Effects of mycorrhizal dominance on species diversity and carbon stock in a large temperate forest region.

Climate-driven vegetation greening in Southwest China's Karst region: A multi-scale kNDVI analysis.

Enhancing collaborative perception through multi-scale contextual information integration.

Abstract We conducted current measurements over a 2-yr period, revealing bottom-trapped topographic Rossby waves (TRWs) on the offshore slope at the southern end of the Kuril Trench in the western North Pacific Ocean. These TRWs were significantly amplified in the 30–70-day period band, characterized by a wavelength of approximately 45 km and eastward (upslope) phase propagation. A ray-tracing analysis suggests that the TRWs were locally excited on the eastern outer rise and propagated energy downslope into the deep trench while decreasing in spatial scale. An analysis of satellite-based sea surface height data reveals that a likely energy source of these waves was an elliptical warm-core ring (WCR), which persisted over the southern Kuril Trench during the period of amplified TRW activity. The elliptical shape of the ring rotated clockwise; this is interpreted as a vortex Rossby wave on a circular WCR. The rotating WCR interacted with a tall seamount, generating cyclonic eddies behind the seamount, thereby amplifying the 30–70-day TRWs over the adjacent trench slope.

Predicting local COVID-19 emergences: A time-series classification approach and value of data from social media, search engines, and neighbouring regions.

Consistent and comprehensive scale aggregation network for drone-view small object detection.

Spatial versus temporal variability in surface oxygen and its metabolic consequences in temperate and subtropical shallow lakes.

Scale effect and landscape thresholds affecting water quality and dissolved organic matter.

The impact of green space environments on college students’ mental health at multiple spatial scales in Chinese universities: An empirical study based on a multilevel mediation model

The regulatory effect of environmental factors on green tide blooms in the Yellow Sea: A spatio-temporal downscaling numerical reconstruction method.

Micrometer-scale evaluation of energy deposition is important for radiation protection and therapy as well as for advancing knowledge of responses to radiation in materials and biological systems. Due to the stochastic nature of radiation interactions, there is significant variation in energy deposition in micrometer-sized targets, especially at low doses. This variability underscores the need for a framework for microdosimetry, particularly in low-dosescenarios. The goal of this work is to develop a novel system for micron-scale characterization of energy deposition and response to radiation that is applicable at low doses, using a combination of Monte Carlo (MC) simulations and experimentaltechniques. EBT3 radiochromic film samples are irradiated to absorbed doses of 0.003-0.5Gy using the 6-MV beam from a clinical linear accelerator. To quantify energy deposition, MC simulations of the experimental irradiations are conducted to evaluate specific energy deposited within micron-scale voxels in the active layer of the film. To investigate the dose response of the film, the following two methods are employed: (i) flatbed scanner measurement of changes in optical density (OD) of the film, and (ii) Raman spectroscopy (RS) to measure response intensity across doses with micron-scale resolution. Experimental film responses are compared to predictions from the microdosimetric one-hitmodel. Specific energy distributions obtained from MC simulations show large variation in energy deposition at low doses and within small targets; the "microdosimetric spread" (relative standard deviation) is significantly higher ( 10 times) at 0.003 Gy than at 0.5 Gy, and is observed to decrease with increases in dose and target size. Both RS and OD measurements exhibit a near linear dose-response relationship, reflecting the film's sensitivity across micro- and macroscopic spatial scales. Overall, the OD and RS values determined using the one-hit model with MC-obtained specific energy distributions fit well to experimental measurements, with percentage differences up to 15 and 9.8%, respectively. An initial comparison of the relative standard deviation of RS and OD measurements (corrected for offset signal) shows qualitative agreement with the trends observed for MC-determined microdosimetric spread. This study provides first results of a system that combines simulations with experimental techniques to investigate radiation response in micron-scale targets, with a focus on low-dose radiation exposure. The system shows promise in enabling future investigations of energy deposition within small volumes at low doses, where biological responses may be heterogeneous as some cells may receive high energy deposits and incur damage, while others may experience minimal or nodeposition.

Habitat fragmentation promotes spatial scale separation under resource competition.

Machine learning has revolutionized scientific modeling, providing breakthroughs in fields ranging from weather prediction to protein folding. However, its adoption in physics-based domains remains limited due to the lack of interpretability in traditional black-box models. In environmental sciences, where understanding underlying mechanisms is critical, symbolic regression offers an alternative by discovering transparent mathematical expressions that can represent physical principles. In this work, we demonstrate the application of symbolic regression to physical modeling through shoreline prediction, a critical area for understanding coastal evolution under climate change and human influence. Unlike traditional physics-based models, which rely on assumptions that may not generalize across diverse coastal environments, our approach evolves a population of interpretable models directly from global observational data. By optimizing both predictive accuracy and model complexity, we uncover region-specific formulations that reveal the dominant physical drivers of shoreline change. This methodology enables data-driven discovery while maintaining alignment with physical intuition, providing new insights into physical dynamics across multiple spatial and temporal scales.

