Channel Network Research Articles

Improved Uniformity and Processability of Inkjet-Printed Single-Walled Carbon Nanotube Thin-Film Transistor by Introducing Cellulose Dispersant.

Solution-processed thin-film transistors (TFTs) based on single-walled carbon nanotubes (SWCNTs) hold great potential for next-generation electronics owing to their remarkable electrical, mechanical, and optical characteristics. However, challenges in efficiently dispersing SWCNTs hinder scalable fabrication. Conventional methods using surfactants improve SWCNT dispersion but lead to degradation of device performance due to increased contact resistance between the SWCNTs. Furthermore, the surfactant removal process induces unexpected characteristic nonuniformity by residual surfactant and network impairment. Here, we propose a facile and effective strategy for achieving superior performance uniformity in inkjet-printed SWCNT TFTs by using cellulose as a dispersant for SWCNTs. Cellulose-based SWCNT ink exhibits excellent dispersibility and stability, preserving the intrinsic electronic properties of SWCNTs while enabling optimal droplet formation for inkjet printing by adjusting the cellulose concentration. Based on the thermal decomposition characteristics of cellulose, we form a uniform SWCNT random network channel without affecting the nanotube network by selectively removing cellulose through a simple annealing process. As a result, the SWCNT TFTs fabricated on a 4-in. wafer substrate show significant improvements in characteristic uniformity, with a reduction of over 35% in performance variation, and exhibit strengths in switching performance compared to conventional surfactant-based SWCNT TFT fabrication methods.

Enhancing single image dehazing with self-supervised convolutional neural network and dark channel prior integration

The removal of noise from images holds great significance as clear and denoised images are vital for various applications. Recent research efforts have been concentrated on the dehazing of single images. While conventional methods and deep learning approaches have been employed for daytime images, learning-based techniques have shown impressive dehazing results, albeit often with increased complexity. This has led to the persistence of prior-based methods, despite their slightly lower performance. To address this issue, we propose a novel deep learning-based dehazing method utilizing a self-supervised convolutional neural network (CNN). This approach incorporates both the input hazy image and the dark channel prior. By leveraging an encoder, the combined information of the dark channel prior and haze image is encoded into a condensed latent representation. Subsequently, a decoder is employed to reconstruct the clean image using these latent features. Our experimental results demonstrate that our proposed algorithm significantly enhances image quality, as indicated by improved peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values. We perform both quantitative and qualitative comparisons with recently published techniques, showcasing the efficacy of our approach.

Redundant task offloading with dual-reliability in MEC-assisted vehicular networks

With the rise and development of intelligent vehicles, the computation capability of vehicles has increased rapidly and considerably. Vehicle-to-Vehicle (V2V) offloading, in which computation-intensive tasks are offloaded to underutilized vehicles, has been proposed. However, V2V offloading faces the challenges of task transmission reliability and task computation reliability. In V2V offloading, tasks are transmitted via V2V communication, which is volatile and spotty because of rapidly changing network topology and channel conditions between vehicles, resulting in time-varying delays of task transmission and even loss of connectivity. Thus, it is challenging to complete V2V offloading within a given delay constraint. In addition, the realistic diverse vehicular environment always comes with malicious vehicles, which can cause irreparable harm to V2V offloading. Therefore, in this paper, we propose a V2V task offloading scheme called Redundant Task Offloading with Dual-Reliability (RTODR), aiming to minimize task offloading costs while ensuring both task transmission reliability and task computation reliability in a Mobile Edge Computing (MEC)-assisted vehicular network. Specifically, for a computation task, a V2V connection is considered reliable only if the task can be successfully transmitted via the V2V connection within the deadline of the task. To ensure task computation reliability, task computation results from a trusty service vehicle are considered to be reliable. Then we formally model a Minimizing Task Offloading Cost with Dual-reliability (MTOCD) problem, which is mathematically formulated as a multi-objective optimization problem. Afterward, we propose a heuristic redundant task offloading algorithm, named Dual-Reliability Offloading (DRO), to solve the problem. Finally, comprehensive experiments have been conducted to demonstrate that RTODR achieves lower costs compared with other approaches.

