Stream Network Research Articles

Characterizing Space‐Time Channel Network Dynamics in a Mediterranean Intermittent Catchment of Central Italy Combining Visual Surveys and Cameras

AbstractNon‐perennial streams have a global prevalence, but quantitative knowledge of the temporal dynamics of their flowing length—namely the extent of the wet portion of the stream network—remains limited, as the monitoring of the spatiotemporal configuration of wet channels is challenging in most settings. This work combines the high spatial resolution of visual surveys and the high temporal resolution of camera‐based approaches to reconstruct the space‐time stream network dynamics in a 3.7 km2 Mediterranean catchment of central Italy. Information on the hydrological status of the stream network derived from 40 field surveys and sub‐hourly images collected with 21 stage‐cameras are combined exploiting the hierarchical principle. The latter postulates the existence of a Bayesian chain, defined from the local persistence of the nodes that dictates their wetting/drying order during expansion/retraction cycles of the flowing stream network. Our results highlight the complexity of network dynamics in the study area: while the number of wet nodes decreases during the dry season and increases during the wet season, the local persistency exhibits a highly heterogeneous non‐monotonic spatial pattern, originating a dynamically disconnected network. Despite this heterogeneity, the hierarchical model well approximates the temporal evolution of the state of the network nodes, with an accuracy that exceeds 99%. Crucially, the model allows the reconstruction of the wet portion even in cases in which part of the network was not observed. This work provides a novel conceptual approach for the reconstruction of the wet portion of the network in poorly accessible sites.

Changes in Water Age During Dry‐Down of a Non‐Perennial Stream

AbstractNon‐perennial streams, which lack year‐round flow, are widespread globally. Identifying the sources of water that sustain flow in non‐perennial streams is necessary to understand their potential impacts on downstream water resources, and guide water policy and management. Here, we used water isotopes (δ18O and δ2H) and two different modeling approaches to investigate the spatiotemporal dynamics of young water fractions (Fyw) in a non‐perennial stream network at Konza Prairie (KS, USA) during the 2021 summer dry‐down season, as well as over several years with varying hydrometeorological conditions. Using a Bayesian model, we found a substantial amount of young water (Fyw: 39.1–62.6%) sustained flows in the headwaters and at the catchment outlet during the 2021 water year, while 2015–2022 young water contributions estimated using sinusoidal models indicated smaller Fyw amounts (15.3% ± 5.7). Both modeling approaches indicate young water releases are highly sensitive to hydrological conditions, with stream water shifting to older sources as the network dries. The shift in water age suggests a shift away from rapid fracture flow toward slower matrix flow that creates a sustained but localized surface water presence during late summer and is reflected in the annual dynamics of water age at the catchment outlet. The substantial proportion of young water highlights the vulnerability of non‐perennial streams to short‐term hydroclimatic change, while the late summer shift to older water reveals a sensitivity to longer‐term changes in groundwater dynamics. Combined, this suggests that local changes may propagate through non‐perennial stream networks to influence downstream water availability and quality.

Research on Piano Curriculum Education and Its Performance Ecosystem Based on Network Flow Optimization

Abstract This paper investigates music education, where an efficient and accurate performance evaluation system in the piano teaching and performance ecosystem is increasingly becoming an essential tool for improving teaching quality and performance level. The objective evaluation of students’ performance skills can be achieved by carefully analyzing piano performances using the network flow optimization technique. This technique optimizes the performance evaluation system’s audio recognition ability by analyzing the piano audio signal and solving the multi-constraint nonlinear optimization problem in a limited time domain. This paper establishes a network flow optimization model, applies the multi-constraint nonlinear optimization technique, and combines the non-negative matrix decomposition and dynamic time regularization algorithm to analyze the piano performance for experiments. After optimization processing, hundreds of piano audio samples were collected, and the audio recognition accuracy was improved by 20%. By optimizing and processing the audio signals from the network stream, the evaluation system could detect polyphony more accurately and track the musical score effectively, improving accuracy and efficiency. Using the non-negative matrix decomposition algorithm, the accuracy of detecting polyphony can reach 85%, while the dynamic temporal regularization algorithm can match the position of the musical score with 95% accuracy. The accuracy of piano performance evaluation is optimized by this network flow optimization method, providing new technical means for music education, and promoting the quality of teaching and performance.