Image dehazing is a challenging ill-posed problem in low-level computer vision tasks, requiring the restoration of high-quality, haze-free images from complex and foggy conditions. Deep learning-based dehazing methods struggle to effectively remove non-homogeneous fog distributions due to the uneven and dense nature of fog patches, making it difficult to clear real-world fog variations. A key challenge for non-homogeneous image dehazing algorithms is efficiently capturing the spatial distribution of haze in areas with varying fog densities while restoring fine image details. To address these challenges, we propose MLCANet, a multi-level composite attention-guided network for non-homogeneous image dehazing. MLCANet mitigates the impact of uneven haze areas through two main components: the Multi-level Composite Attention Generation Network (MCAGN) and the Dehazed Image Reconstruction Network (DIRN). The MCAGN integrates channel attention (CA), spatial attention (SA), and multi-scale pixel attention (MSPA) to capture haze features at different spatial scales. The DIRN, based on a decoder-encoder architecture, combines multi-scale dilated convolutions and deformable convolutions to restore fine image details more flexibly and efficiently. Extensive qualitative and quantitative experiments, along with ablation studies, demonstrate the effectiveness and feasibility of this method for non-homogeneous image dehazing.

Abstract Farming systems worldwide have exhibited climatic adaptation and improved crop outputs over the last century. Nevertheless, interannual yield fluctuations still drive volatility in food commodity prices, challenging food security. Predicting short-term yield variations from observed climatic patterns can thus provide significant benefits. This paper enhances a transparent, computational efficient and closed-form probabilistic short-term yield forecasting method introduced in our previous research. The case of wheat yield for an Italian province, where the Standardized Precipitation-Evapotranspiration Index (SPEI) was identified as a key predictor, is considered for illustrative purposes. Forecasting is defined as evaluating the conditional probability distribution of the next yield value, given the recorded SPEI in a selected month of the current year. As a main challenge is that the yield–SPEI relationship exhibits parameters varying over time, here we propose a least-squares method adapted for interpolating curves with time-varying parameters. Combined with resampling techniques, this enables robust probabilistic forecasts. Validation through Monte Carlo simulations and resampling confirmed the method’s effectiveness. The framework can be potentially tailored to different crops, regions, and spatial scales. Such predictive capacity may prove valuable in preparing stakeholders across the crop production–consumption chain, including financial markets, in managing risks linked to unexpected production outcomes.

Under the intensifying pressures of climate change and human activities, the characteristics of land-use types and landscape patterns are widely recognized to exert significant influences on river water quality. Nevertheless, in dry-hot valley basins characterized by fragile ecological conditions and frequent geological hazards, the responses of river water quality to changes in landscape characteristics under the combined effects of natural disasters and anthropogenic disturbances remain poorly understood. In the present study, the Xiaojiang River Basin, a typical dry-hot valley basin subjected to intensive anthropogenic activities and frequent geological hazards, was selected. Through the integration of landscape pattern indices analysis and redundancy analysis, the spatial and temporal variations in river water quality in the Xiaojiang River Basin were quantified, and the effects of land-use types and landscape patterns on river water quality were systematically elucidated. Results showed that (1) the key water quality indexes such as total phosphorus, total nitrogen, ammonia nitrogen and COD in the Xiaojiang River Basin were shown as flood season > non-flood season; for example, the average TN increased from 1.37 mg/L (non-flood season) to 2.90 mg/L (flood season), and the average COD increased from 3.24 mg/L to 15.98 mg/L. In contrast, DO decreased from 8.07 mg/L (non-flood season) to 6.72 mg/L (flood season), and conductivity decreased from 561.4 µs/cm to 480.90 µs/cm. (2) Spatially, these key water quality indicators were shown as hazard-prone area > residential area > cultivated land area. (3) The larger the area of the debris flow trace areas, the greater the fluxes of nitrogen and phosphorus in the tributaries and the main stream in the flood season, and the worse the water quality of the river; after heavy rainfall, the fluxes of key water quality indicators generally showed a geometric multiple increase, with average growth rates of 1.95 (TP), 2.41 (TN), 2.34 (NH3-N) and 4.74 (COD), respectively. (4) The ability of landscape patterns in flood season to explain the change in water quality is better than that in non-flood season. On different spatial scales, in the down-stream hazard-prone areas, upstream residential areas and cultivated land areas, the changes in river water quality indicators were mainly affected by landscape pattern indicators such as PD_hazard-influenced areas, IJI_residential areas and DIV_cultivated land. Our results can provide scientific guidance for the soil and water conservation practice, ecological restoration, and land-use management in the dry-hot valley of Southwest China and the water environment protection of the Baihetan Reservoir area.

