Search Results (1,200)

The pressure-gradient Micro-Electro-Mechanical Systems (MEMS) vector hydrophone is a novel type of sensor capable of simultaneously acquiring both scalar and vectorial information within an acoustic field. Conventional azimuth estimation methods struggle to achieve high-resolution localization using a single pressure-gradient MEMS vector hydrophone. In practical marine environments, the multiple signal classification (MUSIC) algorithm is hampered by significant resolution performance loss. Similarly, the complex acoustic intensity (CAI) method is constrained by a high-resolution threshold for multiple targets, often resulting in inaccurate azimuth estimates. Therefore, a cross-spectral model between the acoustic pressure and the particle velocity for the pressure-gradient MEMS vector hydrophone was established. Integrated with an improved particle swarm optimization (IPSO) algorithm, a high-resolution azimuth estimation method utilizing this hydrophone is proposed. Furthermore, the corresponding Cramér-Rao Bound is derived. Simulation results demonstrate that the proposed algorithm accurately resolves two targets separated by only 5° at a low signal-to-noise ratio (SNR) of 5 dB, boasting a root mean square error of approximately 0.35° and a 100% success rate. Compared with the CAI method and the MUSIC algorithm, the proposed method achieves a lower resolution threshold and higher estimation accuracy, alongside low computational complexity that enables efficient real-time processing. Field tests in an actual seawater environment validate the algorithm’s high-resolution performance as predicted by simulations, thus confirming its practical efficacy. The proposed algorithm addresses key limitations in underwater detection by enhancing system robustness and offering high-resolution azimuth estimation. This capability holds promise for extending to multi-target scenarios in complex marine settings. Full article

Wildfires are part of terrestrial ecosystem processes; however, their frequency and intensity have recently increased due to both natural and anthropogenic factors. Geospatial data are essential for analyzing land cover changes at high spatial resolution, making the development of tools that use this information to detect burned areas particularly important, especially in regions of high ecological value. This study aimed to detect burned areas within the Iztaccíhuatl–Popocatépetl Protected Natural Area in central Mexico using a logistic regression model based on spectral variables such as NDVI, RBRc, and SWIR2 derived from Sentinel-2 imagery. The agreement between observed and classified data yielded Kappa coefficients and overall accuracy values of 0.79. Model performance varied with probability threshold: low thresholds achieved higher metrics, while high thresholds produced a more conservative delineation that was spatially more coherent with the reference polygons, prioritizing pixels with higher probability of being affected and generating maps more consistent with actual burned areas. Overall, the model performed well in detecting burned areas, providing a useful tool for fire monitoring. However, it is recommended to conduct analyses by vegetation type to increase model accuracy, as phenological variability associated with vegetation types can influence spectral responses and reduce precision. Full article

Existing docking methods for mobile robots rely on a LiDAR sensor or image processing using a camera. Although both demonstrate excellent performance in terms of sensing distance and spatial resolution, they are sensitive to environmental effects, such as illumination and occlusion, and are expensive. Some environments or conditions require low-power, low-cost novel docking solutions that are less sensitive to the environment. In this study, we propose a guidance and navigation solution for a mobile robot to dock into a docking station using the values of the angle of arrival and received signal strength indicator between the mobile robot and the docking station, measured via wireless communication based on Bluetooth low energy (BLE). This proposed algorithm is a LiDAR- and camera-free docking solution. The proposed algorithm is used to run an actual mobile robot and BLE transceiver hardware, and the obtained result is significantly close to the ground truth for docking. Full article

In the preliminary design stage of wind farms, the theoretical energy output must be adjusted by multiple reduction factors to estimate the actual grid-connected power. As renewable energy becomes increasingly integrated into electricity markets, the conventional approach using static, averaged reduction coefficients for annual yield estimation can no longer meet the market’s demand for high-resolution power time series. Addressing this gap, the novelty of this paper lies in shifting the focus from total annual estimation to hourly-level dynamic allocation. This paper proposes a time-series reduction coefficient evaluation method based on the time-varying entropy weight method (TV-EWM). Under the assumption that the total annual reduction quantity adheres to standard design specifications, this method utilizes long-term wind measurement data, integrates unique ultra-high-altitude wind resource characteristics, and constructs a scenario-based indicator system. By quantifying the coupling relationships between key meteorological variables and incorporating a dynamic weighting mechanism, the proposed approach achieves hourly refined reduction estimation for theoretical power output. Comparative analysis was conducted against the traditional static average reduction method. Results indicate that, compared to the traditional average reduction method, the TV-EWM approach significantly enhances the model’s ability to capture seasonal variability, increasing the coefficient of determination (R²) by 4.19% to 0.7061. Furthermore, it demonstrates higher stability in error control, reducing the Normalized Root Mean Square Error (NRMSE) by 4.51% to 15.45%. The TV-EWM more accurately captures the temporal evolution and coupling effects between meteorological elements and curtailed generation under various reduction scenarios, retains full-load operational features, and enhances physical interpretability and time responsiveness, providing a new analytical framework for market-oriented power generation assessment. Full article