A Simple and Efficient Method with Godunov-Type Schemes for Flow Simulation at Junctions of Open Channel Networks

A Simple and Efficient Method with Godunov-Type Schemes for Flow Simulation at Junctions of Open Channel Networks

Intra/extracellular transmembrane electron transfer for bioelectric enhancement mediated by in-situ bio-FeS and polypyrrole coating

This study involves the construction of an “inside-out” electronic network channel with in-situ formation of biogenic FeS (bio-FeS) nanoparticles in cells of Shewanella oneidensis MR-1 (S. oneidensis MR-1) and a coating of conductive polypyrrole (PPy) on the outer surface of entire cells. The microbial fuel cell driven by bio-FeS and PPy co-modified S. oneidensis MR-1 (FeS/PPy@MR-1) has a maximum power density 3.6 times higher than that of the native strain. Electrochemical analysis demonstrates that the modifications of bio-FeS and PPy markedly enhance the direct electron transfer and c-type cytochrome-bound flavin-mediated single-electron reaction pathways and expand the electron output flux. The electron-donating ability of FeS/PPy@MR-1 is 7 times greater than that of the native strain. Bio-FeS and PPy can mediate novel intra/extracellular transmembrane electron transport pathways independent of the traditional respiratory chain, bridge spatially discrete redox environments, and optimize electron transfer pathways to intracellular to extracellular transfer electrons rapidly and maximally. These findings provide insights into conductive nanomaterial-mediated intra/extracellular transmembrane electron transfer, which is critical for the environment and bioenergy fields.

Study of Hydrologic Connectivity and Tidal Influence on Water Flow Within Louisiana Coastal Wetlands Using Rapid-Repeat Interferometric Synthetic Aperture Radar

The exchange of water, sediment, and nutrients in wetlands occurs through a complex network of channels and overbank flow. Although optical sensors can map channels at high resolution, they fail to identify narrow intermittent channels colonized by vegetation. Here we demonstrate an innovative application of rapid-repeat interferometric synthetic aperture radar (InSAR) to study hydrologic connectivity and tidal influences in Louisiana’s coastal wetlands, which can provide valuable insights into water flow dynamics, particularly in vegetation-covered and narrow channels where traditional optical methods struggle. Data used were from the airborne UAVSAR L-band sensor acquired for the Delta-X mission. We applied interferometric techniques to rapid-repeat (~30 min) SAR imagery of the southern Atchafalaya basin acquired during two flights encompassing rising-to-high tides and ebbing-to-low tides. InSAR coherence is used to identify and differentiate permanent open water channels from intermittent channels in which flow occurs underneath the vegetation canopy. The channel networks at rising and ebbing tides show significant differences in the extent of flow, with vegetation-filled small channels more clearly identified at rising-to-high tide. The InSAR phase change is used to identify locations on channel banks where overbank flow occurs, which is a critical component for modeling wetland hydrodynamics. This is the first study to use rapid-repeat InSAR to monitor tidal impacts on water flow dynamics in wetlands. The results show that the InSAR method outperforms traditional optical remote sensing methods in monitoring water flow in vegetation-covered wetlands, providing high-resolution data to support hydrodynamic models and critical support for wetland protection and management.

