Search Results (12,922)

Complex climate change exacerbates variability in bottom oxygen availability, posing serious threats to aquatic ecosystems. This study investigates the interrelationships among meteorological, thermocline, oxycline variables, and the Kjeldahl nitrogen ratio in Lake Długie Wigierskie, Poland, using long-term monitoring data (2008–2022). Results show a decline in surface and bottom %saturation, but an increase in dissolved oxygen (DO) concentrations. Air temperature and Secchi depth primarily influenced surface oxygen dynamics, while wind speed drove bottom oxygen variability. Thermocline depth and thickness positively correlated with oxycline depth and hypoxic thickness, revealing that stable stratification restricts vertical mixing and shapes oxygen distribution. Air temperature significantly affected Schmidt Stability (SS), with warmer periods promoting stronger stratification, greater hypoxic thickness, and lower hypolimnetic oxygen minimum (HOM). Interestingly, DO levels and their variability showed significant correlation with the Kjeldahl nitrogen ratio (TKN/TN), suggesting that oxygen fluctuations may influence nitrogen cycling more strongly than average DO concentrations. These findings imply that warming may worsen bottom hypoxia by elevating respiration rates, thereby altering organic nitrogen mineralization. Overall, the study highlights the need for effective management strategies to alleviate hypoxia and protect water quality in deep lakes under climate change. Full article

Gas sensors based on metal oxide semiconductors (MOS) have attracted significant attention in monitoring of methane emission and leakage monitoring due to their high sensitivity, fast response time, simple structure and low cost. However, the high power consumption caused by long-term high-temperature operation of MOS sensors restricts their application in mobile and portable devices. In this study, MOF-derived Co₃O₄ dodecahedrons for low-concentration methane detection at room temperature was prepared using Zeolitic Imidazolate Framework-67 (ZIF-67) as a template and with various calcination temperatures. Among them, the Co₃O₄-350 calcined at 350 °C exhibited the optimal CH₄ sensing performance at room temperature, with a response of R_g/R_a = 1.53 to 2000 ppm CH₄. This enhanced gas sensing performance is attributed to the highest Co³⁺ proportions and the largest specific surface area in Co₃O₄-350 nanomaterials, which provided more active sites for gas adsorption and reaction. To address the challenge of slow response speed and irrecoverability during CH₄ detection at room temperature, the Co₃O₄ nanomaterials were printed onto a micro-heater plate (MHP) to form a MEMS gas sensor. By introducing a pulse heating mode to the MEMS sensor, the response and recovery time were significantly reduced to 26 s and 21 s, respectively. This enhancement improves both the efficiency and reliability of the MEMS gas sensor for early-stage detection of CH₄ leaks in various industrial applications. Full article

Rapid urbanization is reshaping thermal environments worldwide, with the strongest impacts occurring at the interface between urban and non-urban areas. Impervious surfaces, as key indicators of urban expansion, are critical for monitoring urban growth and assessing surface urban heat island (SUHI) effects. Land use and land cover change (LULCC) provides an essential link between urban dynamics and their environmental and societal consequences. Here, we integrated the U.S. Geological Survey (USGS) Climate Global Issues (CGI) Land Cover Product with Landsat thermal time-series to investigate SUHI evolution in two contrasting metropolitan regions: Wuhan, China, and Brasília, Brazil. Using data spanning 1986–2023, we analyzed the relationships between land cover, Landsat-based land surface temperature (LST), and SUHI intensity, and identified persistent thermal hotspots. Results demonstrate that the land cover data utilized increases the accuracy of impervious surface mapping along urban–rural gradients. Average SUHI intensities were 3.4 °C in Wuhan and 3.3 °C in Brasília, with statistically significant warming trends of 0.04 °C/year and 0.01 °C/year, respectively. Maximum temperature proved to be a robust indicator of SUHI intensification, capturing long-term upward trends. Our findings highlight the important role of urban land cover dynamics in shaping temporal SUHI variability and hotspot emergence. This prototype framework demonstrates the scientific and policy value of combining long-term land cover monitoring information with satellite thermal monitoring to quantify and track SUHI at city scale, supporting sustainable urban planning and climate adaptation strategies. Full article

