Search Results (3,458)

Event cameras, as bio-inspired visual sensors, offer significant advantages in their high dynamic range and high temporal resolution for visual tasks. These capabilities enable efficient and reliable motion estimation even in the most complex scenes. However, these advantages come with certain trade-offs. For instance, current event-based vision sensors have low spatial resolution, and the process of event representation can result in varying degrees of data redundancy and incompleteness. Additionally, due to the inherent characteristics of event stream data, they cannot be utilized directly; pre-processing steps such as slicing and frame compression are required. Currently, various pre-processing algorithms exist for slicing and compressing event streams. However, these methods fall short when dealing with multiple subjects moving at different and varying speeds within the event stream, potentially exacerbating the inherent deficiencies of the event information flow. To address this longstanding issue, we propose a novel and efficient Asynchronous Spike Dynamic Metric and Slicing algorithm (ASDMS). ASDMS adaptively segments the event stream into fragments of varying lengths based on the spatiotemporal structure and polarity attributes of the events. Moreover, we introduce a new Adaptive Spatiotemporal Subject Surface Compensation algorithm (ASSSC). ASSSC compensates for missing motion information in the event stream and removes redundant information, thereby achieving better performance and effectiveness in event stream segmentation compared to existing event representation algorithms. Additionally, after compressing the processed results into frame images, the imaging quality is significantly improved. Finally, we propose a new evaluation metric, the Actual Performance Efficiency Discrepancy (APED), which combines actual distortion rate and event information entropy to quantify and compare the effectiveness of our method against other existing event representation methods. The final experimental results demonstrate that our event representation method outperforms existing approaches and addresses the shortcomings of current methods in handling event streams with multiple entities moving at varying speeds simultaneously. Full article

Background/Objectives: Pattern glare, associated with cortical hyperexcitability, induces visual distortions and discomfort, particularly in individuals susceptible to migraines or epilepsy. While previous research has primarily focused on transient EEG responses to patterned stimuli, this study aims to investigate how continuous presentation of pattern-glare stimuli affects neural adaptation over both fine (seconds) and coarse (entire experiment) temporal scales. Methods: EEG recordings were obtained from 40 healthy participants exposed to horizontal square-wave gratings at three spatial frequencies presented continuously for three seconds each across multiple trials. Participants’ susceptibility to visual stress, headaches, and discomfort was assessed using questionnaires before and during the experiment. The experiment employed a two-by-two design to evaluate habituation (exponentially decreasing response) and sensitisation (exponentially increasing response) effects at two different time granularities. Mass univariate analysis with cluster-based permutation tests was conducted to identify significant brain response changes during the period of constant stimulation, which we call the DC-shift period. Results: Significant effects were observed during the DC-shift period, indicating sustained hyper-excitation to the medium-pattern glare stimulus. In particular, the mean/intercept analysis revealed a consistent positive-going response to the medium stimulus throughout the DC-shift period, suggesting continued neural engagement. Participants reporting higher discomfort exhibited sensitisation at fine temporal granularity and habituation at coarser temporal granularity. These effects were predominantly localised to the right posterior scalp regions. Conclusions: The study demonstrates that individuals sensitive to pattern-glare stimuli exhibit dynamic neural adaptation characterised by short-term sensitisation and long-term habituation. These findings enhance the understanding of cortical hyperexcitability mechanisms and may inform future interventions for visual-stress-related conditions, such as migraines and epilepsy. Further research is needed to explore the underlying neural processes and validate these effects in clinical populations. Full article

