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Managing diffuse pollution from agricultural land requires a spatially explicit risk assessment that can be applied over large areas. Major components of such assessments are the precise definition of both channel networks that often originate as small channels and streams, and Hydrologically Sensitive Areas (HSAs) of storm runoff that occur on land surfaces. Challenges relate to regions of complex topography and land use patterns, particularly those which have been heavily modified by arterial drainage. In this study, a national scale, transferrable workflow and analysis were developed using a specifically commissioned LiDAR survey. Research on the first half of Northern Ireland (6927 km2) is reported where field-edge drain to major river channels were mapped from 1 m (16 points per metre) digital terrain models, and in-field HSAs were defined across over 400,000 fields with a median field size of 0.86 ha. Manual drainage mapping supplemented with a novel automated drainage channel correction process resulted in an unparalleled high-resolution national drainage network with 37,320 km of channels, increasing mapped channel density from 0.9 km km-2 to 5.5 km km-2. The HSAs were based on a Soil Topographic Index (STI) system using hillslope and contributing area models combined with soil hydraulic characteristics. In all, 249 km2 of runoff risk HSAs were identified by extracting the top 95th percentile of the modelled STI as the areas with the highest propensity to generate in-field runoff. At field and individual farm scale these targeted risk maps of diffuse pollution were delivered to over 13,000 farmers and form part of the nationwide Soil Nutrient Health Scheme programme.

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The study developed a memory task training system using functional near-infrared spectroscopy (fNIRS) and neurofeedback mechanisms, and acquired and analyzed subjects' EEG signals. The results showed that subjects participating in the neurofeedback task had higher correlated brain network node degrees and average cluster coefficients in the right hemisphere brain region of the prefrontal lobe, with relatively lower dispersion of mediator centrality. In addition, the subjects' left hemisphere brain region of the prefrontal lobe section had increased centrality in the neurofeedback task. Classification of brain data by the channel network model and the support vector machine model showed that the classification accuracy of both models was higher in the task state and resting state than in the feedback task and the control task, and the classification accuracy of the channel network model was higher. The results suggested that subjects in the neurofeedback task had distinct brain data features and that these features could be effectively recognized.

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Proton exchange membranes are an essential component of proton-exchange membrane fuel cells (PEMFC). Their performance is directly related to the development of ionic channel networks through hydration. Current sensing atomic force microscopy (CSAFM) can map the local conductance and morphology of a sample surface with sub-nano resolution simultaneously by applying a bias voltage between the conducting tip and sample holder. In this study, the ionic channel network variation of Nafion by hydration has been quantitatively characterized based on the basic principles of electrodynamics and CSAFM. A nano-sized PEMFC has been created using a Pt-coated tip of CSAFM and one side Pt-coated Nafion, and studied under different relative humidity (RH) conditions. The results have been systematically analyzed. First, the morphology of PEMFC under each RH has been studied using line profile and surface roughness. Second, the CSAFM image has been analyzed statistically through the peak value and full-width half-maximum of the histograms. Third, the number of protons moving through the ionic channel network (NPMI) has been derived and used to understand ionic channel network variation by hydration. This study develops a quantitative method to comprehend variations in the ionic channel network by calculating the movement of protons into the ionic channel network based on CSAFM images. To verify the method, a comparison is made between the NPMI and the changes in proton conductivity under different RH conditions and it reveals a good agreement. This developed method can offer a quantitative approach for characterizing the morphological structure of PEM. Also, it can provide a quantitative tool for interpretating CSAFM images.

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Tidal channel networks, which characterize all river deltas, control the exchange of water and nutrients (hydrological connectivity) between the ocean and the delta area. Therefore, a tidal channel network in optimal conditions ensures the maintenance of the diversity and stability of the deltaic ecosystem. However, the developmental status of channel networks in the Yellow River Delta, China, has not been clearly determined. Here, we selected a typical tidal channel network in this delta that showed different spatial patterns (e.g., connectivity attributes) in the past three decades and explored its evolution using entropy as an index of connectivity. Seven scenarios were set up to determine the optimal status of the tidal channel network by optimizing its structure. The optimization effect was evaluated by comparing the connectivity attributes of the channel network before and after optimization. The results showed that the network experienced two obviously different developmental phases: an evolution before 2005 and a regression after 2005. Mann-Kendall analysis indicated that the channel network achieved dynamic stability before 2014 and became unstable thereafter. The simulations conducted to optimize the system showed that adding outlets changed the current patterns of the network' structural and functional connectivity. As the optimization proceeded, structural connectivity increased while functional connectivity decreased, and the tidal channel network tended to be dynamically stable. Our study elucidated the quantitative relationship between outlet number and stability within tidal channel networks, providing reference information that could be incorporated into future projects for the restoration and management of river deltas.