Traditional air temperature-based climate heat indices can be of high uncertainty in regions where ground observations are scarce. In this study, we calculate the Summer Days and Tropical Nights heat indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) for Switzerland and Europe, based on long-term Land Surface Temperature (LST) satellite climate data from EUMETSAT’s Satellite Application Facility on Climate Monitoring (CM SAF). We define relative indices that account for the intermittency of clear-sky LST satellite observations. Furthermore, we propose a novel “Extremely Hot Days index”, tailored to satellite LST data. We find that these LST-based indices are highly correlated with station-based air temperature indices in Switzerland, with coefficients of determination $R^2$ of 0.86, 0.84, and 0.81. Results show a strong increase in LST-based heat indices of up to 12 days/decade since 1991 in parts of Europe, including the Po Valley and the Mediterranean coast. These new LST heat indices can capture changes in heatwave patterns and trends for clear-sky conditions in Europe with unprecedented spatial resolution. They complement traditional air temperature heat indices and enable future climate change studies, also in regions with sparse ground observations. Full article

Surface soil moisture (SSM) serves as a critical indicator for regional water cycles, agricultural management, and drought monitoring. However, existing the SMAP data suffers from limited spatial resolution, making it challenging to meet the demands of large-scale, high-resolution applications. Taking Henan Province, located in east-central China with a continental monsoon climate and marked seasonal variability, as the study area, this research integrates multi-source data to develop a seasonal modeling strategy. Based on stacking ensemble learning, the SSM downscaling inversion model (SOBLE-Top5) is constructed. SHAP value attribution analysis is employed to reveal the primary drivers of seasonal dynamics. The results indicate: (1) The SSM exhibits distinct seasonal characteristics. Compared to the all-season modeling, the RMSE and R² metrics significantly improve during spring and summer. The winter ET and RF models show an approximately 9–14% higher R² and a 47–50% lower RMSE. (2) The SOBLE-Top5 strategy achieved up to a 4.65% higher R² and a 21.22% lower RMSE compared to the optimal single base model. (3) Spatial variations in the SSM characteristics reveal stable performance during the winter. The spring saw slight SSM declines in the northern regions due to rising temperatures. The study area reached its annual low (<0.08 m³/m³) in May–June. Driven by flood season precipitation, July–August witnessed local increases exceeding 52%. The autumn exhibited a stable-then-rising trend with pronounced north–south gradient characteristics. (4) The SHAP analysis indicates that the winter SSM is primarily controlled by bulk density and clay content. The spring SSM is most influenced by LST, followed by bulk density. The summer and the autumn SSM are synergistically driven by multiple factors including elevation, temperature, and precipitation, with the summer precipitation exerting the most significant impact on instantaneous SSM variations. Full article

Acute and chronic kidney diseases remain significant challenges in regenerative medicine, with few therapies capable of reversing tissue injury or preventing progression. Bone marrow mesenchymal stem cell-derived exosomes (BM-MSC-Exos) are nanosized vesicles (30–150 nm) that have emerged as multifunctional nanotheranostic platforms, combining targeted therapeutic activity with imaging-enabled monitoring. In renal pathophysiology, BM-MSC-Exos exert anti-inflammatory, anti-fibrotic, angiogenic, and pro-regenerative effects. These actions are mediated by microRNAs, messenger RNAs, mitochondrial regulators, and bioactive proteins that modulate epithelial repair and immune responses. Recent bioengineering advances enable more precise BM-MSC-Exos design, including enrichment with synthetic RNAs or gene-editing components and membrane functionalization to enhance kidney tropism. In parallel, fluorescence, bioluminescence, and nanoparticle-based approaches support in vivo tracking. These tools allow real-time assessment of biodistribution and tubular uptake, strengthening evidence for target engagement. This review synthesizes current knowledge on BM-MSC-Exos in renal repair. We summarize contemporary strategies for cargo and surface engineering, outline imaging methodologies for in vivo tracking, and discuss how administration routes influence renal targeting. We also provide an updated overview of clinical trials evaluating exosomes as therapeutic agents or biomarkers in nephrology. Collectively, engineered BM-MSC-Exos represent a promising and increasingly sophisticated platform for precision-guided kidney therapy, supported by monitoring tools that facilitate preclinical evaluation of biodistribution and efficacy. Full article