Using Hybrid LSTM Neural Networks to Detect Anomalies in the Fiber Tube Manufacturing Process

The production process of tubes for fiber optic cables is a complex process, where proper execution is crucial to the quality of the final product. This process has a complex state vector whose structure and coordinates dynamically change during the tube extrusion process. Small fluctuations in process parameters, such as temperature, extrusion pressure, production speed, and optical fiber tension, affect the optical attenuation of the final product. Such defects necessitate the withdrawal of the product. Due to the high number of process coordinates and the technological inability to automatically label those segments of the production process that cause anomalies in the final product, the authors used data clustering methods to create a training set that enabled the use of neural tools for anomaly detection. The system proposed in the main part of the paper includes a hybrid Long short-term memory (LSTM) network model, which is fed with data streams recorded on the tube extrusion production line. The input module, which performs preprocessing of input data, conducts multiresolution analysis of recorded process parameters, and recommends the process state’s belonging to a set of classes describing individual production anomalies to appropriate LSTM network modules. The learning process of the three–channel network allowed effective recognition of five classes of the monitored tube production process. The fit level of the proposed network model reached R2 values of ≥0.85.

Design of Litchi-Like Al/PTFE with Superior Reactivity and Application in Solid Propellants.

The combustion efficiency and reactivity of aluminum (Al) particles, as a crucial component in solid propellants, are constrained by the inert oxide layer aluminum oxide (Al2O3). Polytetrafluoroethylene (PTFE) can remove the oxide layer, however, carbon deposition generated during the reaction process still limits the reaction efficiency of Al/PTFE fuel. Here, a litchi-like Al/PTFE fuel with the nano-PTFE islands distributed on the Al particles surface is successfully designed, based on localized activation and synergistic reaction strategies, to solve the Al2O3 layer and carbon deposition. This unique PTFE-coated structure can achieve localized activation of Al by surface etching, creating reaction channels, and exposing the active Al. Such a channel network promotes the circulation of fluorine and oxygen, stimulating the synergistic reactions of Al-F and Al-O and energy output. Regulating the PTFE content can maximize the elimination of carbon deposition and achieve the full combustion reaction of Al/PTFE. The maximum flame area and pressure output of the litchi-like Al/PTFE fuel increased by 241.9%, 734.7%, 118.4%, and 265.2%, respectively, compared with traditional physical mixture and core-shell structure Al/PTFE fuels. The localized activation and synergistic effects of litchi-like structure effectively transform carbon waste into a valuable resource, introducing a novel approach for the propellants.

Enhancing waste classification accuracy with Channel and Spatial Attention-Based Multiblock Convolutional Network.

Municipal waste classification is significant for effective recycling and waste management processes that involve the classification of diverse municipal waste materials such as paper, glass, plastic, and organic matter using diverse techniques. Yet, this municipal waste classification process faces several challenges, such as high computational complexity, more time consumption, and high variability in the appearance of waste caused by variations in color, type, and degradation level, which makes an inaccurate waste classification process. To overcome these challenges, this research proposes a novel Channel and Spatial Attention-Based Multiblock Convolutional Network for accurately classifying municipal waste that utilizes a unique attention mechanism for enhancing feature learning and waste classification accuracy. In this research, the data augmentation technique is utilized to improve the size and diversity of the images, which creates a new municipal waste image from the existing image. After the augmentation process, the data preprocessing is performed through normalization, resizing, and dividing the images into smaller patches. In the feature extraction phase, each image patch's detailed representation is created by integrating and extracting the image's relevant features from the embedding space. Finally, the proposed Channel and Spatial Attention-Based Multiblock Convolutional Network predicts the types of municipal waste that are represented in each image patch by classifying the input images into diverse kinds of waste categories. The experimental validation exposed that the proposed Channel and Spatial Attention-Based Multiblock Convolutional Network effectively classifies the municipal waste images and achieves a higher accuracy of 98.73%, lower mean absolute error of 0.048, and lower root mean square error of 0.087 when compared to existing municipal waste classification strategies. These results prove that the proposed Channel and Spatial Attention-Based Multiblock Convolutional Network framework is more accurate, reliable, and well-suited to real-time municipal waste classification.