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Abnormal gait recognition is important for detecting body part weakness and diagnosing diseases. The abnormal gait hides a considerable amount of information. In order to extract the fine, spatial feature information in the abnormal gait and reduce the computational cost arising from excessive network parameters, this paper proposes a double-channel multiscale depthwise separable convolutional neural network (DCMSDSCNN) for abnormal gait recognition. The method designs a multiscale depthwise feature extraction block (MDB), uses depthwise separable convolution (DSC) instead of standard convolution in the module and introduces the Bottleneck (BK) structure to optimize the MDB. The module achieves the extraction of effective features of abnormal gaits at different scales, and reduces the computational cost of the network. Experimental results show that the gait recognition accuracy is up to 99.60%, while the memory size of the model is reduced 4.21 times than before optimization.

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This study investigates the relations between the shape of hydrologic responses and the dynamic transport properties of channel networks within the framework of random walks on fractal networks, focusing on the shape parameter of Nash model. To this end, we evaluate the static fractal structures and the dynamic transport properties of various channel networks and, then, validate Liu's conjecture (1992) for the shape of hydrologic responses. In the context of random walks on fractal networks, the fractal dimensions of channel networks can directly connect the static structure to the dynamic transport properties of channel networks through Horton's law of drainage composition. It is observed that the peak coordinates of hydrologic responses would have a more intimate relation to the connectivity of channel networks than the conductivity of those. The characteristic times of hydrologic responses also tend to be related to the connectivity of channel networks. Thereby, the shape of hydrologic responses would be expected directly affected by the fractal dimension of channel networks in terms of their static structure, while interpreted a combined result of the conductivity and the connectivity of channel networks in terms of their dynamic transport properties. So, the runoff hydrographs of a river basin could be considered shaped by the fractal dimensions of its channel networks following the linear hydrologic system theory.

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Single-modal images carry limited information for features representation, and RGB images fail to detect grass weeds in wheat fields because of their similarity to wheat in shape. We propose a framework based on multi-modal information fusion for accurate detection of weeds in wheat fields in a natural environment, overcoming the limitation of single modality in weeds detection. Firstly, we recode the single-channel depth image into a new three-channel image like the structure of RGB image, which is suitable for feature extraction of convolutional neural network (CNN). Secondly, the multi-scale object detection is realized by fusing the feature maps output by different convolutional layers. The three-channel network structure is designed to take into account the independence of RGB and depth information, respectively, and the complementarity of multi-modal information, and the integrated learning is carried out by weight allocation at the decision level to realize the effective fusion of multi-modal information. The experimental results show that compared with the weed detection method based on RGB image, the accuracy of our method is significantly improved. Experiments with integrated learning shows that mean average precision (mAP) of 36.1% for grass weeds and 42.9% for broad-leaf weeds, and the overall detection precision, as indicated by intersection over ground truth (IoG), is 89.3%, with weights of RGB and depth images at α = 0.4 and ß = 0.3. The results suggest that our methods can accurately detect the dominant species of weeds in wheat fields, and that multi-modal fusion can effectively improve object detection performance.

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Impairment of rivers by elevated phosphorus (P) concentration is an issue often studied at outlets of mesoscale catchments. Our objective was to evaluate within-catchment spatio-temporal processes along connected reaches to understand processes of internal P loading associated with sediment input, accumulations in channels and sediment-water column P exchange. Our overall hypothesis was that heterogeneous sediment residence within the channel of a 52 km2 mixed land cover catchment resulted in key zones for sediment-water P exchange. We evaluated the channel network through ground-survey, spatial data methods establishing connectivity and energy gradients. This gave a background to understand sampling of sediments and P release/uptake to the water column using 90 s in-situ resuspension isolating a portion of streambed over five sets of three-location transects in May (spring storms, recent active erosion) and September (summer low flow, longer sediment residence). Simple transect position models (top, mid, bottom) predicted increased sediment resuspension yields and P contents in lower settings. Sediment P release following resuspension were mean (and range) 0.5 (-0.8 to 1.8) and 0.5 (-2.5 to 3.6) mg soluble reactive P/m2 bed in May and September, respectively, strengthening generally down the transects but inconsistently. Relationships (log form) showed a steepening rise in fine sediments, P content, background and disturbance-released dissolved P, with specific stream power < 40 W/m2. In-situ methods showed sediments dominantly (12 cases May, 13 cases Sep) as P sources capable of influencing dissolved P concentrations and with potential explanation that heterogeneous locations of internal P loading influence the systems longer-term observed P trends. Combining channel network, stream power assessment and in-situ sorption studies improved the understanding of influential zones of sediment-water P exchange within this mesoscale catchment. Such methods have potential to inform P model development and management.