The escalation of thermal runaway in lithium-ion batteries presents severe safety hazards that necessitate advanced monitoring protocols to ensure early warning of potential failures. Carbon dioxide (CO₂) is released during preliminary decomposition well before catastrophic failure occurs, thereby providing a strategic advantage for early-stage warning. Consequently, identifying materials with high-selective CO₂ recognition is an essential prerequisite for developing reliable sensing platforms. This study integrates Grand Canonical Monte Carlo simulations with Random Forest (RF) models to systematically screen 1470 MOFs from the CoRE-MOF 2019 database. The screening process evaluates selective CO₂ recognition under multicomponent competitive adsorption conditions involving CO₂, C₂H₄, and O₂. The performance evaluation is based on working capacity, selectivity, and the trade-off between working capacity and selectivity (TSN). The RF model achieves high predictive accuracy, with tested R² exceeding 0.92 on the test samples. Shapley Additive Explanations (SHAP) interpretability analysis identifies Q⁰_st(CO₂), Q⁰_st(C₂H₄), WEPA, K_H(C₂H₄), and ETR as key performance drivers. The results indicate that CO₂ selectivity is constrained by the binding strength of competing C₂H₄. Optimal materials tend to have hard Lewis acid centers and polar inorganic clusters to minimize non-specific π-interactions with interfering species. Top-performing MOFs require balanced structural features, concentrating in moderate surface areas (965–1975 m²/g), narrow pore windows (PLD ≈ 4–7 Å, LCD ≈ 5.5–9.6 Å), high void fractions above 0.6, and low densities below 1.3 g/cm³. AJOTEY emerges as the optimal candidate with a TSN of 6.43 mol/kg, combining substantial working capacity (4.57 mol/kg) with strong selectivity (25.52). These results will accelerate the discovery of sensing materials and provide a practical pathway for MOF-based CO₂ sensor development to enhance lithium-ion battery safety. Full article

Leveraging the complementary advantages of InSAR and GNSS, this study proposes a refined method for monitoring mining-induced surface subsidence by integrating both technologies. The method begins with calculating the time-series cumulative subsidence basin from InSAR. Subsequently, a constraint condition is established to identify large-gradient deformations, thereby distinguishing the subsidence edge from the subsidence center. For the subsidence edge with minor deformation, the InSAR results are retained. For the large-gradient subsidence center, the subsidence basin around the mining panel is reconstructed by integrating InSAR and GNSS models. Continuous surface deformation information in a geographic coordinate system is then obtained through spatial interpolation, ultimately yielding comprehensive surface subsidence results across the mining area. Taking a mining area in Shanxi Province as the study region, the feasibility and accuracy of the proposed method were validated using 35 SAR images acquired between April 2016 and September 2017, along with leveling measurement data from the mining panel. The maximum surface subsidence rate of the settlement basin obtained from the solution is −186.68 mm/year, and the maximum surface subsidence amount is 248 mm. Compared with the InSAR monitoring results, the root mean square error of the data collaborative monitoring is reduced by 96.8%, and it is reduced by 64.4% compared with the GNSS probability integral method. The results demonstrate that the proposed method can achieve subsidence results consistent with the actual situation. Its monitoring capability is significantly superior to that of using either InSAR or GNSS alone, effectively compensating for the limitations inherent in each individual technology when applied to mining subsidence monitoring. Consequently, this integrated approach provides more accurate and reliable information on surface subsidence in mining areas. Full article

Poly(vinyl butyral) (PVB) displays exceptional adhesion to glass surfaces and high transparency, serving as the dominant interlayer material in laminated glass composites. This study systematically investigates PVB particulate composites, focusing on the interactions between a plasticized PVB matrix and silicate or silica dispersions as reinforcements. PVB composites reinforced with glass flakes, glass fibers, and fumed silica at loadings of 2, 5, and 8 vol% were produced and characterized. Optical microscopy and thermogravimetric analysis were employed to evaluate filler incorporation and dispersion under melt mixing conditions representative of industrial extrusion. Transparency measurements assessed the optical clarity of the composites, while ATR-FTIR was used to identify chemical interactions between PVB and the fillers. Regarding mechanical performance, fumed silica increased tensile strength up to 29 MPa and reduced displacement at fracture by 120%, while high-aspect-ratio flakes and silane-treated fibers only significantly increased composite stiffness. Impact resistance was additionally evaluated, revealing a significant enhancement upon the addition of fibrous reinforcements, especially when silane-treated fibers were used. Fumed silica increased the thermal stability of PVB by 7 °C and reduced water uptake to approximately 4.5%, in contrast to glass flakes, which increased water absorption reaching up to 8–11%. Lastly, the processability of composites was monitored, showing a progressive decrease with increasing filler content for all reinforcements. Overall, this work provides a comprehensive assessment of PVB–silicate/silica interfacial interactions and highlights the design of PVB composites suitable for advanced applications or the upcycling of secondary recycled PVB grades. Full article