The calcium cation is a crucial signaling molecule involved in numerous cellular pathways. Beyond its role as a messenger or modulator in intracellular cascades, calcium’s function in excitable cells, including nerve impulse transmission, is remarkable. The central role of calcium in nervous activity has driven the rapid development of fluorescent techniques for monitoring this cation in living cells. Specifically, genetically encoded calcium indicators (GECIs) are the most in-demand molecular tools in their class. In this work, we address two issues of calcium imaging by designing indicators based on the successful GCaMP6 backbone and the fluorescent protein BrUSLEE. The first indicator variant (GCaMP6s-BrUS), with a reduced, calcium-insensitive fluorescence lifetime, has potential in monitoring calcium dynamics with a high temporal resolution in combination with advanced microscopy techniques, such as light beads microscopy, where the fluorescence lifetime limits acquisition speed. Conversely, the second variant (GCaMP6s-BrUS-145), with a flexible, calcium-sensitive fluorescence lifetime, is relevant for static measurements, particularly for determining absolute calcium concentration values using fluorescence lifetime imaging microscopy (FLIM). To identify the structural determinants of calcium sensitivity in these indicator variants, we determine their spatial structures. A comparative structural analysis allowed the optimization of the GCaMP6s-BrUS construct, resulting in an indicator variant combining calcium-sensitive behavior in the time domain and enhanced molecular brightness. Our data may serve as a starting point for further engineering efforts towards improved GECI variants with fine-tuned fluorescence lifetimes. Full article

Accurately delineating sediment export dynamics using high-quality vegetation factors remains challenging due to the spatio-temporal resolution imbalance of single remote sensing data and persistent cloud contamination. To address these challenges, this study proposed a new framework for estimating and analyzing monthly sediment inflow to rivers in the cloud-prone Minjiang River Basin. We leveraged multi-source remote sensing data and the Continuous Change Detection and Classification model to reconstruct monthly vegetation factors at 30 m resolution. Then, we integrated the Chinese Soil Loss Equation model and the Sediment Delivery Ratio module to estimate monthly sediment inflow to rivers. Lastly, the Optimal Parameters-based Geographical Detector model was harnessed to identify factors affecting sediment export. The results indicated that: (1) The simulated sediment transport modulus showed a strong coefficient of determination (R² = 0.73) and a satisfactory Nash–Sutcliffe efficiency coefficient (0.53) compared to observed values. (2) The annual sediment inflow to rivers exhibited a spatial distribution characterized by lower levels in the west and higher in the east. The monthly average sediment value from 2016 to 2021 was notably high from March to July, while relatively low from October to January. (3) Erosive rainfall was a decisive factor contributing to increased sediment entering the rivers. Vegetation factors, manifested via the quantity (Fractional Vegetation Cover) and quality (Leaf Area Index and Net Primary Productivity) of vegetation, exert a pivotal influence on diminishing sediment export. Full article

The accurate prediction of PM_2.5 concentration across extensive temporal and spatial scales is essential for air pollution control and safeguarding public health. To address the challenges of the uneven coverage and limited number of traditional PM_2.5 ground monitoring networks, the low inversion accuracy of PM_2.5 concentration, and the incomplete understanding of its spatiotemporal dynamics, this study proposes a refined PM_2.5 concentration estimation model, Bi-LSTM-SA, integrating multi-source remote sensing data. First, utilizing multi-source remote sensing data, such as MODIS aerosol optical depth (AOD) products, meteorological data, and PM_2.5 monitoring sites, AERONET AOD was used to validate the accuracy of the MODIS AOD data. Variables including temperature (TEMP), relative humidity (RH), surface pressure (SP), wind speed (WS), and total precipitation (PRE) were selected, followed by the application of the variance inflation factor (VIF) and Pearson’s correlation coefficient (R) for variable screening. Second, to effectively capture temporal dependencies and emphasize key features, an improved Long Short-Term Memory Network (LSTM) model, Bi-LSTM-SA, was constructed by combining a bidirectional LSTM (Bi-LSTM) model with a self-adaptive attention mechanism (SA). This model was evaluated through ablation and comparative experiments using three cross-validation methods: sample-based, temporal, and spatial. The effectiveness of this method was demonstrated on Beijing–Tianjin–Hebei urban agglomeration, achieving a coefficient of determination (R²) of 0.89, root mean squared error (RMSE) of 12.76 μg/m³, and mean absolute error (MAE) of 8.27 μg/m³. Finally, this model was applied to predict PM_2.5 concentration on Beijing–Tianjin–Hebei urban agglomeration in 2023, revealing the characteristics of its spatiotemporal evolution. Additionally, the results indicated that this model performs exceptionally well in hourly PM_2.5 concentration forecasting and can be used for PM_2.5 concentration hourly prediction tasks. This study provides technical support for the large-scale, accurate remote sensing inversion of PM_2.5 concentration and offers fundamental insights for regional atmospheric environmental protection. Full article

