Search Results (4,076)

The East China Sea (ECS) is a globally active region for internal solitary waves (ISWs); however, its overall spatiotemporal distribution remains poorly understood. To address this gap, this study proposes a deep learning method based on multi-source remote sensing imagery (MODIS and SAR) for the intelligent identification and pixel-level segmentation of ISWs in the ECS. We adopted the TransUNet model, which combines the global context-capturing capability of Transformers with the fine-grained segmentation advantages of U-Net to effectively handle the large-scale continuous characteristics of ISWs. The model achieved a Dice coefficient of 71.0% and a precision of 72.7% on the test set, significantly outperforming existing models such as FCN, SegNet, DeepLabV3+, and U-Net. Using this automated framework, multi-source satellite data from 2002 to 2024 were processed to generate the first high-resolution spatiotemporal map of ISWs covering the entire ECS. The map reveals two spatial hotspots: a primary one at the shelf break northeast of Taiwan and a secondary one in the waters southwest of Jeju Island. Furthermore, ISWs exhibit a marked seasonal cycle in both occurrence frequency and properties, peaking in summer and minimizing in winter. This seasonal pattern aligns closely with the physics of internal tide generation via body forcing. By providing the first long-term, high-resolution ISW dataset for the entire ECS, this study demonstrates the potential of deep learning techniques for ISW research in complex marginal seas. Full article

With global warming, tropical islands, as sensitive areas to climate change, exhibit new and significant temperature variation characteristics. Using the high-resolution Hainan Island Regional Reanalysis (HNR) dataset and multi-source data, this study analyzes temperature changes on Hainan Island from 1900 to 2022, focusing on spatiotemporal trends, diurnal patterns, and probability distribution shifts. The findings reveal significant periodic temperature changes: weak warming (0.02–0.08 °C/decade) from 1900 to 1949, a temperature hiatus from 1950 to 1979, and accelerated warming (0.14–0.28 °C/decade) from 1979 to 2022. Coastal plains (0.11 °C/decade) warm faster than inland mountains (0.08 °C/decade), reflecting oceanic and topographic effects. Diurnal temperature variations show topographic dependence, with a maximum range (8–9 °C) in the north during the warm season, and a southwest–northeast gradient in the cold season. Probability density function analysis indicates that the curves for transitional and cold seasons show a noticeable widening and rightward shift, reflecting the increasing frequency of extreme temperature events under the trend of temperature rise. The study also finds that the occurrence time of daily maximum temperature over coastal plains is advancing (−0.05 to −0.1 h/decade). This study fills gaps in understanding tropical island climate responses under global warming and provides new insights into temperature changes over Hainan Island. Full article

Compound drought and heatwave (CDHW) events pose a rising threat to global water security and ecosystem stability. While their increased frequency under global warming is recognized, their spatiotemporal evolution and subsequent cascading impacts on hydrological processes in monsoonal lake basins remain poorly quantified. This study investigates the characteristics and hydrological impacts of CDHW in the Poyang Lake Basin, China’s largest freshwater lake, from 1981 to 2016. Using a daily rolling-window approach with the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI), we identified CDHW events and characterized them with metrics of frequency, severity, and intensity. Event coincidence analysis (ECA) was employed to quantify the trigger relationship between CDHW and subsequent hydrological droughts (streamflow and lake water level). Our results reveal a paradigmatic shift in the CDHW regime post-2000, marked by statistically significant increases in all three metrics and a fundamental alteration in their statistical distributions. ECA demonstrated that intensified CDHW events significantly enhance hydrological drought risk, primarily through a robust and increasing lagged influence at seasonal timescales (peaking at 40–90 days). Decomposition of compound events attributes this protracted impact predominantly to the heatwave component, which imposes prolonged hydrological stress, in contrast to the more immediate but rapidly decaying influence of drought alone. This study highlights the necessity of integrating compound extremes and their non-stationary, lagged impacts into water resource management and climate adaptation strategies for monsoonal basins. Full article