Wool fibers undergo significant structural changes during industrial stretching, which directly impact their mechanical properties and textile performance, making monitoring of the stretching process essential for optimizing wool products. In this study, we demonstrate the effective use of polarized second harmonic generation (P-SHG) imaging for monitoring the wool fiber stretching process. P-SHG is highly sensitive to non-centrosymmetric structures, enabling clear observation of changes in α-keratin alignment and the reconstruction of cortical interfaces during stretching. Quantitative P-SHG analysis revealed a significant decrease in the effective pitch angle (θ_e) from 54° ± 1° to 33° ± 3° after stretching, confirming the dipole orientation changes in keratin molecules. These findings were further validated through additional characterization techniques, including scanning electron microscopy (SEM), polarizing optical microscopy (POM), X-ray diffraction (XRD), and Raman spectroscopy (RS). The results show that the industrial stretching process of wool alters the morphology at the surface scale, enhances the alignment of macroscopic fibers, and induces a transition from α-helix to β-sheet. Our technique is simple, effective, and capable of in situ monitoring of the structural changes in wool fibers, making it highly promising for applications in the wool industry. Full article

Electrophysiological studies using brain organoids provide valuable insights into neurological disorders and offer promising opportunities for therapeutic development. Accordingly, conventional two-dimensional microelectrode arrays (MEAs) are commonly employed to record neural activity with high spatiotemporal resolution. However, their measurements are mainly limited to the basal surface of the tissue. This limitation restricts the comprehensive analysis of the complex three-dimensional (3D) neural networks formed within organoids. To bridge this gap, this review summarizes recent advances in 3D MEA technologies, with a focus on device geometries, electrode designs, and neural signal acquisition strategies ranging from noninvasive to invasive approaches. Among these advances, photolithography-based fabrication processes have enabled submicron-scale structures, improving device flexibility, spatial resolution, and signal-to-noise ratio. Furthermore, the integration of 3D MEAs with perfusion systems and shape-transformable architectures facilitates stable, long-term electrophysiological monitoring of organoids. Finally, this review discusses emerging research trends and future perspectives in 3D MEA development in organoid-based neuroscience. Full article

Investigating the long-term performance of building materials, such as drying shrinkage, moisture expansion, creep, and others, usually requires long-lasting tests with a high number of specimens. Given the initial costs, required data acquisition systems, and the time allocated, conventional sensors like LVDTs become costly for such long-term experimental studies. This article proposes an innovative cost-effective solution combining optical microscopy imaging, 3D printed sliding rulers, and Python-based artificial vision to overcome these limitations. The 3D printed rulers establish a local physical reference frame, while the artificial vision system uses contour detection and point tracking of optical targets to quantify displacements. Unlike continuous monitoring systems, the proposed solution utilises a discontinuous point-tracking approach, allowing a single USB microscope to monitor an unlimited number of specimens while maintaining the possibility for moisture exchange between the material surface and the environment. The system was metrologically validated against a laser interferometer, achieving an expanded instrumental uncertainty of 0.0042 mm (4.2 µm), determined through strict calibration. These results demonstrate that the proposed solution delivers accuracy comparable to conventional sensors but with significantly higher scalability and lower cost, making it highly suitable for extensive long-term experimental programmes. Full article