Monochamus alternatus Hope and Arhopalus rusticus (L.) are important stem-boring pests that co-occur on weakened Pinus spp. Their larvae damage the xylem and phloem of the trunks and branches. At present, the consequences of the interspecific relationship between two longicorn beetles on the same host of Pinus trees are unclear. The population dynamics and spatial distribution of these two species on Pinus thunbergii trees were investigated to clarify the ecological niches and interspecific relationship of two longicorn beetles on the different degrees of decline in P. thunbergii trees. The results showed temporal niche overlap values from 0.02 ± 0.01 to 0.05 ± 0.02, suggesting a very high degree of temporal ecological niche segregation and no competition in temporal niche resources. There is significant interspecific competition between the two longicorn beetles in spatial distribution, and the spatial niche overlap values are 0.67 ± 0.11 and 0.61 ± 0.09 in the middle and late stages of the decline in P. thunbergii trees, respectively. Full article

Forest fires represent a significant intersection between nature and society, often leading to the loss of natural resources, soil nutrients, and economic opportunities, as well as causing desertification and the displacement of communities. Therefore, the objective of this work is to analyze the multitemporal conditions of a sixth-generation forest fire through the use and implementation of tools such as remote sensing, photointerpretation with geographic information systems (GISs), thematic information on land use, and the use of spatial indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Burned Ratio (NBR), and its difference (dNBR) with satellite images from Sentinel-2. To improve our understanding of the dynamics and changes that occurred due to the devastating forest fire in Los Guájares, Granada, Spain, in September 2022, which affected 5194 hectares and had a perimeter of 150 km, we found that the main land use in the study area was forest, followed by agricultural areas which decreased from 1956 to 2003. We also observed the severity of burning, shown with the dNBR, reflecting moderate–low and moderate–high levels of severity. Health and part of the post-fire recovery process, as indicated by the NDVI, were also observed. This study provides valuable information on the spatial and temporal dimensions of forest fires, which will favor informed decision making and the development of effective prevention strategies. Full article

Changes in cultivated land use significantly impact food production capacity, which in turn affects food security. Therefore, accurately understanding the spatial and temporal variations in cultivated land use is critical for strategic decision-making regarding national food security. Since the second national soil survey was conducted in around 1980, China has implemented major efforts, such as a nationwide soil testing and fertilization project in around 2005 and the establishment of the National Standards for Cultivated Land Quality Grading in 2016. However, limited research has focused on how cultivated land use has changed during these periods and the mechanisms driving these changes. This study, using Enshi Prefecture in the mountainous region of southwestern Hubei Province as a case study, examines the spatiotemporal changes in cultivated land use during 1980–2018. Land use data from 1980, 2005, and 2018 were combined with statistical yearbook data from Enshi Prefecture, and remote sensing and GIS technology were applied. Indicators such as the dynamic degree of cultivated land use, the relative rate of change in cultivated land use, and a Geoscience Information Atlas model were used to explore these changes. Additionally, principal component analysis was employed to examine the mechanisms influencing these changes. The results show that (1) the area of cultivated land in Enshi Prefecture increased slightly from 1980 to 2005, while from 2005 to 2018, it significantly decreased; compared with the earlier period, the transformation of land use types during 2005–2018 was more intense; (2) the increase in cultivated land area from 1980 to 2005 was mainly due to deforestation, the creation of farmland from lakes, and the reclamation of wasteland, while the decrease in land area was primarily attributed to the conversion of farmland back to forests and grassland. From 2005 to 2018, the main drivers for the increase in cultivated land were deforestation and the reclamation of wasteland, while the return of farmland to forests remained the primary reason for the decrease in land area; (3) from 1980 to 2005, the dynamic degree of cultivated land use in each county and city of Enshi Prefecture was generally low. However, between 2005 and 2018, the dynamic degree increased in most counties and cities except Enshi City and Xianfeng County; (4) there were significant variations in the relative rate of change in cultivated land utilization across counties and cities from 1980 to 2005. However, from 2005 to 2018, the relative rate of change decreased in all counties and cities compared to the previous period; (5) since 1980, nearly 50% of the cultivated land in Enshi Prefecture has undergone land classification conversion, with frequent shifts between different land classes; and (6) economic development, population growth, capital investment, food production, and production efficiency are the dominant socioeconomic factors driving changes in cultivated land use in Enshi Prefecture. The results of this study can provide a scientific basis for the protection and optimization of cultivated land resources in the mountainous regions of southwestern Hubei Province. Full article