Optimizing Video Streaming in Dynamic Networks: An Intelligent Adaptive Bitrate Solution Considering Scene Intricacy and Data Budget

Optimizing Video Streaming in Dynamic Networks: An Intelligent Adaptive Bitrate Solution Considering Scene Intricacy and Data Budget

Stories and Data: Australian Musicians Navigating the Spotify for Artists Platform

ABSTRACT Spotify offers users powerful algorithmic recommendations and hybrid “algo-torial” curation at scale. In this paper we argue that juxtaposed within this data-driven platform, is the power of human stories and networking. We investigate the experiences of 10 Australian musicians through semi-structured interviews, asking how they navigate the Spotify for Artists interface. By attending to the experiences of the artists themselves, and their use of the Spotify for Artists platform, we explore the human stories that flow within this socio-technical music streaming network, arguing that stories, like data, have an important role and value in this context.

Lightweight Dual Stream Network With Knowledge Distillation for RGB-D Scene Parsing

Lightweight Dual Stream Network With Knowledge Distillation for RGB-D Scene Parsing

Development of Network Streaming System for CGH Video in Wired/Wireless Communications

Development of Network Streaming System for CGH Video in Wired/Wireless Communications

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Predicting future frames and improving inter-frame prediction are ongoing challenges in the field of video streaming. By creating a novel framework called STreamNet (Spatial-Temporal Video Coding), fusing bidirectional long short-term memory with temporal convolutional networks, this work aims to address the issue at hand. The development of STreamNet, which combines spatial hierarchies with local and global temporal dependencies in a seamless manner, along with sophisticated preprocessing, attention mechanisms, residual learning, and effective compression techniques, is the main contribution. Significantly, STreamNet claims to provide improved video coding quality and efficiency, making it suitable for next-generation networks. STreamNet has the potential to provide reliable and optimal streaming in high-demand network environments, as shown by preliminary tests that show a performance advantage over existing methods.

Quantification of bedrock structural controls of longitudinal sediment connectivity using the probability of connectivity and sediment continuity model

The structural controls of sediment connectivity are poorly understood in low-gradient watersheds and stream networks. We developed a field- and modeling-based framework to understand the timescales and thresholds associated with the structural controls of sediment connectivity and continuity in the Inner Bluegrass region of Kentucky, USA. Field reconnaissance was carried out to assess stream morphology and sediment connectivity, which was leveraged to derive instream sediment connectivity indices for 181 pool-riffle sequences in the low-gradient bedrock stream network. Our model framework coupled a probability-based connectivity formulation with the sediment continuity formula to assess sediment transport and connectivity in the stream network. Results indicated that the bedrock system reflects an end-member in which lithology controls longitudinal stream morphology. Specifically, we found that regional morphology controls pool-riffle length scales via bedrock outcrops, which therein controls the timescales of sediment connectivity over multiple hydrologic regimes. The pool to riffle length ratio was found to be 4.6, an order of magnitude greater than the ratio for alluvial systems. The new formulation of the sediment continuity formula coupled with the probability of connectivity framework adequately captured both short-term and long-term continuous dynamics of sediment transport, as indicated by evaluation metrics with measured sediment flux and field reconnaissance. Based on the analysis of 362 riffles and pools, we found that sediment in pools upstream of bedrock outcrop barriers are disconnected 93 % of the time whereas riffles are connected and allow full continuity 99 % of the time. Longitudinal sediment connectivity within the stream network was found to be controlled by both functional connectivity and structural connectivity, which contrasts recent findings describing the controls of sediment connectivity over lateral pathways on hillslopes.

Towards heart infarction detection via image-based dataset and three-stream fusion framework

As the global ageing society intensifies, many elderly people are facing the situation of living alone without care. When sudden illnesses like heart attacks or heart infarctions happen, they might not be able to get prompt and efficient treatment due to not being noticed in time, which can be fatal. To alleviate the above issue, this paper presents a Heart Infarction Image (HII) dataset, an image-based benchmark for heart attack/infarction identification tasks. The dataset contains images of people suffering from heart infarction or normal conditions. In addition, we propose a methodological framework based on this dataset that utilizes convolutional neural networks to identify multidimensional features of potential patients through three network streams in order to quickly and efficiently predict possible heart attack cases and provide early warning. We also present a Guided Adaptive Weight Fusion (GAWF) mechanism in the feature fusion stage. Numerous qualitative and ablation analyses corroborate the necessity of our dataset and framework. Our method achieves an accuracy of 90.01%, indicating that it performs well in recognizing acute heart disease in real-world settings. At the same time, systematic experiments show that using lightweight models under the proposed framework can also achieve good results, which allows our method to be extended to mobile intelligence platforms.

