Flexible Network Research Articles

A few-shot oil spill segmentation network guided by multi-scale feature similarity modeling

Segmentation of oil spills with few-shot samples using UAV optical and SAR images is crucial for enhancing the efficiency of oil spill monitoring. Current oil spill semantic segmentation predominantly relies on SAR images, rendering it relatively data-dependent. We propose a flexible and scalable few-shot oil spill segmentation network that transitions from UAV optical images to SAR images based on the image similarity of oil spill regions in both types of images. Specifically, we introduce an Adaptive Feature Enhancement Module (AFEM) between the support set branch and the query set branch. This module leverages the precise oil spill information from the UAV optical image support set to derive initial oil spill templates and subsequently refines and updates the query oil spill templates through training to guide the segmentation of SAR oil spills with limited samples. Additionally, to fully exploit information from both low and high-level features, we design a Feature Fusion Module (FFM) to merge these features. Finally, the experimental results demonstrate the effectiveness of our network in enhancing the performance of UAV optical-to-SAR oil spill segmentation with few samples. Notably, the SAR oil spill detection accuracy reaches 75.88% in 5-shot experiments, representing an average improvement of 5.3% over the optimal baseline model accuracy.

A cryo-electron tomography study of ciliary rootlet organization

Ciliary rootlets are striated bundles of filaments that connect the base of cilia to internal cellular structures. Rootlets are critical for the sensory and motile functions of cilia. However, the mechanisms underlying these functions remain unknown, in part due to a lack of structural information of rootlet organization. In this study, we obtain 3D reconstructions of membrane-associated and purified rootlets from mouse retina using cryo-electron tomography. We show that flexible protrusions on the rootlet surface, which emanate from the cross-striations, connect to intracellular membranes. In purified rootlets, the striations were classified into amorphous (A)-bands, associated with accumulations on the rootlet surface, and discrete (D)-bands corresponding to punctate lines of density that run through the rootlet. These striations connect a flexible network of longitudinal filaments. Subtomogram averaging suggests the filaments consist of two intertwined coiled coils. The rootlet’s filamentous architecture, with frequent membrane-connecting cross-striations, lends itself well for anchoring large membranes in the cell.

A cryo-electron tomography study of ciliary rootlet organization.

Ciliary rootlets are striated bundles of filaments that connect the base of cilia to internal cellular structures. Rootlets are critical for the sensory and motile functions of cilia. However, the mechanisms underlying these functions remain unknown, in part due to a lack of structural information of rootlet organization. In this study, we obtain 3D reconstructions of membrane-associated and purified rootlets from mouse retina using cryo-electron tomography. We show that flexible protrusions on the rootlet surface, which emanate from the cross-striations, connect to intracellular membranes. In purified rootlets, the striations were classified into amorphous (A)-bands, associated with accumulations on the rootlet surface, and discrete (D)-bands corresponding to punctate lines of density that run through the rootlet. These striations connect a flexible network of longitudinal filaments. Subtomogram averaging suggests the filaments consist of two intertwined coiled coils. The rootlet's filamentous architecture, with frequent membrane-connecting cross-striations, lends itself well for anchoring large membranes in the cell.

An IGDT‐stochastic model for low‐carbon economic dispatch of integrated electricity‐natural gas systems considering grid‐enhancing technologies

AbstractUtilizing wind power alongside flexible resources such as power‐to‐gas technology, gas‐fuel generator, demand response (DR) program, grid‐enhancing technologies, and carbon capture and storage can help to low carbon operation of integrated electricity‐gas systems (IEGSs). Accordingly, this paper proposes a low‐carbon economic dispatch model for the IEGS, in which gas‐fuel generators, DR, and gas‐fuel generator are considered to realize the economic and environmentally friendly operation of these systems. Also, the flexible AC transmission system device as one of the grid‐enhancing technologies is innovatively included in IEGSs to guarantee that wind power is deliverable entire the electricity system. Besides, power‐to‐gas equipped with hydrogen storage is used to absorb the excess wind power to produce CH4. On the other hand, to capture the inherent flexibility of the gas network, the gas‐storing characteristic of pipelines is shown by line pack modelling. To manage uncertainties associated with wind power and DR program, the proposed model is formulated as an IGDT‐stochastic problem. For efficient computation purposes, the present work follows the mixed‐integer linear programming framework. Different case studies are performed on an integrated test system. Numerical simulation results show that the proposed model leads to reducing the total cost, carbon emissions, and wind curtailment by 29%, 16.4%, and 100%, respectively. It can be seen that the proposed low‐carbon ED model is environmentally friendly and has economic benefits.