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Large, deep, complex, and severe tissue defects and deformities of the face are the problems encountered in clinical practice. Autologous tissue reconstruction or allograft face transplantation has been adopted but has problems such as blood supply difficulties, collateral damage, immune rejection, and ethical disputes. 3D bioprinting enables personalized tissue regeneration. However, simple hydrogels are prone to collapse during printing, are limited in size, and have poor shape and structure. The present study used three polysaccharide hydrogel composites of nanocellulose, agarose, and sodium alginate with seeded cells as bioinks and polyvinyl alcohol (PVA) as sacrificial material to construct the structures that did not collapse (characteristic parts, such as lips and nose). The nutrient network gradually formed a blood vessel-like structure. The hydrogels prepared using these three polysaccharides have great potential in the construction of personalized, complex, and vascularized tissue-engineered anatomical faces and provide a new strategy for autologous full face reconstruction.

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Several key processes in freshwater ecology are governed by the connectivity inherent to dendritic river networks. These have extensively been analyzed from a geomorphological and hydrological viewpoint, yet structures classically used in ecological modeling have been poorly representative of the structure of real river basins, often failing to capture well-known scaling features of natural rivers. Pioneering work identified optimal channel networks (OCNs) as spanning trees reproducing all scaling features characteristic of natural stream networks worldwide. While OCNs have been used to create landscapes for studies on metapopulations, biodiversity, and epidemiology, their generation has not been generally accessible.Given the increasing interest in dendritic riverine networks by ecologists and evolutionary biologists, we here present a method to generate OCNs and, to facilitate its application, we provide the R-package OCNet. Owing to the stochastic process generating OCNs, multiple network replicas spanning the same surface can be built; this allows performing computational experiments whose results are irrespective of the particular shape of a single river network. The OCN construct also enables the generation of elevational gradients derived from the optimal network configuration, which can constitute three-dimensional landscapes for spatial studies in both terrestrial and freshwater realms. Moreover, the package provides functions that aggregate OCNs into an arbitrary number of nodes, calculate several descriptors of river networks, and draw relevant network features.We describe the main functionalities of the package and its integration with other R-packages commonly used in spatial ecology. Moreover, we exemplify the generation of OCNs and discuss an application to a metapopulation model for an invasive riverine species.In conclusion, OCNet provides a powerful tool to generate realistic river network analogues for various applications. It thereby allows the design of spatially realistic studies in increasingly impacted ecosystems and enhances our knowledge on spatial processes in freshwater ecology in general.

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Automatic extraction of channel networks from topography in systems with multiple interconnected channels, like braided rivers and estuaries, remains a major challenge in hydrology and geomorphology. Representing channelized systems as networks provides a mathematical framework for analyzing transport and geomorphology. In this paper, we introduce a mathematically rigorous methodology and software for extracting channel network topology and geometry from digital elevation models (DEMs) and analyze such channel networks in estuaries and braided rivers. Channels are represented as network links, while channel confluences and bifurcations are represented as network nodes. We analyze and compare DEMs from the field and those generated by numerical modeling. We use a metric called the volume parameter that characterizes the volume of deposited material separating channels to quantify the volume of reworkable sediment deposited between links, which is a measure for the spatial scale associated with each network link. Scale asymmetry is observed in most links downstream of bifurcations, indicating geometric asymmetry and bifurcation stability. The length of links relative to system size scales with volume parameter value to the power of 0.24-0.35, while the number of links decreases and does not exhibit power law behavior. Link depth distributions indicate that the estuaries studied tend to organize around a deep main channel that exists at the largest scale while braided rivers have channel depths that are more evenly distributed across scales. The methods and results presented establish a benchmark for quantifying the topology and geometry of multichannel networks from DEMs with a new automatic extraction tool.

