAP), revealing that SIF markedly reduced the dependence of GPP and LE simulations on shortwave radiation (SW), air temperature (Ta), and leaf area index (LAI). These findings highlight the critical role of SIF in ecohydrological modeling of semi-arid cropland ecosystems and provide a scientific basis for advancing process understanding and improving the precision management of water and carbon budgets in terrestrial ecosystems. Full article

This work presents a comparative study of micro- and nano-scale fillers in liquid composite molding processes, focusing on how particle size and morphology affect resin rheology, flow behavior, and filler filtration within fiber preforms. Glass microspheres and organo-modified montmorillonite were dispersed in epoxy resin and injected through glass-mat preforms at different fiber volume fractions (ranging from 0.27 to 0.47). Our study integrates rheological characterization, in situ flow-front tracking, unsaturated permeability analysis, thermogravimetric quantification of retained particles, and microstructural observations by SEM. Despite their smaller loading, nanoclay suspensions showed a markedly higher viscosity increase than microsphere systems, yet their permeability remained nearly unchanged. In contrast, microsphere-filled resins exhibited strong filtration at the flow inlet, density-driven settling near the lower tool face, and significant permeability loss. The results demonstrate that nano-fillers, although more viscous, maintain homogeneous distribution and flow continuity, whereas micro-fillers promote cake formation and local compaction. This controlled side-by-side comparison clarifies how filler size and shape govern filtration mechanisms in liquid composite molding (LCM), providing design guidelines for processing filled resin systems without compromising part quality. Full article

Non-exhaust particulate emissions are expected to remain a relevant source of traffic-related air pollution, including an increase in electrified vehicle fleets. Particle formation results from tribological interactions and is influenced by both operating conditions and friction material system. This study presents an extended measurement methodology under application-relevant tribological conditions for the reproducible quantification of PM₁₀ and PM_2.5 emissions from dry-running friction systems and applies it to a systematic investigation of operating parameter and friction pairing effects. A dry inertial brake test bench with an enclosed friction chamber and integrated aerosol measurement chain was used under controlled tribologically relevant conditions. Specific friction work and specific friction power were varied by adjusting sliding velocity, contact pressure, and inertial load. Six friction pairings, comprising four representative friction lining types combined with either C45 cast steel or GGG40 gray cast iron, were examined. In situ PM₁₀ and PM_2.5 measurements were complemented by gravimetric wear and microstructural analyses. The results show that specific friction work has a direct influence on PM₁₀ and PM_2.5 emissions, whereas the independent effect of contact pressure is secondary. Friction power exhibits material-dependent effects. Emissions also vary strongly with friction pairing, indicating that operating conditions and material system must be considered jointly when assessing low-emission brake systems. Full article

While the broader context of molecular machinery has already been extensively discussed in the scientific literature, there is a lack of dedicated reviews focusing specifically and exclusively on information ratchets. These ratchets deserve a dedicated analysis as they are common in nature and their implementation in artificial systems can lead to new ways of achieving biomimetic processes and endergonic synthesis. This review summarizes recent advancements in the design of synthetic information ratchets, highlighting breakthroughs in the rationalization and optimization of fueling and structural parameters for the sake of efficiency. Novel methods are described for in situ quantification and the translation of molecular motion into macroscopic work. The latest artificial information ratchets are compared to the previous literature and the natural motors that inspired them. The reported findings are meant to show how research on information ratcheting has progressed in the last five years, with various designs paving the way to bio-inspired nanotechnologies and materials. Full article

This paper proposes an innovative contact sponge–fluorescent tracer technique for the rapid, non-destructive detection of 1–100 μm microcracks in cementitious materials. The technique combines a porous sponge carrier with a moisture-sensitive fluorescent tracer: after the sponge adsorbs the aqueous dye solution, capillary action drives fluorescent molecules into microcracks upon contact with the wall, ensuring stable luminescence during a 30-day continuous observation period. This technique was applied to cement paste specimens with three different water-to-cement ratios, dried at 105 °C for varying durations to induce drying–shrinkage microcracks. Results demonstrate that the technique clearly characterizes microcrack networks with high resolution and excellent stability. Under the same drying duration, the average microcrack width decreases with an increasing water-to-cement ratio, while the total crack length and fractal dimension increase. Regression analysis reveals that the average crack width is the primary factor controlling capillary water absorption. This method enables the early detection of microcracks in critical infrastructure such as tunnels and bridges, facilitating timely maintenance and reducing deterioration risk. Full article