Major depressive disorder (MDD), identified by the World Health Organization as the leading cause of disability worldwide, remains underdiagnosed due to the lack of objective diagnostic tools. Electroencephalogram (EEG) signals offer potential biomarkers, yet conventional analyses often overlook the brain’s nonlinear dynamics. In this study, we analyzed resting-stage EEG data to identify four microstate types in MDD patients. Symmetrical microstate–brain networks were then constructed for each microstate by using time series of four types of microstates as dynamic windows. Then, we compared microstate features (duration, occurrence, coverage, transition probability) and brain network parameters (clustering coefficient, characteristic path length, local and global efficiency) between MDD patients and healthy controls to analyze the characteristics of the changes in the brain activities of the patients with MDD and the topological patterns of the functional connectivity. The comparative analysis showed that MDD patients showed more frequent microstate transitions and reduced network efficiency, suggesting elevated energy consumption and impaired neural integration, which may imply a cognitive shift in MDD patients toward internal focus and psychological withdrawal from external stimuli. By integrating microstate and brain network features, we captured the temporal and spatial characteristics of MDD-related brain activity and validated their diagnostic utility using our previously proposed multiscale spatiotemporal convolutional attention network (MSCAN). Our MSCAN achieved an accuracy of 98.64% for MDD detection, outperforming existing approaches. Our study can offer promising implications for the intelligent diagnosis of MDD and a deeper understanding of its neurophysiological underpinnings. Full article

In densely populated and traffic-congested major cities, medical emergency rescue incidents occur frequently, making the use of drones for emergency medical supplies delivery a new emergency distribution method. However, establishing drone transportation networks in urban areas requires balancing spatiotemporal fluctuations in emergency needs, meeting hospitals’ mandatory constraints on response time, and addressing factors like airspace restrictions and weather impacts. By analyzing the spatiotemporal distribution characteristics of medical emergency logistics in large cities, this study constructs a drone base station location optimization model integrating dynamic and static factors. The model combines multi-source data including emergency needs, geographic information, and airspace limitations. It employs kernel density estimation to identify hotspot areas, uses DBSCAN clustering to detect long-term stable demand hotspots, and applies LSTM methods to predict short-term and sudden demand fluctuations. The model optimizes coverage rate, response time, and cost budget control for drone transportation networks through a multi-objective genetic algorithm. Using Guangzhou as a case study, the results demonstrate that through “dynamic-static” collaborative deployment and multi-model drone coordination, the network achieves 96.18% demand coverage with an average response time of 673.38 s, significantly outperforming traditional vehicle transportation. Sensitivity analysis and robustness testing further validate the model’s effectiveness in handling demand fluctuations, weather changes, and airspace restrictions. This research provides theoretical support and decision-making basis for scientific planning of urban medical emergency drone transportation networks, offering practical significance for enhancing urban emergency rescue capabilities. Full article

Wind power forecasting is critical to renewable energy generation, as accurate predictions are essential for the efficient and reliable operation of power systems. However, wind power output is inherently unstable and is strongly affected by meteorological factors such as wind speed, wind direction, and atmospheric pressure. Weather conditions and wind power data are recorded by sensors installed in wind turbines, which may be damaged or malfunction during extreme or sudden weather events. Such failures can lead to inaccurate, incomplete, or missing data, thereby degrading data quality and, consequently, forecasting performance. To address these challenges, we propose a method that integrates a pre-trained large-scale language model (LLM) with the spatiotemporal characteristics of wind power networks, aiming to capture both meteorological variability and the complexity of wind farm terrain. Specifically, we design a spatiotemporal graph neural network based on multi-view maps as an encoder. The resulting embedded spatiotemporal map sequences are aligned with textual representations, concatenated with prompt embeddings, and then fed into a frozen LLM to predict future wind turbine power generation sequences. In addition, to mitigate anomalies and missing values caused by sensor malfunctions, we introduce a novel frequency-domain learning-based interpolation method that enhances data correlations and effectively reconstructs missing observations. Experiments conducted on real-world wind power datasets demonstrate that the proposed approach outperforms state-of-the-art methods, achieving root mean square errors of 17.776 kW and 50.029 kW for 24-h and 48-h forecasts, respectively. These results indicate substantial improvements in both accuracy and robustness, highlighting the strong practical potential of the proposed method for wind power forecasting in the renewable energy industry. Full article