Enhanced morphometric analysis for soil erosion susceptibility mapping in the Godavari river basin, India: leveraging Google Earth Engine and principal component analysis

ABSTRACT Morphometric analysis of a drainage basin along with its stream network is indispensable for analysing a river basin’s prevailing geomorphology, geo-hydrology, and erosional susceptibility. In this study, an enhanced morphometric evaluation method using Google Earth Engine (GEE) and Principal Component Analysis (PCA) was used to assess and prioritize 12 sub-basins of the Godavari River basin (GRB), India, based on their erosional susceptibility. The findings of erosional susceptibility were validated using the Sediment Yield (SY) model. Our findings highlight that the sub-basins of Indravati, Pranhita, and Manjira, i.e. 42.42% (132707.4 km2) of the entire GRB are at high risk of erosional susceptibility, requiring immediate implementation of soil conservation measures. Our findings also highlight that 52.71% of the GRB is in the mature stage of geological evolution, wherein erosional landform-building processes are still ongoing. Overall, GRB has a relatively low drainage density with dendritic drainage patterns, permeable soils, and elongated sub-basins of enhanced erosional susceptibility. Moreover, the results of the SY model exhibited 90% agreement with the results of the enhanced morphometric analysis. This study presents an efficient framework for enhanced morphometric analysis that can assist policymakers in the demarcation of priority zones for erosional susceptibility.

Recognizing the ephemeral stream floodplain: Identification and importance of flood zones in drylands

AbstractPrevious syntheses of the hydrologic, geomorphic and biotic functions of floodplains have largely integrated research conducted along perennial stream networks, despite a growing body of literature on analogous functional floodplain zones along ephemeral streams. Although variable inundation characteristic of ephemeral streams complicates the delineation between channel and floodplain, ephemeral stream floodplains are distinct landforms that are hypothesized to host similar ecosystem functions as perennial stream floodplains, including the attenuation of downstream fluxes and provision of habitat to diverse flora and fauna. Here, we review the literature on dryland ephemeral stream floodplains in order to identify: (1) common definitions and styles; (2) unique hydrologic, geomorphic and biotic functions separate from uplands or channels; (3) current and future anthropogenic and natural stressors to such functions; and (4) knowledge gaps regarding research on ephemeral stream floodplains. Although active floodplain definitions follow criteria commonly used in perennial stream corridors, the extent of ephemeral stream floodplains is typically defined by less frequent floods. A set of floodplain styles emerges from the literature dependent on confinement and the presence of channelized streamflow. Functions related to attenuation and storage are typically concentrated in unconfined, channelled floodplains. The temporary storage of sediment and subsurface water in ephemeral stream floodplains makes them disproportionately important for biogeochemical cycling and hosts to richer, denser and more diverse vegetation communities compared with surrounding uplands. Many functions of ephemeral stream floodplains are also found in perennial counterparts, but variable flow regimes and sediment loads in ephemeral streams can potentially impact rates and magnitudes of comparable processes and functions. Future research needs to incorporate a holistic view of the ephemeral stream corridor—including channels and floodplains—to better understand current and future fluvial processes and cascading impacts in drylands globally.

Scales of Connectivity within Stream Temperature Networks of the Clackamas River Basin, Oregon

Water quality varies along the stream network; thus, considering the directional, dendritic nature of stream networks with surrounding landscape variables is essential in explaining spatial variations of water quality. Using a spatially extensive stream temperature monitoring effort in the Clackamas River Basin in the United States, we first compare spatial scales of analysis of atmospheric, landscape, and in-stream explanatory variables through their correlation with summer stream temperatures. We then derive a predictive stream temperature model with factors representing the spatial variation of local climate, recent wildfire effects, and discharge. Finally, we compare nonspatial multiple linear regression to a spatial stream network (SSN) model to assess the combined importance of the spatial scale of analysis and flow-connected stream distance in explaining total variation in stream temperatures. Most explanatory variables show the most highly significant relationships to stream temperature when derived as a percentage of the total upstream area above observation sites. Elevation and vegetation cover, however, were most significantly correlated to stream temperature at the riparian buffer area scale and the local reach contributing area scale, respectively. Multiple regression analysis using total upstream burned area, total upstream area with underlying High Cascades geology, and the elevation within the 100-m-wide riparian area explained 81 percent of variation in stream temperature. SSN outperformed this nonspatial statistical model, however, in explaining the total variation in stream temperature. These comparisons of scaled data sets demonstrate both the local and cumulative upstream effects on stream temperature, providing a spatial network-informed framework to those prioritizing watershed restoration and wildfire recovery activities.