Tropical forests are the most plant-diverse ecosystems on Earth, characterized by extremely high species richness and playing essential roles in ecosystem stability, carbon sequestration, and hydrological regulation. Although remote sensing has been widely applied to monitoring tropical forest plant diversity in recent decades, a systematic understanding of its actual monitoring capacity remains limited. Based on a bibliometric analysis of 15,878 publications from 1960 to 2025, this study draws several key conclusions: (1) Global research is highly unevenly distributed, with most studies concentrated in China’s tropical monsoon forests, Brazil’s Amazon rainforest, Costa Rica’s tropical rainforests, and Mexico’s tropical dry forests, while many other regions remain understudied; (2) The Sentinel-2 and Landsat series are the most widely used satellite sensors, and indirect indicators are applied more frequently than direct spectral metrics in monitoring models. Hyperspectral data, Light Detection and Ranging (LiDAR), and nonlinear models generally achieve higher accuracy than multispectral data, Synthetic Aperture Radar (SAR), and linear models; (3) Sampling scales range from 64 m² to 1600 ha, with the highest accuracy achieved when plot size is within 400 m² < Area ≤ 2500 m², and spatial resolutions below 10 m perform best. Based on these findings, we propose four priority directions for future research: (1) Quantifying spectral indicators and models; (2) Assessing the influence of canopy structure on biodiversity remote sensing accuracy; (3) Strengthening the application of high-resolution data and reducing intraspecific spectral variability; and (4) Enhancing functional diversity monitoring and advancing research on the relationship between biodiversity and ecosystem functioning. Full article

High-resolution three-dimensional temperature fields are essential for studying flame combustion, and tunable diode laser absorption tomography (TDLAT) is an effective method for diagnosing flame combustion conditions. In actual combustion measurements, the reliance of TDLAT on line-of-sight (LOS) measurements leads to limited data and reduced dimensionality in analyzing combustion fields. This study proposes a method using area-array sensor-coupled absorption spectroscopy to measure the three-dimensional temperature field of flame accurately, aiming for enhanced combustion diagnosis. The laser beam is configured into a cone shape, and after traversing the combustion field under examination, the area-array sensor receives a projection signal. This signal is then used to reconstruct a high-resolution, multidimensional temperature field. We confirmed the accuracy and robustness of the algorithm through numerical simulations and compared these with experimental results from the TDLAT setup. Our TDLAT detection system demonstrates high precision and effectively measures temperature fields in complex flame imaging scenarios. Full article

The surface tension of a liquid droplet can be determined by fitting its actual profiles using the Young–Laplace equation, effectively reducing the measurement of surface tension to an accurate determination of the droplet’s profiles. Spectral confocal sensors are high-precision, interference-resistant, non-contact measurement systems for droplet surface profiling, employing a light source together with a dispersive objective lens and a spectrometer to acquire depth-dependent spectral information. The accuracy and stability of surface tension measurements can be effectively enhanced by spectral confocal sensors measuring the droplet surface profile. Although existing spectral confocal sensors have significantly improved measurement range and accuracy, their angular measurement performance remains limited, and deviations may arise at droplet edges with large inclinations or pronounced surface profile variations. This study presents the optical design of a large-angle spectral confocal sensor. By theoretically analyzing the conditions for generating linear axial dispersion in the dispersive objective lens, a front-end dispersive objective lens was designed by combining positive and negative lenses. Based on a Czerny–Turner (C-T) configuration, the back-end spectrometer was designed under the astigmatism-free condition, taking into account both central and edge wavelength effects. Zemax was employed for simulation optimization and tolerance analysis of each optical module. The results show that the designed system achieves an axial dispersion of 1.5 mm over the 430–700 nm wavelength range, with a maximum allowable object angle of ±40° and a theoretical resolution of 3 μm. The proposed spectral confocal sensor maintains high measurement accuracy over a wide angular range, facilitating precise measurement of droplet surface tension at large inclination angles. Full article