Hydrogen embrittlement is known to adversely affect the mechanical properties of low-carbon steels used for pipelines and pressure vessels, leading to accelerated crack growth and lowered fracture toughness. To overcome the limitations of surface-based analysis, this study employs X-ray micro-computed tomography (µ-CT) to provide a comprehensive 3D evaluation of the crack evolution. This approach is used to assess hydrogen-assisted crack growth in P355NH compact tension samples from previous fracture mechanical tests and enables a precise quantification of the internal crack path and the crack tip opening angle (CTOA) across the entire specimen thickness as well as the local damage morphology. By integrating these spatial parameters, a deeper understanding of the impact of hydrogen on local fracture mechanisms is achieved, revealing insights that have remained hidden in previous two-dimensional microscopy observations. For instance, µ-CT results clearly demonstrate that the hydrogen-assisted crack propagation is associated with increased void formation and secondary cracking in vicinity of the crack tip. However, it is proposed that the results are superimposed with continuous hydrogen desorption, which implies a need for in situ charging during mechanical loading and an analysis of the hydrogen concentration profile. Both will be the scope of further studies. Full article

Bothriochloa ischaemum is a key forage species with strong grazing tolerance and high nutritional value, making precise quantification of spike and seed traits essential for germplasm evaluation and yield prediction. However, the compact architecture and minute seed size in natural field conditions render manual counting inefficient and labor-intensive. To address this limitation, this study presents a non-destructive and automated quantification framework integrating advanced object detection and regression analysis for accurate in situ estimation of spikes and seed numbers. To further address the challenges of dense spike detection caused by occlusion and small object sizes, this study developed a modified model named YOLOv12-DAN by integrating DySample dynamic upsampling, ASFF feature fusion, and NWD loss, which achieved a mean average precision (mAP) of 91.6%. Meanwhile, for the detection of dense kernels on compact spikes, an improved YOLOv12 architecture incorporating an Explicit Visual Center (EVC) module was proposed to enhance multi-scale feature representation. The optimized model attained a bounding box precision of 96.5%, a recall rate of 86.4%, an mAP50 of 94.3%, and an mAP50-95 of 73.9%. Furthermore, a univariate linear regression model based on 132 spike samples verified the reliable consistency between the predicted and actual seed counts, with a mean absolute error (MAE) of 6.30, a mean absolute percentage error (MAPE) of 9.35, and an R-squared (R²) value of 0.808. Finally, the model was deployed through a lightweight end-to-end web application, enabling real-time field operation and promoting its applicability in breeding programs and agronomic decision-making. This study provides a robust technical pathway for automated phenotyping and precision forage improvement. Full article

Advancing hydropower development is crucial for supporting China’s “Dual Carbon” strategy and ensuring energy security. A key safety challenge in this endeavor is the stability of arch dam abutments under the combined action of high in situ stress and reservoir water loads. This study addresses this issue by proposing an integrated methodology that links detailed geological characterization, in situ stress quantification, and mechanical stability analysis. Using the Guxue high-arch dam as a case study, we first established a three-dimensional geological model to identify controlling discontinuities and delineate potential sliding blocks. A finite difference model was then developed to simulate the in situ geo-stress field and operational water pressures. Through stress tensor transformation, the stress state on potential slip surfaces was accurately determined, and safety factors were calculated based on the Mohr–Coulomb strength criterion. The results show that the critical left and right abutment rock blocks exhibit safety factors of 1.30 and 1.24, respectively, meeting design specifications while indicating a relatively lower safety margin on the right bank. The proposed approach, grounded in precise stress analysis, provides a reliable framework for assessing abutment stability under complex loading conditions, offering practical support for the safety evaluation and targeted reinforcement of high-arch dam projects in similar geological settings. Full article

Standing tile inlets are commonly used to drain unwanted surface water from croplands but can exacerbate pollution by facilitating the transport of nutrients to waterways. Blind inlets have increasingly been viewed as a beneficial alternative to standing inlets since they control erosion and capture particulate nutrients. However, conventional blind inlets do little to limit dissolved nutrient transport, and modified blind inlet (MBI) designs have been proposed that incorporate woodchips—a medium that facilitates denitrification. While initial investigations have highlighted MBIs’ remediation potential, their ability to meet prescribed drainage standards has not been well-documented. In this study, we designed and installed MBIs composed of pea gravel and woodchips in two eastern Iowa fields under row crop cultivation. Flow and nitrate were continuously monitored using in situ equipment directly downstream of the MBIs (February 2023–June 2025). Observed flows were very ephemeral, consisting of ~25 distinct events at both sites, with no flow recorded in between. During several wet weather events, flow rates exceeded the MBIs’ design requirements, confirming their sufficient drainage capacity to prevent in-field ponding. Nitrate concentrations varied considerably, with long-term averages of 11.6 and 19.1 mg/L and overall loadings of 4.94 and 7.10 kg during our 28-month study. We also measured phosphate and sulfate during select wet weather events, and discrepancies in concentrations between inlets and outlets suggested that groundwater was often present alongside surficial drainage in our monitoring setup. We believe our results argue for increased adoption of MBIs in conservation and further quantification of their remediation capabilities. Full article

