Search Results (18,309)

Quantifying and estimating shipping emissions is a critical component of global emission reduction research and has become a growing area of interest in recent years. However, emissions from short-distance passenger ships operating on inter-island routes and their environmental impacts have received limited attention. This contribution investigated the temporal and spatial distribution characteristics of pollutants emitted by short-distance passenger ships at Zhoushan (China) using Automatic Identification System (AIS) data and the bottom–up emission model integrated with multi-source meteorological data. A year-long emission inventory was investigated. The results indicated that high-speed passenger ships contributed to the largest share of the emissions. The emissions were predominantly concentrated during daytime hours, with the routes between Zhoushan Island and Daishan, Daishan and Shengsi, and Zhoushan Island and Liuheng Island accounting for most of the emissions. Furthermore, intra-port waterways were identified as the primary emission areas for short-distance passenger ships. This study provides essential data support and references for the relevant authorities to understand the emission patterns of short-distance passenger ships, thereby facilitating the formulation of targeted emission reduction strategies for the maritime passenger transport sector. Full article

The continental margin is a major repository for organic carbon; however, anthropogenic alterations to global sediment and particulate terrestrial organic carbon (TerrOC) fluxes have reduced delivery by rivers and offshore burial in recent decades. Despite the absence of mainstem damming, land use change in the Ayeyarwady and Thanlwin River catchments in Myanmar has accelerated over the last 50 years. As a result, deforestation and landscape erosion have likely altered fluvial fluxes to the Northern Andaman Sea shelf; however, the magnitude and preservation of geochemical signals associated with development are unknown. Utilizing elemental and bulk stable and radioisotope analysis, this study investigates spatial and temporal trends in sediment sources and TerrOC concentrations to identify the potential impacts of recent (<100 years) offshore development. While our results demonstrate an along-shelf trend in provenance and TerrOC concentrations, temporal (downcore) trends are not observed. We attribute this observation to frequent, large-scale seabed resuspension and suggest that extensive mixing on the inner shelf creates a low-pass filter that effectively attenuates such signatures. This is in contrast to other large Asian deltas, where signals of human landscape disturbance are clearly preserved offshore. We predict that planned mainstem damming in Myanmar will result in larger alterations in sediment and TerrOC supply that may become apparent offshore in the near future. Full article

Marine regions are undergoing rapid evolution, primarily driven by natural and anthropogenic activities. Safeguarding these ecosystems necessitates the ability to observe their physical features and control processes with precision in both space and time. This demands the acquisition of precise and up-to-date information regarding several marine parameters. Thus, to gain a comprehensive understanding of these ecosystems, this study employs remote sensing techniques, Machine Learning algorithms and traditional in situ approaches. Together, these serve as valuable tools to help comprehend the distinctive parametric characteristics and mechanisms occurring within these regions of the Maltese archipelago. An empirical workflow was implemented to predict the spatial and temporal variations in sea surface salinity and sea surface temperature from 2022 to 2024. This was achieved by leveraging Sentinel-2 satellite platforms, the random forest Machine Learning algorithm, and in situ data collected from sea gliders and floats. Subsequently, the numerical data generated by the random forest algorithm were validated with different error metrics and converted into visual representations to illustrate the sea surface salinity and sea surface temperature variations across the Maltese Islands. The random forest algorithm demonstrated strong performance in predicting sea surface salinity and sea surface temperature, indicating its capability to handle dynamic parameters effectively. Additionally, the parametric maps generated for all three years provided a clear understanding of both the spatial and temporal changes for these two parameters. Full article

Vegetation coverage in the upper reaches of the Yangtze River is very important to the ecological balance in this area, and it also has an impact on the inflow runoff and sediment transport processes of the Three Gorges Reservoir. Based on the normalized vegetation index data (NDVI) with 250 m resolution in the upper reaches of the Yangtze River, annual runoff, sediment transport, land use, meteorology, and other data—and by using the methods of Sen + Mann–Kendall trend analysis, partial correlation analysis, and Hurst index—this paper analyzes the temporal and spatial variation characteristics, driving factors, and the influence on the water and sediment inflow processes of the Three Gorges Reservoir in each sub-basin in the upper reaches of the Yangtze River. The results show that (1) NDVI in the upper Yangtze River showed a fluctuating upward trend from 2001 to 2022, and the overall vegetation cover continued to increase, showing a spatial pattern of low in the west and high in the east. At the same time, the runoff volume of the upper reaches of the Yangtze River did not show a significant upward trend from 2006 to 2022, while the sand transport decreased significantly; (2) Among the NDVI-influencing factors in the upper reaches of the Yangtze River, the area driven by the land use factor accounts for about 43% of the whole study area, followed by precipitation; (3) Precipitation significantly affected runoff, and NDVI was negatively correlated with sand transport in most of the watersheds, suggesting that improved vegetation could help reduce sediment loss. In addition, the future trend of vegetation change was predicted to be dominated by improvement (Hurst > 0.5) based on the Hurst index, which will provide a reference for the NDVI change in the upper Yangtze River and the prediction of sediment inflow to the Three Gorges Reservoir. Full article