The causes of agricultural drought are complex, and its actual occurrence process is often characterized by rapid onset in terms of time and small scale in terms of space. Monitoring agricultural drought using satellite remote sensing with low spatial resolution makes it difficult to accurately capture the details of small-scale drought events. High-resolution satellite remote sensing has relatively long revisit cycles, making it difficult to capture the rapid evolution of drought conditions. Furthermore, the occurrence of agricultural drought is linked to multiple factors including precipitation, evapotranspiration, soil properties, and crop physiological characteristics. Consequently, relying on a single variable or indicator is insufficient for multidimensional monitoring of agricultural drought. This study takes Hebi City, Henan Province as the research area. It uses Sentinel-1 satellite data (HV, VV), Sentinel-2 data (NDVI, B2, B11), elevation, slope, aspect, and GPM precipitation data from 2019 to 2024 as independent variables. Three machine learning algorithms—Random Forest (RF), Random Forest-Recursive Feature Elimination (RF-RFE), and eXtreme Gradient Boosting (XGBoost)—were employed to construct a multi-dimensional agricultural drought monitoring model at the field scale. Additionally, the study verified the sensitivity of different environmental variables to agricultural drought monitoring and analyzed the accuracy performance of different machine learning algorithms in agricultural drought monitoring. The research results indicate that under the condition of full-factor input, all three models exhibit the optimal predictive performance. Among them, the XGBoost model performs the best, with the smallest Relative Root Mean Square Error (RRMSE) of 0.45 and the highest Correlation Coefficient (R) of 0.79. The absence of Digital Elevation Model (DEM) data impairs the models’ ability to capture the patterns of key features, which in turn leads to a reduction in predictive accuracy. Meanwhile, there is a significant correlation between model performance and sample size. Ultimately, the constructed XGBoost model takes the lead with an accuracy of 89%, while the accuracies of Random Forest (RF) and Random Forest-Recursive Feature Elimination (RF-RFE) are 88% and 86%, respectively. Based on these three drought monitoring models, this study further monitored a drought event that occurred in Hebi City in 2023, presented the spatiotemporal distribution of agricultural drought in Hebi City, and applied the Mann–Kendall test for time series analysis, aiming to identify the abrupt change process of agricultural drought. Meanwhile, on the basis of the research results, the feasibility of verifying drought occurrence using irrigation signals was discussed, and the potential reasons for the significantly lower drought occurrence probability in the western mountainous areas of the study region were analyzed. Full article

While the international influence of the Programme for International Student Assessment (PISA) on education policy debates is well recognized, the degree to which PISA findings drive actual policy reforms and classroom practices remain debated. Using PISA as a case, this article examines how educational research is translated into policy responses and practices in German federalism, focusing specifically on immigrant students—a key group within German education reform discourse. It analyzes the reflection of PISA findings from the 2000, 2018, and 2022 assessments on immigrant student performance in the resolutions of the Standing Conference of Ministers of Education and Cultural Affairs, the process of implementation by the federal states (Länder), and the effect on school-level practice. Framed by research knowledge mobilization theory, the article investigates the relationships among research production, mediation, and usage, clarifying the interplay between educational research, policy, and practice in Germany’s federal system. Historical analysis exposes consistent gaps between research-derived recommendations and binding, actionable change at both policy and practice levels, often due to challenges in developing evidence-based and consistently applied policy measures across the Länder. The article concludes with practical recommendations for improving the impact of interdisciplinary, policy-oriented research on policy and practice, considering the complexities of Germany’s federal governance. Full article