Traffic and Scenario Adaptive OFDM-IM for Vehicular Networks: A Fuzzy Logic Based Optimization Approach

Orthogonal Frequency Division Multiplexing with Index Modulation (OFDM-IM) holds significant importance in vehicle-to-everything (V2X) communications, with its main advantages being outstanding spectral efficiency and strong interference resistance. However, the existing OFDM-IM systems in vehicular networks overlook actual vehicular network channels and the impact of scatterers, thus failing to accurately reflect the system performance. Moreover, these systems focus solely on the bit error rate (BER) and ignore user requirements for low energy consumption and high spectral efficiency. To address these issues, we propose a user demand- and scenario-adaptive OFDM-IM method that optimizes the OFDM-IM index parameter by considering the spectral efficiency, BER, and energy consumption. Firstly, considering non-line-of-sight components and roadside reflectors, we establish a vehicle-to-vehicle (V2V) communication channel model for straight road scenarios. Then, we construct a transmission framework for vehicular network communication using OFDM-IM. Specifically, we develop an energy efficiency maximization formula, in which fuzzy logic is used to adjust the weights of the three performance indicators to meet various environmental and user requirements. In detail, we discuss the minimum signal-to-noise ratio (SNR) required for OFDM-IM to achieve a lower BER than traditional OFDM in various vehicular communication scenarios. Thus, we can make appropriate choices based on the robustness of the simulation results. The simulation results presented in this paper indicate our method’s effectiveness in enhancing the system’s reliability, efficiency, and flexibility.

EEG-based image classification using an efficient geometric deep network based on functional connectivity

To ensure that the FC-GDN is properly calibrated for the EEG-ImageNet dataset, we subject it to extensive training and gather all of the relevant weights for its parameters. Making use of the FC-GDN pseudo-code. The dataset is split into a "train" and "test" section in Kfold cross-validation. Ten-fold recommends using ten folds, with one fold being selected as the test split at each iteration. This divides the dataset into 90% training data and 10% test data. In order to train all 10 folds without overfitting, it is necessary to apply this procedure repeatedly throughout the whole dataset. Each training fold is arrived at after several iterations. After training all ten folds, results are analyzed. For each iteration, the FC-GDN weights are optimized by the SGD and ADAM optimizers. The ideal network design parameters are based on the convergence of the trains and the precision of the tests. This study offers a novel geometric deep learning-based network architecture for classifying visual stimulation categories using electroencephalogram (EEG) data from human participants while they watched various sorts of images. The primary goals of this study are to (1) eliminate feature extraction from GDL-based approaches and (2) extract brain states via functional connectivity. Tests with the EEG-ImageNet database validate the suggested method's efficacy. FC-GDN is more efficient than other cutting-edge approaches for boosting classification accuracy, requiring fewer iterations. In computational neuroscience, neural decoding addresses the problem of mind-reading. Because of its simplicity of use and temporal precision, Electroencephalographys (EEG) are commonly employed to monitor brain activity. Deep neural networks provide a variety of ways to detecting brain activity. Using a Function Connectivity (FC) - Geometric Deep Network (GDN) and EEG channel functional connectivity, this work directly recovers hidden states from high-resolution temporal data. The time samples taken from each channel are utilized to represent graph signals on a topological connection network based on EEG channel functional connectivity. A novel graph neural network architecture evaluates users' visual perception state utilizing extracted EEG patterns associated to various picture categories using graphically rendered EEG recordings as training data. The efficient graph representation of EEG signals serves as the foundation for this design. Proposal for an FC-GDN EEG-ImageNet test. Each category has a maximum of 50 samples. Nine separate EEG recorders were used to obtain these images. The FC-GDN approach yields 99.4% accuracy, which is 0.1% higher than the most sophisticated method presently available

Microfluidic mixing probe: generating multiple concentration-varying flow dipoles