While understanding the drivers of river water quality is crucial, the dependence on ground observations hinders the accurate quantification of driver thresholds, as well as the scale-dependent effects of buffer zones. By transcending the limitations of ground observations, satellite remote sensing provides the spatially continuous data required to define effective buffer zones and determine the threshold intervals for natural and anthropogenic drivers, effectively promoting sustainable watershed management. Herein, we determined the total nitrogen (TN), total phosphorus (TP), permanganate index (COD_Mn), and turbidity in the Minjiang River of Fujian Province by synergizing Sentinel-2 imagery and in situ data (2021–2024). Subsequently, we further employed generalized additive models (GAMs) considering scale-dependent (50 m to 20 km) characteristics to screen and evaluate the natural–anthropogenic factors influencing the water quality indicators. The GAMs revealed that TN exhibited multiphasic responses to forest cover and water area, characterized by alternating positive and negative effects across their range. TP was found to be predominantly driven by agricultural and urban land use, showing clear scale–threshold effects. This study provides an integrated framework that moves beyond retrieval to quantitatively assess the impact of multi-scale natural–anthropogenic factors, offering actionable insights for precise watershed zoning and science-based management for the sustainable development of river systems. Full article

Carbon fluxes from the atmosphere to the ocean and from the ocean surface to the deep ocean are a key pathway in the long-term sequestration of anthropogenic CO₂. Particulate Organic Carbon (POC), which comprises living plankton, detritus and other microscopic organisms, is a very dynamic carbon pool in surface waters, so an ability to assess POC reliably from satellites and autonomous profilers is fundamental to the quantification of the reservoirs and fluxes of carbon within the ocean, and to assess their response to climate change. In situ records from sample filtration during dedicated hydrographic surveys are limited both in terms of spatial coverage and time, so reliable algorithms are required that make use of readily available autonomously collected data that provide much better spatial and temporal coverage. In this paper, algorithms that use ocean colour data from satellites to estimate POC are re-assessed, and then the satellite-derived products are compared with near-surface in situ observations from biogeochemical (BGC) Argo profilers. The satellites and in situ BGC-Argo records match each other to within 30%, but a regional bias persists that may be related to the BGC-Argo fluorometers overestimating the chlorophyll concentration in the Southern Ocean. A simple coarse-resolution regional correction to the observed chlorophyll-a concentration and backscatter coefficient, plus the removal of clear outliers, improves the agreement to approximately 15%. The association of POC with the surface chlorophyll value is so strong that an algorithm based on chlorophyll-a alone provides an almost equally good estimate of POC compared with more complex algorithms that incorporate additional bio-optical variables such as the backscattering coefficient. Full article

Chemical monitoring of pollutants and hazardous materials in water supply systems traditionally depends on centralized laboratories, advanced instrumentation, and trained personnel, limiting accessibility and preventing real-time, on-site analysis. This work presents an alternative cost-effective, field-deployable approach that uses genetically engineered bioluminescent bioreporters, encapsulated in self-sufficient alginate capsules and integrated with an optoelectronic detection circuit, to detect and quantify target materials in water. We have developed a scalable single-channel prototype featuring four sensing tracks—two for sample measurement, one for clean water, and one for a standard reference solution. The latter employs the standard ratio (SR) method to ensure robust quantification, compensating for batch variability and environmental effects. System characterization showed high uniformity across tracks. Validation with nalidixic acid (NA) demonstrated reliable quantitative performance, with a blind test estimation of 5.6 mg/L for a true concentration of 5 mg/L, well within the calibration error range. Additional sensitivity testing confirmed detection of mitomycin C (MMC) at concentrations as low as 50 µg/L. Overall, the results highlight the potential of bacterial chemical sensing as a practical and scalable tool for real-time, in situ water quality monitoring networks. Full article

The further development of scalable fabrication for perovskite solar cells has been considerably constrained by strong process variability and the lack of a reliable real-time predictive mechanism during the thin-film formation process. Existing machine learning-based methods are incapable of capturing the inherent multi-stage kinetic characteristics and uncertainties of the perovskite crystallization process, as they rely on deterministic point prediction models and flatten time-series signals into static features, which necessitates more advanced modeling strategies. To address these challenges, an in situ process monitoring and predictive modeling framework based on a lightweight probabilistic Transformer is proposed for the scalable preparation of perovskite thin films. The strategically designed inputs, consisting of time-resolved photoluminescence (PL) and diffuse reflectance imaging signals acquired during the vacuum quenching process, enable the model to directly learn the conditional probability distribution of the final device performance metrics. Rather than producing a single predicted value, this method enables the explicit quantification of prediction uncertainty, providing statistical support for uncertainty-aware process assessment. Leveraging its advantages over feed-forward neural networks and traditional tree-based machine learning methods, the proposed Transformer architecture effectively captures the staged and non-stationary kinetic features of thin-film formation. Consequently, it exhibits higher robustness and superior uncertainty calibration capability during the early-stage prediction phase. The results demonstrate that the probabilistic Transformer-based modeling paradigm provides a viable pathway toward uncertainty-aware, data-driven process evaluation in perovskite manufacturing. This framework extends its application beyond perovskite photovoltaic device fabrication, providing a generalizable modeling strategy for real-time predictive assessment in the preparation of other complex materials governed by irreversible stochastic dynamics. Full article