Evapotranspiration (ET) refers to the total water vapor flux transported by vegetation and surface soil to the atmosphere. It is an important component of water and heat regulation, and has an impact on plant productivity and water resource management. As a water-shortage region, the Mongolian Plateau is characterized by drought and an uneven distribution of rainwater resources. Understanding the spatiotemporal distribution characteristics of ET on the Mongolian Plateau is important for water resource regulation for climate change adaption and regional sustainable development. This study calculated the spatiotemporal distribution characteristics of the actual ET in the Mongolian Plateau based on the SFE-NP model and generated a surface ET dataset with a spatial resolution of 1 km and monthly temporal resolution from 2001 to 2020. Theil-Sen median and Mann–Kendall trend models were used to analyze the temporal and spatial distribution characteristics of the actual ET over the Mongolian Plateau. This dataset has been validated for accuracy against the commonly used authoritative ET datasets ERA5_Land and MOD16A2, demonstrating high precision and accuracy. This dataset can provide data support for research and applications such as surface water resource allocation and drought detection in the Mongolian Plateau. Full article

Population activity drives urban development, and high-spatiotemporal-resolution population distribution provides critical insights for refined urban management and social services. However, mixed population activity patterns and spatial heterogeneity make simultaneous high-temporal- and -spatial-resolution estimation difficult. Therefore, we propose the High-Spatiotemporal-Resolution Population Distribution Estimation Based on the Strong and Weak Perception of Population Activity Patterns (SWPP-HSTPE) method to estimate hourly population distribution at the building scale. During the weak-perception period, we construct a Modified Dual-Environment Feature Fusion model using building features within small-scale grids to estimate stable nighttime populations. During the strong-perception period, we incorporate activity characteristics of weakly perceived activity populations (minors and older people). Then, the Self-Organizing Map algorithm and spatial environment function purity are used to decompose mixed patterns of strongly perceived activity populations (young and middle-aged) and to extract fundamental patterns, combined with building types, for population calculation. Results demonstrated that the SWPP-HSTPE method achieved high-spatiotemporal-resolution population distribution estimation. During the weak-perception period, the estimated population correlated strongly with actual household counts (r = 0.72) and outperformed WorldPop and GHS-POP by 0.157 and 0.133, respectively. During the strong-perception period, the SWPP-HSTPE model achieves a correlation with hourly population estimates that is approximately 4% higher than that of the baseline model, while reducing estimation errors by nearly 2%. By jointly accounting for temporal dynamics and population activity patterns, this study provides valuable data support and methodological insights for fine-grained urban management. Full article

The Changjiang estuary–Hangzhou Bay region is a critical zone of land–sea interaction, where Total Suspended Matter (TSM) dynamics significantly influence coastal ecology and engineering. While previous studies have examined individual factors affecting TSM variability, the synergistic effects of “tide–monsoon–current” interactions and the actual pathways of turbid plume transport remain poorly understood. Using GOCI satellite data, in situ buoy measurements, and voyage data from 2020, this study applied Data Interpolating Empirical Orthogonal Functions (DINEOFs) and comprehensive spatio-temporal analysis to reconstruct continuous high-resolution TSM fields and elucidate multi-factor controls on TSM dynamics. Based on this high-resolution dataset of TSM, we found that, during the dry season, elevated TSM concentrations are primarily driven by wind–tide resuspension and transport under the comprehensive forcing of the Jiangsu Alongshore Current (JAC), the Yellow Sea Warm Current (YSWC), and wind–tide-induced flows. Contrary to the conventional understanding, the Jiangsu-origin surface TSM can transport to the outer sea without supplementing the TSM in the Turbidity Maximum Zone (TMZ). The YSWC in autumn can cause either low C_TSM gradients or high gradients nearshore depending on whether it is carrying Korean coastal turbid water or not. During the wet season, stratification induced by the Changjiang freshwater discharge suppresses wind–tide resuspension, reducing TSM concentrations in the TMZ and the Qidong water. However, the Changjiang freshwater combined with the Taiwan Warm Current (TWC) dilutes surface TSM in Hangzhou Bay, where the two water masses meet on the 10 m isobath. These insights into factor interactions and TSM plume pathways provide a scientific basis for improved environmental monitoring and coastal management. Full article