Advanced deep learning algorithms play a key role in optimizing energy usage in smart cities, leveraging massive datasets to increase efficiency and sustainability. These algorithms analyze real-time data from sensors and IoT devices to predict energy demand, enabling dynamic load balancing and reducing waste. Reinforcement learning models optimize power distribution by learning from historical patterns and adapting to changes in energy usage in real time. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) facilitate detailed analysis of spatial and temporal data to better predict energy usage. Generative adversarial networks (GANs) are used to simulate energy usage scenarios, supporting strategic planning and anomaly detection. Federated learning ensures privacy-preserving data sharing in distributed energy systems, promoting collaboration without compromising security. These technologies are driving the transformation towards sustainable and energy-efficient urban environments, meeting the growing demands of modern smart cities. However, there is a view that if the pace of development is maintained with large amounts of data, the computational/energy costs may exceed the benefits. The article aims to conduct a comparative analysis and assess the development potential of this group of technologies, taking into account energy efficiency. Full article

Driven by the rapid advancement of Unmanned Aerial Vehicle (UAV) technology, the field of UAV object tracking has witnessed significant progress. This study introduces an innovative single-stream UAV tracking architecture, dubbed NT-Track, which is dedicated to enhancing the efficiency and accuracy of real-time tracking tasks. Addressing the shortcomings of existing tracking systems in capturing temporal relationships between consecutive frames, NT-Track meticulously analyzes the positional changes in targets across frames and leverages the similarity of the surrounding areas to extract feature information. Furthermore, our method integrates spatial and temporal information seamlessly into a unified framework through the introduction of a temporal feature fusion technique, thereby bolstering the overall performance of the model. NT-Track also incorporates a spatial neighborhood feature extraction module, which focuses on identifying and extracting features within the neighborhood of the target in each frame, ensuring continuous focus on the target during inter-frame processing. By employing an improved Transformer backbone network, our approach effectively integrates spatio-temporal information, enhancing the accuracy and robustness of tracking. Our experimental results on several challenging benchmark datasets demonstrate that NT-Track surpasses existing lightweight and deep learning trackers in terms of precision and success rate. It is noteworthy that, on the VisDrone2018 benchmark, NT-Track achieved a precision rate of 90% for the first time, an accomplishment that not only showcases its exceptional performance in complex environments, but also confirms its potential and effectiveness in practical applications. Full article

We describe the various steps of a gas imaging algorithm developed for detecting, identifying, and quantifying gas leaks using data from a snapshot infrared spectral imager. The spectral video stream delivered by the hardware allows the system to combine spatial, spectral, and temporal correlations into the gas detection algorithm, which significantly improves its measurement sensitivity in comparison to non-spectral video, and also in comparison to scanning spectral imaging. After describing the special calibration needs of the hardware, we show how to regularize the gas detection/identification for optimal performance, provide example SNR spectral images, and discuss the effects of humidity and absorption nonlinearity on detection and quantification. Full article

Engineers, geomorphologists, and ecologists acknowledge the need for temporally and spatially resolved measurements of sediment clogging (also known as colmation) in permeable gravel-bed rivers due to its adverse impacts on water and habitat quality. In this paper, we present a novel method for non-destructive, real-time measurements of pore-scale sediment deposition and monitoring of clogging by using wire-mesh sensors (WMSs) embedded in spheres, forming a smart gravel bed (GravelSens). The measuring principle is based on one-by-one voltage excitation of transmitter electrodes, followed by simultaneous measurements of the resulting current by receiver electrodes at each crossing measuring pores. The currents are then linked to the conductive component of fluid impedance. The measurement performance of the developed sensor is validated by applying the Maxwell Garnett and parallel models to sensor data and comparing the results to data obtained by gamma ray computed tomography (CT). GravelSens is tested and validated under varying filling conditions of different particle sizes ranging from sand to fine gravel. The close agreement between GravelSens and CT measurements indicates the technology’s applicability in sediment–water research while also suggesting its potential for other solid–liquid two-phase flows. This pore-scale measurement and visualization system offers the capability to monitor clogging and de-clogging dynamics within pore spaces up to 10,000 Hz, making it the first laboratory equipment capable of performing such in situ measurements without radiation. Thus, GravelSens is a major improvement over existing methods and holds promise for advancing the understanding of flow–sediment–ecology interactions. Full article