Methane is the second most important contributor to global warming, and monitoring super-emitters from space is critical for climate mitigation. Despite the advancements in hyperspectral remote sensing, comparing methane observations across diverse imaging spectrometers remains a challenging task. Different retrieval algorithms, plume segmentation techniques and uncertainty treatments make it very hard to perform fair comparisons between different products. To overcome these difficulties, this study presents HyGAS (Hyperspectral Gas Analysis Suite), a unified, open-source framework for sensor-agnostic methane retrieval and flux estimation. Starting from the established clutter-matched-filter (CMF) formalism and a physical calibration in concentration–path-length units (ppm·m), we propagate both instrument noise and surface-driven background variability consistently from methane enhancement to Integrated Mass Enhancement (IME) and flux. The framework further includes a spectrally matched background-selection strategy, scale-aware segmentation with fixed physical criteria across resolutions, and emission-rate estimation via an IME–U_eff$U_{eff}$ approach informed by Large Eddy Simulation (LES). We demonstrate the framework on near-simultaneous observations of landfills and gas infrastructure in Argentina, Turkmenistan, and Pakistan, spanning Level-1 radiance workflows (PRISMA, EnMAP, Tanager-1) and Level-2 methane products (EMIT, GHGSat). The standardised chain enables systematic inter-comparison of methane enhancement products and reduces methodological bias, supporting robust multi-mission assessment and future global monitoring. Full article

Landslide susceptibility mapping (LSM) is a fundamental approach for identifying and predicting areas prone to slope failure. However, most conventional LSM methods are based on time-invariant conditioning factors or long-term-averaged predictors and seldom incorporate slope-kinematic information from deformation observations, thereby limiting their ability to capture evolving slope instability. Moreover, the black-box nature of many models limits interpretability and confidence in their predictions. In this study, we integrate small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) with interpretable machine learning (ML) methods to develop a dynamic LSM framework that improves the accuracy and reliability of susceptibility assessment. First, static LSM was performed using ML algorithms, and SHapley Additive exPlanations (SHAP) was used to quantify and visualize feature importance. Subsequently, SBAS-InSAR was applied to retrieve surface deformation rates. Finally, a dynamic LSM matrix was constructed to integrate InSAR-derived deformation with static susceptibility classes, producing time-varying landslide susceptibility maps. Application of the framework in the Baihetan Reservoir area, Southwest China, demonstrates its practical value. During the static LSM phase, the extreme gradient boosting (XGBoost) model achieved strong predictive performance (the area under the receiver operating characteristic curve (AUC) = 0.8864; accuracy = 0.8315; precision = 0.8947), outperforming the alternative models. SHAP analysis indicates that elevation and distance to rivers are the primary controls on landslide occurrence. Incorporating SBAS-InSAR deformation data into the dynamic LSM matrix effectively captures the spatiotemporal evolution of slope instability. Susceptibility upgrades are observed for multiple inventoried landslides, and the actively deforming Xiaomidi and Gantianba landslides are presented as representative case studies, further supported by multisource observations from satellite imagery, unmanned aerial vehicle (UAV) surveys, and ground-based global navigation satellite system (GNSS) monitoring. Consequently, the proposed dynamic LSM framework overcomes limitations of static approaches by integrating deformation information and enhancing interpretability through explainable artificial intelligence. Full article

Laser measurement technology is widely used for deformation or pose monitoring of the dual-reflector antenna systems. However, conventional models of surface temperature variation with altitude fail to accurately characterise the temperature gradients between the main reflector and the subreflector of the large-aperture antennas, due to the complex near-ground environment, the antenna’s dual-reflector structural properties, and the antenna’s own rotation changes. This temperature modelling discrepancy significantly influences the laser atmospheric propagation deflection characteristics, ultimately leading to a decrease in the accuracy of antenna attitude measurements. To address these issues, this paper proposes a theory of air stratification within large-aperture antennas and utilizes this theory to optimize the temperature gradient between the antenna’s dual reflectors. Secondly, a coupled heat-fluid dynamics model for the dual-reflector surfaces is established using Computational Fluid Dynamics to simulate the atmospheric stratification under different rotational positions of the antenna. Finally, the effectiveness and feasibility of the proposed theory were verified through experiments in the antenna model and the China Nanshan 25 m non-rotatable antenna. This research provides an original theoretical and practical basis for precision environmental modelling in antenna measurements, offering prior assurance for improving the accuracy of laser-based antenna attitude measurement. Full article