N-methyl-D-aspartate receptors (NMDARs) are critical components of the mammalian central nervous system, involved in synaptic transmission, plasticity, and neurodevelopment. This review focuses on the structural and functional characteristics of NMDARs, with a particular emphasis on the GRIN2 subunits (GluN2A-D). The diversity of GRIN2 subunits, driven by alternative splicing and genetic variants, significantly impacts receptor function, synaptic localization, and disease manifestation. The temporal and spatial expression of these subunits is essential for typical neural development, with each subunit supporting distinct phases of synaptic formation and plasticity. Disruptions in their developmental regulation are linked to neurodevelopmental disorders, underscoring the importance of understanding these dynamics in NDD pathophysiology. We explore the physiological properties and developmental regulation of these subunits, highlighting their roles in the pathophysiology of various NDDs, including ASD, epilepsy, and schizophrenia. By reviewing current knowledge and experimental models, including mouse models and human-induced pluripotent stem cells (hiPSCs), this article aims to elucidate different approaches through which the intricacies of NMDAR dysfunction in NDDs are currently being explored. The comprehensive understanding of NMDAR subunit composition and their mutations provides a foundation for developing targeted therapeutic strategies to address these complex disorders. Full article

Soil salinization is a critical global environmental issue, exacerbated by climatic and anthropogenic factors, and posing significant threats to agricultural productivity and ecological stability in arid regions. Therefore, remote sensing-based dynamic monitoring of soil salinization is crucial for timely assessment and effective mitigation strategies. This study used Landsat imagery from 2001 to 2021 to evaluate the potential of support vector machine (SVM) and classification and regression tree (CART) models for monitoring soil salinization, enabling the spatiotemporal mapping of soil salinity in the Yutian Oasis. In addition, the land use transfer matrix and spatial overlay analysis were employed to comprehensively analyze the spatiotemporal trends of soil salinization. The geographical detector (Geo Detector) tool was used to explore the driving factors of the spatiotemporal evolution of salinization. The results indicated that the CART model achieved 5.3% higher classification accuracy than the SVM, effectively mapping the distribution of soil salinization and showing a 26.76% decrease in salinized areas from 2001 to 2021. Improvements in secondary salinization and increased vegetation coverage were the primary contributors to this reduction. Geo Detector analysis highlighted vegetation (NDVI) as the dominant factor, and its interaction with soil moisture (NDWI) has a significant impact on the spatial and temporal distribution of soil salinity. This study provides a robust method for monitoring soil salinization, offering critical insights for effective salinization management and sustainable agricultural practices in arid regions. Full article

In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model’s ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model. Full article