Early spring frost is a major meteorological hazard during the Apple Flowering period. To improve frost event prediction, this study proposes a hybrid 1D-CNN-BiLSTM-Attention model, with its core novelty lying in the integrated dual attention mechanism (Self-attention and Cross-variable Attention) and hybrid architecture. The 1D-CNN extracts extreme points and mutation features from meteorological factors, while BiLSTM captures long-term patterns such as cold wave accumulation. The dual attention mechanisms dynamically weight key frost precursors (low temperature, high humidity, calm wind), aiming to enhance the model’s focus on critical information. Using 1997–2016 data from Luochuan (four variables: Ground Surface Temperature (GST), Air Temperature (TEM), Wind Speed (WS), Relative Humidity (RH)), a segmented interpolation method increased temporal resolution to 4 h, and an adaptive Savitzky–Golay Filter reduced noise. For frost classification, Recall, Precision, and F1-score were higher than those of baseline models, and the model showed good agreement with the actual frost events in Luochuan on 6, 9, and 10 April 2013. The 4 h lead time could provide growers with timely guidance to take mitigation measures, alleviating potential losses. This research may offer modest technical references for frost prediction during the Apple Flowering period in similar regions. Full article

FY-4B/GIIRS (Geostationary Interferometric Infrared Sounder) is a new-generation infrared hyperspectral atmospheric vertical sounder onboard a Chinese geostationary meteorological satellite. Its observations with high spatial and temporal resolution play an important role in high-impact weather forecasts. The GIIRS data assimilation module is developed in the GSI (Gridpoint Statistical Interpolation) assimilation system. Super Typhoon Doksuri in 2023 (No. 5) is taken as an example based on this module in this paper. Firstly, the sensitivity of analysis fields to five data thinning schemes at four daily assimilation times from 22 to 28 July 2023 is analyzed: the wavelet transform modulus maxima (WTMM) scheme, the grid-distance schemes of 30 km, 60 km, and 120 km in the GSI assimilation system, and a center field of view (FOV) scheme. Taking the ERA5 reanalysis fields as true, it is found that the mean error of temperature and humidity analysis for the WTMM scheme is the smallest, followed by the 120 km thinning scheme. Subsequently, a 72 h cycling assimilation and forecast experiments are conducted for the WTMM and 120 km thinning schemes. It is found that the root mean square error (RMSE) profiles of temperature and humidity forecast fields with no thinning scheme are the largest at all pressure levels and forecast times. The temperature forecast error decreases after data thinning at altitudes below 300 hPa. Since the WTMM scheme has assimilated more observations than the 120 km scheme, the accuracy of its temperature and humidity forecast fields gradually increases with the forecast time. In terms of typhoon track and intensity forecast, the typhoon intensities are underestimated before landfall and overestimated after landfall for all thinning schemes. As the forecast time increases, the advantage of the WTMM is increasingly evident, with both the forecast intensity and track being closest to the actual observations. Similarly, the forecasted 24 h accumulated precipitation over land is overestimated after typhoon landfall compared with the IMERG Final precipitation products. The location of precipitation simulated by no thinning scheme is more westward overall. The forecast accuracy of the locations and intensities of severe precipitation cores and the typhoon’s outer spiral rain bands over the South China Sea has been improved after thinning. The Equitable Threat Scores (ETSs) of the WTMM thinning scheme are the highest for most precipitation intensity thresholds. Full article

Industrial air emissions are major contributors to human exposure to toxic pollutants, posing significant health risks. Life cycle assessment (LCA) is increasingly used to quantify human toxicity impacts from industrial processes. Conventional LCA often overlooks spatial and temporal variability, limiting its ability to capture actual inhaled doses and exposure-driven impacts. To address this, we developed a site-dependent dynamic LCA (SdDLCA) framework that integrates conventional LCA with Enhanced Structural Path Analysis (ESPA) and atmospheric dispersion modeling. Applied to the production of double-glazed PVC windows for a residential project, the framework generates high-resolution, site-specific emission inventories for three key pollutants: nitrogen oxides (NO_x), sulfur dioxide (SO₂), and fine particulate matter (PM_2.5). Local concentration fields are compared with World Health Organization (WHO) air quality thresholds to identify hotspots and periods of elevated exposure. By coupling these fields with the ReCiPe 2016 endpoint methodology and localized demographic and meteorological data, SdDLCA quantifies human health impacts in Disability-Adjusted Life Years (DALYs), providing a direct measure of inhalation toxicity. This approach enhances LCA’s ability to capture exposure-driven effects, identifies populations at greatest risk, and offers a robust, evidence-based tool to guide industrial planning and operations that minimize health hazards from air emissions. Full article