Channel change during catastrophic flood: Example of Storm Alex in the Vésubie and Roya valleys

Documenting and interpreting channel responses to catastrophic floods help understanding how rapid fluvial metamorphosis can propagate in a catchment under sediment cascading effects. The recent example of the October 2020 Storm Alex in SE France (∼500 mm of rain in 24 h) provides a unique opportunity to investigate major geomorphic responses along 70 km of two confined alpine valleys (Vésubie and Roya) and to link them to sediment wave initiation and propagation. GIS-based analysis of remote sensing data (high-resolution ortho-imagery and airborne LiDAR data) acquired before and after the flood allowed combining channel changes with sediment erosion and deposition along a 35-km reach of the Vésubie, including the most impacted portions of the valley. In the Roya, the analysis was restricted to 2D morphological changes reconstructed with the sequence of ortho-imagery. Archives of aerial imagery were also used to integrate the storm impact in the historical trajectory of the rivers. The reconstruction of geomorphic responses shows a quasi-continuous fluvial metamorphosis along the investigated stream networks, with dramatic active channel widening and aggradation, having no antecedent analogs during the last 70 years in both valleys. The different glacial imprints between the two valleys are considered a key factor explaining the exacerbated channel response in the Vésubie, where a braided channel emerged along a 35-km river length. Many evidences strongly support that the fuelling effect of alluvial storage is a key element of the sediment cascade at the origin of the braided channel formation. This regional case study allows us to discuss the critical role of floodplain and terrace erosion in the formation of the post-flood braided channel, and to compare the geomorphic impact of the storm with similar reported cases in the literature.

A One-Pass Clustering Based Sketch Method for Network Monitoring

Network monitoring solutions need to cope with increasing network traffic volumes, as a result, sketch-based monitoring methods have been extensively studied to trade accuracy for memory scalability and storage reduction. However, sketches are sensitive to skewness in network flow distributions due to hash collisions, and need complicated performance optimization to adapt to line-rate packet streams. We provide Jellyfish, an efficient sketch method that performs one-pass clustering over the network stream. One-pass clustering is realized by adapting the monitoring granularity from the whole network flow to fragments called subflows, which not only reduces the ingestion rate but also provides an efficient intermediate representation for the input to the sketch. Jellyfish provides the network-flow level query interface by reconstructing the network-flow level counters by merging subflow records from the same network flow. We provide probabilistic analysis of the expected accuracy of both existing sketch methods and Jellyfish. Real-world trace-driven experiments show that Jellyfish reduces the average estimation errors by up to six orders of magnitude for per-flow queries, by six orders of magnitude for entropy queries, and up to ten times for heavy-hitter queries.

Alpha diversity of planktonic organisms across stream networks associated with mining activities in Southeast Sulawesi

Planktonic organisms are the main biotic components of the food chain of aquatic ecosystems. Little is known about the impact of multiple stressors on plankton community response in mining areas. This study aimed to elucidate the alpha diversity of zooplankton and phytoplankton in different stream networks associated with mining activities in the Molore watershed, Southeast Sulawesi. The research was carried out by taking samples for water quality and plankton analyses in four streams in November 2021 and November 2022. A rarefaction curve approach was used to assess the species richness and diversity trends of the region. Streams were clustered using the Bray-Curtis similarity index of alpha diversity indices, such as the Shannon-Wiener, Simpson, Margalef, Pielou, and Berger-Parker indices. We found six species of zooplankton only in three sites and 21 species of Phytoplankton in the class of Bacillariophyceae, Chlorophyceae, Cyanophyceae, Dinophyceae, and Euglenophyceae across the landscape gradient. The present study showed that the lowest value of the Shannon-Wiener diversity of the plankton community was 0.48, while the maximum expected value based on individuals’ rarefaction curve was 2.24. Species richness is in line with cluster analysis, which shows the similarity of stream groups according to the level of environmental pressure, indicating a valuable ecological indicator for stream management.

A Preliminary Study of Brain Functional Magnetic Resonance Imaging in Text Reading and Comprehension.

Few studies have focused on the changes in human brain function activities caused by reading Chinese characters with different intelligibility and whether it can reflect the understanding and cognitive ability of the human brain. Task-fMRI based on Chinese character reading tasks with different intelligibility was used to explore activated brain regions and their cognitive changes. Volunteers were randomly recruited using advertisements. Forty volunteers were recruited based on strict inclusion and exclusion criteria, and 40 volunteers were recruited. Brain function data of 40 healthy right-handed volunteers in fuzzy/clear Chinese reading tasks were collected using a Siemens Skyra 3.0T magnetic resonance scanner. Data were preprocessed and statistically analyzed using the statistical software SPM12.0 to observe the activation of the cortex and analyze its characteristics and possible changes in cognitive function. Task-fMRI analysis: (1) The main brain regions activated in fuzzy/clear reading tasks were located in the occipital visual cortex (P < 0.001); (2) a paired sample t-test suggested that there was a significant difference in BOLD signals in the brain regions activated by fuzzy/clear reading tasks (P < 0.001, equiv Z = 4.25). Compared with the fuzzy reading task, the brain regions more strongly activated in the clear reading task were mainly located in the right superior frontal gyrus and the bilateral temporal lobe. Compared with the clear reading task, the brain region that was more strongly activated in the fuzzy reading task was mainly located in the right fusiform gyrus. Clear Chinese character information mainly activates the dorsal stream of the visual-spatial network. This reflects the information transmission of the brain after understanding the text content and is responsible for guiding and controlling attention. Fuzzy words that cannot provide clear text content activate the fusiform gyrus of the ventral stream of the visual-spatial network, strengthening the function of orthographic processing.