This study presents a case study of the Ms5.5 Dezhou Earthquake to document the spatiotemporal characteristics of Outgoing Longwave Radiation (OLR) anomalies and their concurrent patterns with tidal force cycles. Based on NOAA satellite OLR data, synchronous monitoring and comparative analysis were conducted with tidal force variation cycles. The results show that pronounced OLR anomalies were concentrated exclusively in the co-seismic tidal cycle (Cycle C: 23 July–5 August 2023), while no significant anomalies were detected in pre-seismic Cycles A/B and post-seismic Cycle D. Temporally, the OLR anomalies in Cycle C exhibited a distinct six-stage evolutionary pattern: initial warming (31 July) → rapid intensification (1–3 August) → peak (4 August) → abrupt decline (5 August) → post-seismic pulse (6 August) → exponential decay (7–9 August). Spatially, the anomalies were closely distributed along the Liaocheng–Lankao Fault, showing a NE-trending (N35°E) distribution that matches the structural characteristics of the fault zone. Additionally, the spatial extent of OLR anomalies (within 400 km of the epicenter) is consistent with the effective detection range of co-seismic electromagnetic signals reported in existing studies. This study provides a typical observational case of OLR anomaly characteristics associated with medium-magnitude earthquakes, offering a reference for understanding the spatiotemporal evolution of seismic thermal anomalies. Full article

Using MODIS-Aqua satellite observations, this study analyzes the spatiotemporal distribution characteristics of particulate organic carbon (POC) in China’s marginal seas from 2003 to 2024. The statistical relationships between various marine environmental variables, including sea surface temperature (SST), nutrients, and primary production (PP), and POC concentrations are explored using partial least squares path modeling (PLS-PM). Finally, a box model approach is conducted to assess the POC budget in the study area. The results indicate that the POC concentration in the marginal seas of China generally exhibits a characteristic of being high in spring and low in summer. The highest concentration of POC is observed in the Bohai Sea, followed by the Yellow Sea, and the lowest in the East China Sea, with coastal waters exhibiting higher POC concentrations compared to the central areas. The spatial distribution and seasonal changes in POC are jointly influenced by PP, water mass exchange, resuspended sediments, and terrestrial inputs. Large-scale climate modes show statistical associations with POC concentration in the open waters of China’s marginal seas. PP and respiratory consumption are identified as the predominant input and output fluxes, respectively, in China’s marginal seas. This study enriches the understanding of carbon cycling processes and carbon sink mechanisms in marginal seas. Full article

The Japanese chub mackerel (Scomber japonicus) is a small pelagic economically important fish species in the northwest Pacific Ocean, and its abundance and distribution are influenced by water temperature changes. In recent years, frequent marine heatwaves (MHWs), defined as prolonged anomalously warm sea surface temperature events, in this region have significantly impacted marine ecosystems and fishery resources. The effects of MHWs on Japanese chub mackerel remain poorly understood. This study analyzed the relationship between Japanese chub mackerel abundance and MHW characteristics in the northwest Pacific Ocean from 2014 to 2021. It includes comparative analyses on the spatiotemporal patterns of catch per unit effort (CPUE) and MHWs, an exploration of CPUE distribution under varying MHW intensities and durations, and an assessment of the relationship between MHW characteristics and CPUE using a Generalized Additive Model (GAM) approach. Additionally, CPUE variations before, during, and after MHWs in 2016, 2018, and 2021 across different regions are measured. Results reveal significant interannual variability in MHWs, with increasing trends in the frequency, intensity, and duration of MHWs. As the frequency, intensity, and duration of MHWs increased, the abundance of Japanese chub mackerel decreased, particularly in years with higher intensity and longer lasting MHWs. The study concludes that MHWs negatively impact Japanese chub mackerel, highlighting the urgent need for climate-adaptive fishing and management strategies. Full article

Coastal wetlands, situated at the critical land–sea ecotone, play a vital role in sustaining ecological balance and supporting human activities. Currently, these ecosystems face dual stresses from climate change and intensified anthropogenic activities, making the quantitative assessment of ecosystem functions—represented by Gross Primary Productivity (GPP)—essential for their protection and management. However, a knowledge gap remains regarding coastal–urban complex ecosystems, and existing studies on coastal wetlands often overlook macro-environmental drivers beyond sea-level rise. This study leveraged the MOD17A2H V006 dataset to generate a 500 m GPP product for Zhanjiang City. We analyzed the spatiotemporal dynamics of GPP, utilized land use data to examine the evolution of coastal wetlands, and employed the Geodetector model to quantify the contributions of various factors to GPP in Zhanjiang and its coastal wetlands. The results indicate that: (1) GPP in Zhanjiang exhibited an overall steady upward trend, increasing at an average rate of $13.8\mathrm{g}\mathrm{C}·{\mathrm{m}}^{-2}·{\mathrm{yr}}^{-1}$. However, it displayed strong spatial heterogeneity, characterized by higher values in the southwest and lower values in the northern and coastal regions. (2) The land use pattern in Zhanjiang underwent significant transformations over the past two decades. Cropland and impervious surfaces expanded markedly, increasing by 194.6 km² and 290.42 km², respectively, while coastal wetland areas showed a continuous decline, with degraded and newly formed areas of 101.5 km² and 42 km², respectively. (3) The Geodetector results revealed that the q-value of Nighttime Light (NTL) increased from negligible values to over 0.1, emerging as a dominant driving factor. Although the driving force of anthropogenic activity factors on Zhanjiang and its coastal wetlands has steadily increased, natural factors currently remain the dominant forces. These findings unravel the driving mechanisms of natural and anthropogenic factors on GPP in Zhanjiang, providing valuable scientific evidence for the sustainable development of coastal ecosystems. Full article