Precipitation data is a primary influencing factor in hydrological modeling. However, the sparse distribution of surface hydrological stations and the lack of available data constrain the development of watershed models and the management and allocation of water resources. This study employs statistical metrics to evaluate discrepancies between observed precipitation data and multi-source precipitation products (CMADS, ERA5, GPM IMERG, and TRMM). It identifies highly sensitive parameters in the SWAT model established using observed hydrological data and quantitatively assesses runoff simulation performance in the Manas River Basin using the coefficient of determination and Nash index. Results indicate the following: (1) CMADS and TRMM exhibit good overall trends within a year. For multi-year monthly precipitation averages, CMADS performs best at monthly and seasonal scales (CC > 0.7), while TRMM performs best at the annual scale (CC > 0.75). (2) At spatial scales, IMERG shows the poorest performance compared to observed stations, and ERA5 exhibits anomalous points. (3) TRMM achieved the best monthly runoff simulation performance in the Manas River Basin, with an average NSE value of 0.73, average R2 of 0.80, and average KGE of 0.80. This study provides valuable scientific support for hydrological forecasting in data-scarce regions with complex topography and similar climate variability.

Neuroscience is rapidly growing, rendering it hard to keep track of the field’s structural organization, research trends, and open questions. This study tackles this issue by analyzing a vast corpus of articles published between 1999 and 2023. Findings reveal a field with a strong experimental focus, encompassing both hypothesis-driven and data-driven methodologies and an increasing use of advanced data analytics and artificial intelligence. A growing emphasis on applied research is evident, particularly in areas like neurodegeneration, neuromodulation, and technological advancements, while fundamental research is at risk of decline. The field demonstrates high levels of interdisciplinarity and surprisingly robust cross-fertilization across research domains, with key intellectual hubs shaping the broader landscape. Nevertheless, neuroscience widely relies on specific mechanistic explanations rather than unifying theoretical frameworks, and the integration of findings across different spatial and temporal scales remains limited. This study provides a framework for understanding neuroscience’s trajectory and identifies potential avenues for strengthening the field.

The residual current is the ocean current after the tidal component has been removed. Understanding the spatiotemporal distribution characteristics of sea surface residual currents is key to revealing the local current field evolution and typical physical oceanographic processes. The Taiwan Strait is in the East Asian monsoon region, where residual currents are significantly influenced by monsoons during periods of high wind speeds. However, the characteristics and dynamic mechanisms of residual currents under low wind speed conditions (≤5 m/s) remain unclear. Based on high-frequency surface wave radar current data and wind field reanalysis data, this study analyzed the characteristics of residual currents in the southwestern Taiwan Strait under low wind speed conditions, focusing on two orthogonal directions: cross-shore and along-shore. During these periods, residual currents exhibit counter-wind current characteristics. These currents cross the Taiwan Bank and generate wave signals with wavelengths ranging from 35.6 km to 65.8 km and durations of 6 to 12 h in the Xiapeng Depression area. These fluctuations are triggered by the combined timing of low winds and nonlinear current–topography interactions. In terms of dynamic mechanisms, the Coriolis force term and the acceleration term dominate the momentum equations in both two orthogonal directions, indicating that the current field is in a non-steady inertial adjustment phase during this period. Furthermore, this study constructs a two-layer ocean model of rotationally modified gravity waves to analyze the influences of topography, oceanic stratification, and steady current velocity on the characteristics of residual current fluctuations under low wind speed conditions. The theoretical model yields spatial scales that closely match the observed wavelength characteristics.

Image data acquisition is crucial for understanding long-term ecological processes. The video transect technique, developed in 1996 in Australia, has the main advantage of continuously visual recording the reef, allowing for the recovery of information that was previously unrecognized as relevant. With technological advances, the recovery of this information has reached a new level. Using photogrammetric techniques, it is possible to three-dimensionally reconstruct the filmed surface of reefs, enabling the collection of fundamental data on the structural complexity and population structure of corals and other benthic species. Structural complexity is the number of irregularities and indentations in the reef, indicating the amount of shelter it provides to marine fauna. Population structure describes the age distribution of species and is positively correlated with structural complexity. In this paper, we present the application of a 3D photogrammetry method to measure structural complexity in coral reefs using video transect images. The video transects used are 20 meters long by 48 centimeters wide, with an average duration of two to three minutes. To construct the models, 350 deinterlaced images were extracted from each video, ensuring a high overlap rate. The models were generated using Metashape 2.0.0 (Agisoft). The images were automatically aligned, followed by the generation of a mesh of overlapping points, which was then textured. A mosaic model was then produced. Spatial scale was calibrated using ten one-centimeter markings arranged along a 20-meter measuring tape. Structural complexity was estimated by the ratio between the number of points and vertices and the reconstructed area of the transect. To ensure comparability between models, it is essential to standardize the parameters used during processing to avoid bias. Changes in camera lens, resolution, and image quality can also affect the results. Despite these limitations, the technique offers a valuable opportunity to revisit the past of ecosystems recorded on video, allowing retrospective analyses with a high level of detail.