The sustainability of watershed management is a crucial issue that must be addressed to guarantee the persistence of watershed services including agriculture, food production, and energy supply. This issue has also been addressed in Presidential Regulation No. 18/2020 concerning the National Medium-Term Development Plans for 2020–2024, which stipulate the restoration of priority watersheds, including the Upstream Bengawan Solo Watershed. This study seeks to address this information gap by assessing the local sustainability of the watershed from a temporal dynamics perspective by calculating the Local Sustainability Index (LSI), Local Moran Index, and spatial associations. Measuring sustainable development indices locally is essential because each location has different characteristics, and using specific indicators at the local level is rarely done. The enactment of the national law on village autonomy in Indonesia necessitates the formulation of sustainable development indicators at the village level. These indicators serve as the metrics and frameworks for local government policies and initiatives. Our results show that village sustainability in the social and economic dimensions has increased from 2007 to 2021, especially in urban activity center areas that serve social and economic facilities. This seems different in the environmental dimension, where the sustainability value decreased from 2007 to 2021. The concentration of low sustainability values on ecological conditions occurred in pocket areas. Environmental problems were indicated by land-use conversion and disaster areas. Full article

Deep reinforcement learning (DRL)-based navigation in an environment with dynamic obstacles is a challenging task due to the partially observable nature of the problem. While DRL algorithms are built around the Markov property (assumption that all the necessary information for making a decision is contained in a single observation of the current state) for structuring the learning process; the partially observable Markov property in the DRL navigation problem is significantly amplified when dealing with dynamic obstacles. A single observation or measurement of the environment is often insufficient for capturing the dynamic behavior of obstacles, thereby hindering the agent’s decision-making. This study addresses this challenge by using an environment-specific heuristic approach to augment the dynamic obstacles’ temporal information in observation to guide the agent’s decision-making. We proposed Multichannel Cost Map Observation for Spatial and Temporal Information (M-COST) to mitigate these limitations. Our results show that the M-COST approach more than doubles the convergence rate in concentrated tunnel situations, where successful navigation is only possible if the agent learns to avoid dynamic obstacles. Additionally, navigation efficiency improved by 35% in tunnel scenarios and by 12% in dense-environment navigation compared to standard methods that rely on raw sensor data or frame stacking. Full article

Wetlands are crucial for sustainable development, and the evaluation of their GEP is a key focus for governments and scientists. This study created a dynamic accounting model for wetland GEP and assessed the GEP of 39 wetlands in the middle reaches of the Yellow River in Ningxia province. The results indicate that Ningxia province’s wetlands have an average annual GEP of CNY 5.24 billion. Haba wetland contributes the most at 0.52, while Qingtongxia, Sha, and Tenggeli wetlands follow with 0.12, 0.04, and 0.03, respectively. Climate regulation is the most valuable function at 38.24%, with species conservation and scientific research/tourism at 24.93% and 15.11%, respectively. Ningxia’s northern wetlands are vast and shaped by the Yellow River, while the smaller, seasonal southern wetlands are more affected by rainfall and mountain groundwater. Southern wetlands show a strong correlation between GEP and precipitation (0.82), whereas northern wetlands have a moderate correlation between GEP and evapotranspiration (0.52). The effective conservation and management of these wetlands require consideration of their locations and weather patterns, along with customized strategies. To maintain the stability of wetland habitats and provide a suitable environment for various species, it is essential to preserve wetlands within a certain size range. Our study found a strong correlation of 0.85 between the wetland area and the GEP value, indicating that the size of wetlands is a key factor in conserving their GEP. The results provide accurate insights for creating a wetland ecological benefit compensation mechanism. Full article

Ecosystem stability plays a pivotal role in safeguarding the enduring well-being of both the natural world and human society. This work explores the uncertainty surrounding changes in ecosystem stability and their response mechanisms at localized scales, focusing on the Ugan–Kuqa River Basin in Xinjiang, China. Based on remote sensing data and spatial lag modeling (SLM), we evaluated the spatial and temporal dynamics of the basin’s stability from 2001 to 2020. Additionally, structural equation modeling (SEM) was employed to assess the impacts of climate conditions, human activities, and habitat fragmentation on ecosystem stability. The results of the study indicated that the basin ecosystem stability tended to increase in the temporal dimension, and that the spatial distribution was greater in the north than in the south. In addition, the trade-off between resistance and recovery in the watershed decreased, with a considerable increase in high-resistance–high-recovery zones. Climate warming and increased humidity have emerged as the predominant factors driving the watershed ecosystem stability. Full article