Learning to Adapt Adversarial Perturbation Consistency for Domain Adaptive Semantic Segmentation of Remote Sensing Images

Semantic segmentation techniques for remote sensing images (RSIs) have been widely developed and applied. However, most segmentation methods depend on sufficiently annotated data for specific scenarios. When a large change occurs in the target scenes, model performance drops significantly. Therefore, unsupervised domain adaptation (UDA) for semantic segmentation is proposed to alleviate the reliance on expensive per-pixel densely labeled data. In this paper, two key issues of existing domain adaptive (DA) methods are considered: (1) the factors that cause data distribution shifts in RSIs may be complex and diverse, and existing DA approaches cannot adaptively optimize for different domain discrepancy scenarios; (2) domain-invariant feature alignment, based on adversarial training (AT), is prone to excessive feature perturbation, leading to over robust models. To address these issues, we propose an AdvCDA method that guides the model to adapt adversarial perturbation consistency. We combine consistency regularization to consider interdomain feature alignment as perturbation information in the feature space, and thus propose a joint AT and self-training (ST) DA method to further promote the generalization performance of the model. Additionally, we propose a confidence estimation mechanism that determines network stream training weights so that the model can adaptively adjust the optimization direction. Extensive experiments have been conducted on Potsdam, Vaihingen, and LoveDA remote sensing datasets, and the results demonstrate that the proposed method can significantly improve the UDA performance in various cross-domain scenarios.

Multi-Stream Fusion Network for Skeleton-Based Construction Worker Action Recognition.

The global concern regarding the monitoring of construction workers' activities necessitates an efficient means of continuous monitoring for timely action recognition at construction sites. This paper introduces a novel approach-the multi-scale graph strategy-to enhance feature extraction in complex networks. At the core of this strategy lies the multi-feature fusion network (MF-Net), which employs multiple scale graphs in distinct network streams to capture both local and global features of crucial joints. This approach extends beyond local relationships to encompass broader connections, including those between the head and foot, as well as interactions like those involving the head and neck. By integrating diverse scale graphs into distinct network streams, we effectively incorporate physically unrelated information, aiding in the extraction of vital local joint contour features. Furthermore, we introduce velocity and acceleration as temporal features, fusing them with spatial features to enhance informational efficacy and the model's performance. Finally, efficiency-enhancing measures, such as a bottleneck structure and a branch-wise attention block, are implemented to optimize computational resources while enhancing feature discriminability. The significance of this paper lies in improving the management model of the construction industry, ultimately aiming to enhance the health and work efficiency of workers.

Real-Time Network Video Data Streaming in Digital Medicine

Today, the use of digital medicine is becoming more and more common in medicine. With the use of digital medicine, health data can be shared, processed, and visualized using computer algorithms. One of the problems currently facing digital medicine is the rapid transmission of large amounts of data and their appropriate visualization, even in 3D. Advances in technology offer the possibility to use new image processing, networking, and visualization solutions for the evaluation of medical samples. Because of the resolution of the samples, it is not uncommon that it takes a long time for them to be analyzed, processed, and shared. This is no different for 3D visualization. In order to be able to display digitalized medical samples in 3D at high resolution, a computer with computing power that is not necessarily available to doctors and researchers is needed. COVID-19 has shown that everyday work must continue even when there is a physical distance between the participants. Real-time network streaming can provide a solution to this, by creating a 3D environment that can be shared between doctors/researchers in which the sample being examined can be visualized. In order for this 3D environment to be available to everyone, it must also be usable on devices that do not have high computing capacity. Our goal was to design a general-purpose solution that would allow users to visualize large amounts of medical imaging data in 3D, regardless of the computational capacity of the device they are using. With the solution presented in this paper, our goal was to create a 3D environment for physicians and researchers to collaboratively evaluate 3D medical samples in an interdisciplinary way.