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River deltas contain complex self-organizing channel networks that continuously exchange fluxes of water, matter, energy, and information with their surroundings. The connectivity of these exchange processes plays a crucial role in controlling the evolution and dynamic stability of river deltas. However, connectivity patterns related to tidal channel networks have rarely been studied, especially in the Yellow River Delta (YRD), which is impacted by severe reclamation. Here, we evaluated the potential hydrological connectivity dynamics between the tidal channel network and its surroundings using an index of connectivity (IC) in the whole YRD and its three sub-regions: erosion zone, oil field zone and deposition zone. The results suggested that different areas had different spatial connectivity potential. The mean value of the IC related to the channel networks showed little difference for any zones. However, the total connectivity response area (CRA; set of connectivity response units) varied with the study scale. A decreasing trend was found on the delta scale and a relatively stable trend was found in the deposition zone. In terms of dynamic connectivity, the tidal flat system did not show a continuous trend over time. Our results indicated that the YRD is such a dynamic complex that a relatively stable connectivity pattern is unlikely to be achieved over time. Therefore, future ecological restoration based on hydrological connectivity needs to consider more related influencing factors and their temporal and spatial dynamics.

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BACKGROUND: Cells cannot survive in the area 200 µm away from nutrients in vitro. Vascular network construction is crucial for thick tissue and organ regeneration in tissue engineering. Coaxial cell printing provides a new way to construct vascular-like channels in vitro. OBJECTIVE: To optimize the coaxial cell printing performance of bioink and to build the tissue-engineered scaffolds with vascular-like structure. METHODS: The aseptic sodium alginate solution was prepared by intermittent pasteurization and then frozen. Freeze-dried powder of aseptic silk fibroin was prepared from degummed silk and sealed. The thawed sodium alginate solution was added to the silk fibroin protein freeze-dried powder and human umbilical vein endothelial cells were added to prepare the bioink. The outer axis of the biological three-dimensional printer was connected with the bioink, and the inner axis was connected with the crosslinking agent. The scaffolds were prepared by coaxial printing, and performed by optical coherence tomography, scanning electron microscopy observation and tensile test. Coaxial scaffolds were made by freeze-preserved sodium alginate solution for 7 days with human umbilical vein endothelial cells. Coaxial scaffolds were also made by freeze-dried sodium alginate solution for 7 days with human umbilical vein endothelial cells and silk fibroin protein sealed for 6 months. The cell survival rate was detected by dead and alive staining after 24 hours of culture in vitro. Vascular-like scaffolds with series and parallel structures were designed and printed. The cell proliferation was detected after 1, 3, 7, 10, and 14 days of culture. RESULTS AND CONCLUSIONS: (1) The optical coherence tomography showed that the maximum printing height of the bioink was 9 layers and the overall thickness was about 4.4 mm. Scanning electron microscopy showed that the outer wall of hollow fiber-filament of vascular-like scaffolds presented irregular strip-shaped crimp with micron-scale internal connected pore structure, while the inner wall of hollow fiber-filament had denser pore structure. (2) The elastic modulus of silk protein freeze-dried scaffold was higher than that of sodium alginate solution (P < 0.05). (3) The cell survival rate of scaffolds treated with sodium alginate solution for 7 days was (86.7±3.4)%, and that of scaffolds treated with silk protein freeze-dried powder for 7 days was (98.1±1.2)%, indicating that the sodium alginate solution freeze- preserved for 7 days was free of bacteria and the shelf-life of silk protein could be up to 6 months. (4) The proliferation activity of cells cultured with parallel structure for 7, 10, and 14 days was higher than that with series structure (P < 0.05). (5) These results imply that the scaffolds have good biocompatibility and mechanical properties.

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Shoal margin collapses of several million cubic meters have occurred in the Western Scheldt estuary, the Netherlands, on average five times a year over the last decades. While these collapses involve significant volumes of material, their effect on the channel-shoal morphology is unknown. We hypothesize that collapses dynamicize the channel-shoal interactions, which could impact the ecological functioning, flood safety, and navigation in the estuary. The objective is to investigate how locations, probability, type, and volume of shoal margin collapse affect the channel-shoal dynamics. We implemented an empirically validated parameterization for shoal margin collapses and tested its effect on simulated estuary morphological development in a Delft3D schematization of the Western Scheldt. Three sets of scenarios were analyzed for near-field and far-field effects on flow pattern and channel-shoal morphology: (1) an observed shoal margin collapse of 2014, (2) initial large collapses on 10 locations, and (3) continuous collapses predicted by our novel probabilistic model over a time span of decades. Results show that a single shoal margin collapse only affects the local dynamics in the longitudinal flow direction and dampen out within a year for typical volumes, whereas larger disturbances that reach the seaward or landward sill at tidal channel junctions grow. The direction of the strongest tidally averaged flow determined the redistribution of the collapsed sediment. We conclude that adding the process of shoal margin collapses increases the channel-shoal interactions and that in intensively dredged estuaries shoal margins oversteepen, amplifying the number of collapses, but because of dredging the natural morphological response is interrupted.