This study advances microfluidic probe (MFP) technology through the development of a 3D-printed Microfluidic Mixing Probe (MMP), which integrates a built-in pre-mixer network of channels and features a lined array of paired injection and aspiration apertures. By combining the concepts of hydrodynamic flow confinements (HFCs) and “Christmas-tree” concentration gradient generation, the MMP can produce multiple concentration-varying flow dipoles, ranging from 0 to 100%, within an open microfluidic environment. This innovation overcomes previous limitations of MFPs, which only produced homogeneous bioreagents, by utilizing the pre-mixer to create distinct concentration of injected biochemicals. Experimental results with fluorescent dyes and the chemotherapeutic agent Cisplatin on MCF-7 cells confirmed the MMP’s ability to generate precise, discrete concentration gradients with the formed flow dipoles, consistent with numerical models. The MMP’s ability to localize drug exposure across cell cultures without cross-contamination opens new avenues for drug testing, personalized medicine, and molecular biology. It enables precise control over gradient delivery, dosage, and timing, which are key factors in enhancing drug evaluation processes.

Digital Elevation Model Extraction and Correction of Hilly River Channel Network in Pi-Shi-Hang Irrigation District, China

Digital Elevation Model Extraction and Correction of Hilly River Channel Network in Pi-Shi-Hang Irrigation District, China

Multi-rate sampled-data observer-based networked load frequency control for multi-area interconnected power systems under multiple cyber-attacks

This paper addresses the problem of adaptive event-triggered load frequency control (LFC) for multi-rate sampling multi-area interconnected power systems with integrated renewable energy. Different network channels can be vulnerable to multiple cyber-attacks, including deception attacks and DoS attacks. First, a matching mechanism is constructed to fuse the multi-rate sampled data, and a set of observers based on multi-rate sampled data is designed to independently estimate the states of the individual power systems. A networked sampled-data PI controller is then developed by introducing a new synchronous sampling scheme and an adaptive event-triggered mechanism. A co-design approach is proposed that integrates decentralized PI controllers, observers, and adaptive event-triggered mechanisms to achieve exponential mean-square stability with a given H∞ performance level for the networked LFC system in the presence of multiple cyber-attacks and multi-rate sampling. Finally, the effectiveness of the proposed method is verified through an example of an interconnected power system.

Remaining useful life prediction for stratospheric airships based on a channel and temporal attention network

Remaining useful life prediction for stratospheric airships based on a channel and temporal attention network

PaySwitch: Smart Contract-Based Payment Switch for Off-Chain Payment Channel Networks

PaySwitch: Smart Contract-Based Payment Switch for Off-Chain Payment Channel Networks

Three-dimensional bioprinting utilizing sacrificial material support and longitudinal printability evaluation through volumetric imaging.

In three-dimensional (3D) bioprinting, the internal channel network is vital for nutrient and oxygen transport, crucial for cell survival and tissue construction. However, bioinks' poor mechanical properties hinder precise control over these networks. Advancements in 3D printing strategies, structure characterization, and deformation monitoring can improve hydrogel scaffolds with interconnected channels. Using label-free, non-invasive, in-situ optical coherence tomography (OCT) imaging, we monitored the dynamic deformation of 3D bioprinted hydrogel scaffolds. We validated sacrificial materials' role in enhancing internal channels and introduced deformation characteristics, lateral pore ratio and pore-specific surface area as new parameters. Results from cell-laden hydrogels show that 3D bioprinting with sacrificial materials achieves high fidelity, minimizing collapse, inter-filament fusion, and enhancing lateral porosity. Furthermore, these promote cell proliferation and cell viability.