Microseismic/acoustic emission (AE) monitoring enables real-time, non-destructive observation of deformation and failure processes in rock during loading and unloading. Accordingly, this study designed two experimental schemes—sandstone loading and unloading—to comparatively investigate the spatiotemporal evolution characteristics of AE during sandstone failure under these distinct stress paths. Based on AE waveform time-frequency parameters and AE source location results obtained during testing, the failure evolution patterns of rock under both loading paths were analyzed. The results demonstrate that: (1) In both loading and load-unloading experiments, rock failure exhibited a distinct four-stage characteristic. Under pure loading conditions, failure concentrated near the point of catastrophic rupture, whereas unloading triggered premature rock fracturing, with a more pronounced AE response observed during the unloading phase. (2) For both loading paths, the dominant frequencies of AE waveforms were concentrated within the 0–200 kHz range. A distinct low-frequency (0–100 kHz), high-amplitude zone emerged prominently during Stage 4 in both cases. (3) AE source locations under load-unloading conditions revealed that during Stage 3—characterized by vertical loading combined with lateral unloading in the minimum principal stress direction—tensile failure cracks nucleated within the rock. Subsequently, during Stage 4 of the loading phase, these cracks propagated and coalesced, ultimately forming a macroscopic fracture surface on the sandstone specimen. (4) The AE source location results under pure loading failure conditions indicate that under uniaxial vertical loading, compression-shear failure fractures begin to develop within the rock mass during Stage 3. With continued loading in Stage 4, these shear fractures propagate through to the specimen surface, forming a through-going shear fracture plane. Full article

Exploring the spatio-temporal evolution patterns of rapeseed production at the county level in Sichuan Province, China, and analyzing the influence of natural conditions and socioeconomic development based on regional spatial characteristics, can help guide the rational distribution of crop production and provide a reference for the high-quality and sustainable development of the local rapeseed industry. Based on panel data from 2001 to 2023, this study employs GIS spatial analysis to examine the spatio-temporal evolution of rapeseed production in Sichuan and applies a Geodetector model to identify factors influencing its spatial and temporal variations. The results reveal that rapeseed production in Sichuan is concentrated in three main production areas: the northeastern Sichuan region, the middle Sichuan hilly region, and the Chengdu Plain. The dynamic evolution exhibits a composite pattern characterized by the stability and expansion of core areas, alongside breakthroughs and growth in peripheral regions, with increased production observed across 134 counties. The spatial center of rapeseed production shows short-range fluctuations and distinct regional anchoring, oscillating among Santai County, Shehong City, and Daying County, tracing a “Z”-shaped trajectory. Over the 23-year period, the global Moran’s I index ranged from 0.464 to 0.558, indicating a significant spatial clustering trend in rapeseed output among adjacent counties. Local spatial autocorrelation patterns were predominantly H-H, L-L, and L-H clusters. Factor detection identifies labor force availability, fertilizer application intensity, and effective irrigated area as the most influential factors. Interaction detection results consistently exhibit a two-factor enhancement effect. To enhance the rapeseed industry’s performance and efficiency, it is recommended to stabilize production capacity in the three core production areas, leverage central regions to strengthen radiation to the surrounding counties, optimize resource allocation based on clustering patterns, and focus on improving key factors such as labor and irrigation, as well as their synergistic effects. Full article