Video instance segmentation (VIS) is plagued by the high cost of pixel-level annotation and defects of weakly supervised segmentation, leading to the urgent need for a trade-off between annotation cost and performance. We propose a novel In-Depth Collaboratively Supervised video instance segmentation (IDCS) with efficient training. A collaborative supervised training pipeline is designed to flow samples of different labeling levels and carry out multimodal training, in which instance clues are obtained from mask-annotated instances to guide the box-annotated training through an in-depth collaborative paradigm: (1) a trident learning method is proposed, which leverages the video temporal consistency to match instances with multimodal annotation across frames for effective instance relation learning without additional network parameters; (2) spatial clues in the first frames are captured to implement multidimensional pixel affinity evaluation of box-annotated instances and augment the noise-disturbed spatial affinity map. Experiments on YoutTube-VIS validate the performance of IDCS with mask-annotated instances in the first frames and the bounding-box-annotated samples in the remaining frames. IDCS achieves up to 92.0% fully supervised performance and average 1.4 times faster, 2.2% mAP higher than the weakly supervised baseline. The results show that IDCS can efficiently utilize multimodal data, while providing advanced guidance for effective trade-off in VIS training. Full article

The spatial–temporal dynamics of an in vitro radicular system of Typha domingensis for the development of rhizofiltration technologies, with the potential for use as a phytotreatment of eutrophicated water, were studied for the first time in the roots of seedlings and in rhizotron systems. The effect of indole-3-acetic acid (AIA) in combination with kinetin (CIN) or 6-benzylaminopurine (BAP) on seedlings cultivated in the light and dark in three radicular systems and in a rhizotrophic regime for the screening of dynamic rhizogenic lines, by weekly allometric measurements of the length and number of roots, were studied. Inhibition of the elongation and branching velocities of roots by BAP and light was observed but CIN increased elongation and branching. In rhizotrons cultivated in light and dark conditions with different AIA/CIN ratios, isolated root explants remained inactive; however, roots attached to a meristematic base presented a significant increase in growth development, with values comparable to those of roots attached to seedlings cultivated in light without hormones. The results revealed that six adventitious rhizogenic root lines with basal meristems have the potential for use in a wide range of environmental and innovative applications in phytotreatment technologies involving eutrophicated water. Full article

Salt marsh vegetation in the Yellow River Delta, including Phragmites australis (P. australis), Suaeda salsa (S. salsa), and Tamarix chinensis (T. chinensis), is essential for the stability of wetland ecosystems. In recent years, salt marsh vegetation has experienced severe degradation, which is primarily due to invasive species and human activities. Therefore, the accurate monitoring of the spatial distribution of these vegetation types is critical for the ecological protection and restoration of the Yellow River Delta. This study proposes a multi-source remote sensing data fusion method based on Sentinel-1 and Sentinel-2 imagery, integrating the temporal characteristics of optical and SAR (synthetic aperture radar) data for the classification mapping of salt marsh vegetation in the Yellow River Delta. Phenological and polarization features were extracted to capture vegetation characteristics. A random forest algorithm was then applied to evaluate the impact of different feature combinations on classification accuracy. Combining optical and SAR time-series data significantly enhanced classification accuracy, particularly in differentiating P. australis, S. salsa, and T. chinensis. The integration of phenological features, polarization ratio, and polarization difference achieved a classification accuracy of 93.51% with a Kappa coefficient of 0.917, outperforming the use of individual data sources. Full article