Numerical Study of Carreau Fluid Flow in Symmetrically Branched Tubes

The non-Newtonian Carreau fluid model is a suitable model for pseudoplastic fluids and can be used to characterize fluids not so different from biological fluids, such as the blood, and fluids involved in geological processes, such as lava and magma. These fluids are frequently conveyed by complex flow structures, which consist of a network of channels that allow the fluid to flow from one place (source or sink) to a variety of locations or vice versa. These flow networks are not randomly arranged but show self-similarity at different spatial scales. Our work focuses on the design of self-similar branched flow networks that look the same on any scale. The flow is incompressible and stationary with a viscosity following the Carreau model, which is important for the study of complex flow systems. The flow division ratios, the flow resistances at different scales, and the geometric size ratios for maximum flow access are studied, based on Computational Fluid Dynamics (CFD). A special emphasis is placed on investigating the possible incidence of flow asymmetry in these symmetric networks. Our results show that asymmetries may occur for both Newtonian and non-Newtonian fluids and shear-thinning fluids most affect performance results. The lowest flow resistance occurs when the diameters of the parent and daughter ducts are equal, and the more uniform distribution of flow resistance occurs for a ratio between the diameters of the parent and daughter ducts equal to 0.75. Resistances for non-Newtonian fluids are 4.8 to 5.6 times greater than for Newtonian fluids at Reynolds numbers of 100 and 250, respectively. For the design of engineering systems and the assessment of biological systems, it is recommended that the findings presented are taken into account.

Discovery of fossil avian footprints from Late Holocene sediments of Allahbund uplift in Great Rann of Kachchh of Western India

The Great Rann of Kachchh is a sabkha terrain with a thick succession of Quaternary to Late Holocene sediments, deposited during high sea level after the Last Glacial Maxima. Geomorphologically, the Great Rann of Kachchh is subdivided into Bet Zone, Linear Trench Zone, Great Barren Zone, and Banni Plain. The Bet zone is a slightly elevated flat surface comprising a complex network of bets and interbet channels—the geomorphic entities developed as complex interplay of sea level and coseismic tectonic activity during the Holocene. Moreover, during the last 5.5 to 2 ka, the central part of the western Great Rann was under the influence of tidal flat sedimentation. The present study at Karimshahi Bet/ Allahbund (the western portion of the Bet zone) records the first group of avian tracks of shorebirds from Late Holocene sediment preserved at a depth of 15 cm below the present-day Rann surface. According to the morphological and preservational study, the footprints are classified as cf. Gruipeda, suggesting the trace maker as waders (Shorebirds) foraging on the shoreface. The discovery of avian trackways and abundant shallow marine ichnoassemblage within the studied unit suggests that the area was flooded by low-energy water, forming a favourable habitat for waders (shorebirds) to forage shoreline. Thus, the work describes the first-ever record of avian shorebird tracks from the Late Holocene mudflat succession of Great Rann of Kachchh, Western India.

Observations of Estuarine Salt Intrusion Dynamics During a Prolonged Drought Event in the Rhine‐Meuse Delta

AbstractSalt intrusion poses a global threat to estuaries and deltas, exacerbated by climate change, drought, and sea level rise. This observational study investigates the impact of river discharge, wind, and tidal variations on salt intrusion in a branching river delta during drought. The complexity and spatial extent of deltas make comprehensive measurements challenging and rare. In this paper, we present a 17‐week data set of a historic drought in the Rhine‐Meuse Delta, capturing dynamics in a multiple‐channel system in a wide range of conditions. Key characteristics of this low‐lying delta are its branching channel network and complicated, human‐controlled discharge. Despite the system's complexity, we found that the subtidal salt intrusion length, defined by the 2 PSU isohaline , follows a power law relationship with Rhine River discharge . Subtidal water level variations contribute to short‐term variations in intrusion length, shifting the limit of salt intrusion upstream and downstream with a distance similar to the tidal excursion length. This can be attributed to the up‐estuary transport of seawater, caused by the estuary adjusting to variations in water levels at its mouth. However, spring‐neap variation in the tidal range does not alter the subtidal salt intrusion length. Side branches exhibit distinct dynamics from the main river, and their most important control is the downstream salinity. We show that treating the side branches separately is crucial to incorporate the highly variable downstream boundary condition, and may apply in other deltas or complex estuaries.