The urban heat island (UHI) effect, intensified by rapid urbanization, necessitates the precise identification and mitigation of thermal sources and sinks. However, existing studies often overlook landscape connectivity and rarely analyze integrated source–sink networks within a unified framework. To address this gap, this research combines source–sink theory with the local climate zone classification to examine the spatiotemporal patterns of thermal characteristics in Fuzhou, China, from 2016 to 2023. Using morphological spatial pattern analysis, the minimum cumulative resistance model, and a gravity model, we identified key thermal source and sink landscapes, their connecting corridors, and barrier points. Results indicate that among built-type local climate zones, low-rise buildings exhibited the highest land surface temperature, while LCZ E and LCZ F were the warmest among natural types. Core heat sources were primarily LCZ 4, LCZ 7, and LCZ D, accounting for 19.71%, 13.66%, and 21.72% respectively, whereas LCZ A dominated the heat sinks, contributing to over 86%. We identified 75 heat source corridors, mainly composed of LCZ 7 and LCZ 4, along with 40 barrier points, largely located in LCZ G and LCZ D. Additionally, 70 heat sink corridors were identified, with LCZ A constituting 96.39% of them, alongside 84 barrier points. The location of these key structures implies that intervention efforts—such as implementing green roofs on high-intensity source buildings, enhancing the connectivity of cooling corridors, and performing ecological restoration at pinpointed barrier locations—can be deployed with maximum efficiency to foster sustainable urban thermal environments and support climate-resilient city planning. Full article

Background and Objectives: Sarcopenia and muscle wasting contribute significantly to functional decline in older adults, but differences in lower extremity muscle stiffness and gait variability between these groups are not yet fully understood. This study aimed to compare gait variability, and lower extremity muscle stiffness during contraction and relaxation in community-dwelling older adults classified as non-diseased, sarcopenic, and dynapenic. Materials and Methods: This cross-sectional study included 164 community-dwelling older adults classified as non-diseased, dynapenic, or sarcopenic, based on handgrip strength, 5-time sit-to-stand test, and skeletal muscle index. Spatiotemporal gait variability was measured at the participants’ preferred speed. Moreover, muscle thickness, as well as the contractile and relaxed stiffness, were measured for the rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), gastrocnemius medialis (GAmed), and lateralis (GAlat). Results: In dynapenic and sarcopenic groups, gait variability increased across most parameters, but only the step width coefficient of variation differed significantly between the dynapenic and sarcopenic groups. Contractile stiffness of the RF, BF, and GAlat was lower in both groups, with additional GAmed stiffness reduction in the sarcopenic group. Relaxed stiffness of the BF and GAmed was significantly higher in the sarcopenic group than in the dynapenic group. Conclusions: This study identified differences in muscle thickness, stiffness, and gait variability among non-diseased, dynapenic, and sarcopenic older adults. Step width variability, GAmed contractile stiffness, and BF and GAmed relaxed stiffness emerged as potential early indicators for distinguishing dynapenia from sarcopenia. These findings highlight the importance of assessing muscle quality—including both mass and stiffness characteristics—to better characterize early stages of age-related muscle decline and to inform targeted intervention strategies. Full article

This review summarizes a growing collection of data on adult neurogenesis in various vertebrate species, with a focus on teleost fish and mammals. Teleost fish serve as exceptional models for studying the dynamics of the cell cycle and the functions of adult neural stem progenitor cells (aNSPCs) throughout the central nervous system (CNS). New information about the characteristics of cells in various areas of the telencephalon of non-model objects—juvenile masu salmon Oncorhynchus masou and chum salmon Oncorhynchus keta—during postembryonic ontogenesis and after traumatic injury expands the current understanding of the issue. The expression of molecular markers of adult-type glial precursors in the model zebrafish and non-model objects, juveniles O. masou and O. keta, was presented. Immunohistochemical (IHC) verification of BrdU and PCNA made it possible to identify a population of rapidly and slowly proliferating cells in the pallium of intact O. masou and after traumatic brain injury (TBI). In salmonids, unlike in mammals, progenitor cells are able to differentiate into neurons after injury. The expression of vimentin and GFAP in the aNSCPs has functional specificity. A comparative analysis of the expression of Pax transcription factors in various vertebrates and juveniles O. masou is presented. Pax genes maintain cells in an undifferentiated state and ensure the spatiotemporal formation of mature cell types in changing developing neurogenic niches. The functions of glutamine synthetase (GS) and H₂S in the brains of vertebrates and juvenile chum salmon under intact conditions and after TBI are characterized. In fish, unlike mammals, as a result of TBI, neuronal conduction is restored in the injury area, whereas in mammals the regenerative process is complicated by neuroinflammation and culminates in the formation of a glial scar. Full article