Summary Statement: A tailored image analysis workflow was applied to quantify cortical organoid health, development, morphology and cellular composition over time. The assessment of cellular composition and viability of stem cell-derived organoid models is a complex but essential approach to understanding the mechanisms of human development and disease. Aim: Our study was motivated by the need for an image-analysis workflow, including high-cell content, high-throughput methods, to measure the architectural features of developing organoids. We assessed stem cell-derived cortical organoids at 4 and 6 months post-induction using immunohistochemistry-labelled sections as the analysis testbed. The workflow leveraged fluorescence imaging tailored to classify cells as viable and dying or non-viable and assign neuronal and astrocytic perinuclear markers to count cells. Results/Outcomes: Image acquisition was accelerated by capturing the organoid slice in 3D using widefield-fluorescence microscopy. This method used computational clearing to resolve nuclear and perinuclear markers and retain their spatial information within the organoid’s heterogeneous structure. The customised workflow analysed over 1.5 million cells using DAPI-stained nuclei, filtering and quantifying viable and non-viable cells and the necrotic-core regions. Temporal analyses of neuronal cell number derived from perinuclear labelling were consistent with organoid maturation from 4 to 6 months of in vitro differentiation. Overall: We have provided a comprehensive and enhanced image analysis workflow for organoid structural evaluation, creating the ability to gather cellular-level statistics in control and disease models. Full article

Addressing global warming and adapting to the impacts of climate change is a primary focus of climate change adaptation strategies at both European and national levels. Land surface temperature (LST) is a widely used proxy for investigating climate-change-induced phenomena, providing insights into the surface radiative properties of different land cover types and the impact of urbanization on local climate characteristics. Accurate and continuous estimation across large spatial regions is crucial for the implementation of LST as an essential parameter in climate change mitigation strategies. Here, we propose a deep-learning-based methodology for LST estimation using multi-source data including Sentinel-2 imagery, land cover, and meteorological data. Our approach addresses common challenges in satellite-derived LST data, such as gaps caused by cloud cover, image border limitations, grid-pattern sensor artifacts, and temporal discontinuities due to infrequent sensor overpasses. We develop a regression-based convolutional neural network model, trained on ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) mission data, which performs pixelwise LST predictions using 5 × 5 image patches, capturing contextual information around each pixel. This method not only preserves ECOSTRESS’s native resolution but also fills data gaps and enhances spatial and temporal coverage. In non-gap areas validated against ground truth ECOSTRESS data, the model achieves LST predictions with at least 80% of all pixel errors falling within a ±3 °C range. Unlike traditional satellite-based techniques, our model leverages high-temporal-resolution meteorological data to capture diurnal variations, allowing for more robust LST predictions across different regions and time periods. The model’s performance demonstrates the potential for integrating LST into urban planning, climate resilience strategies, and near-real-time heat stress monitoring, providing a valuable resource to assess and visualize the impact of urban development and land use and land cover changes. Full article

Grid-cell data are increasingly used in research due to the growing availability and accessibility of remote sensing products. However, grid-cell data often fails to represent the actual decision-making unit, leading to biased estimates in socio-economic analysis. To this end, this paper presents a comprehensive parcel-level dataset for Salt Lake County, Utah, spanning from 2008 to 2018. This dataset combines detailed spatial and temporal data on land ownership, land use, and preferential farmland tax assessments under the Greenbelt program. Compiled from multiple geospatial sources, the dataset includes nearly 200,000 parcel-year observations, providing valuable insights into landowner decision-making and the impact of tax abatement incentives at the decision-making level. This resource is beneficial for researchers, educators, and practitioners in sustainable development, environmental studies, and farmland conservation. Full article

Background/Objectives: Mental representation of spatial information relies on egocentric (body-based) and allocentric (environment-based) frames of reference. Research showed that spatial memory deteriorates as Alzheimer’s disease (AD) progresses and that allocentric spatial memory is among the earliest impaired areas. Most studies have been conducted in static situations despite the dynamic nature of real-world spatial processing. Thus, this raises the question: Does temporal order affect spatial memory? The present study, by adopting a dynamic spatial memory task, explored how the temporal order of item presentation influences egocentric and allocentric spatial judgments in individuals with early-stage Alzheimer’s disease (eAD) and healthy elderly individuals (normal controls—NC). Method: Participants were required to memorize dyads of simple 3D geometrical objects presented one at a time on a desk along with a bar. Afterwards, they had to choose what stimulus appeared either closest to them (egocentric judgment) or closest to the bar (allocentric judgment). Results: Results revealed that the temporal order significantly affected spatial judgments in eAD patients but not in NC participants. While eAD patients remain anchored to the item presented first, which is more accurate regardless of the frame used, NC are equally accurate with the item that appears first or second. This is presumably because eAD patients struggle to flexibly shift attention and update spatial representations in dynamic situations, which leads to reliance on initial information and difficulties with information presented later. Conclusions: This highlights the importance of further understanding the cognitive strategies employed by AD patients. Full article