Probe-Optimizer: Discovering important nodes for proactive in-band network telemetry to achieve better probe orchestration

By embedding the state data maintained by the programmable data plane into additional customizable probes, proactive in-band network telemetry (INT) can easily achieve flexible, full-coverage and fine-grained network measurement. However, a significant portion of these probes are invalid, failing to capture meaningful network event information, and instead increasing bandwidth occupancy as well as communication overhead between the control plane and the data plane. Furthermore, these invalid probes exacerbate controller overhead, forcing resource-limited CPUs to perform a large amount of meaningless computation and analysis. In this paper, we propose Probe-Optimizer, a novel framework tailored for proactive INT, which can reduce the introduction of invalid probes to comprehensively lower the various telemetry overheads mentioned above. Technically, Probe-Optimizer assigns a unique importance to each node in the telemetry scenario. The importance is significantly related to the probability of network events occurring, which can be used to select important nodes worth monitoring in the topology over a period of time. Then, Probe-Optimizer generates a dedicated set of probe paths for important nodes and another set for the remaining nodes/links, customizing a more appropriate probe frequency for each probe path. Extensive evaluations on both random and FatTree topologies with different scales are conducted. The results show that Probe-Optimizer introduces significantly fewer invalid probes. Benefiting from this, for the topology with a size of more than 200 nodes, compared to the state-of-art proactive INT methods, Probe-Optimizer achieves a higher proportion of probes carrying network events and at least 13%, 42%, and 26% lower communication overhead, CPU usage, and average bandwidth occupancy, respectively.

Self-Healing Supramolecular Flexible Network of Zn(II): Exploring Chemo-Responsiveness, Antimicrobial Efficiency, and Variable Microelectronic Device Performances.

The Zn(II)-supramolecular metallogel (i.e., Zn-Py) of 2,6-pyridinedicarboxylic acid was prepared through the addition of a metal source and 2,6-pyridinedicarboxylic acid as a low molecular weight gelator. The Zn-Py metallogel encapsulated N,N'-dimethylformamide as gel-immobilized polar aprotic solvent media. The mechanical features of the synthesized metallogel were investigated. The thixotropic behavior of the Zn-Py metallogel was also analyzed. The microscopic feature of the metallogel was imaged through FESEM and TEM studies. The EDS pattern of the metallogel ratified the role of different gel-building chemical constituents. The stimuli responsiveness of the metallogel was also tested. The metallogel-forming mechanistic protocol was visualized through FTIR and ESI mass spectroscopic analyses. The bioeffectiveness, i.e., antimicrobial potency, of Zn-Py was also studied. The antimicrobial efficiency against both Gram +ve and Gram -ve bacteria including Salmonella typhimurium (MTCC 98), Escherichia coli (MTCC 1667), Bacillus cereus (ATCC 13061), Listeria monocytogenes (MTCC 657), and Staphylococcus aureus (MTCC 96) was critically analyzed. The FESEM images of the live bacteria and damaged bacteria due to the action of the metallogel were experimentally investigated. The Zn-Py metallogel was also used to fabricate the heterojunction and Schottky-type photodetectors to show their excellent light-matter interaction and their potentiality as active material in optoelectronics. The electrical parameters of semiconductor diodes such as the p-n junction and Schottky diode fabricated by the synthesized Zn-Py metallogel were investigated. Outcomes of the experimental investigation demonstrated that the tested metallogel effectively showed p-n junction diode parameters, especially with the ideality factor (η) of 1.3 under a dark environment. This fabricated device efficiently depicted a great ON/OFF ratio of 33.4 at a reverse bias voltage of -2 V. The metal-semiconductor-metal (M-S-M) junction-type Schottky barrier diode was also fabricated using the Zn-Py metallogel where the Au/Zn-Py metallogel/Au-based device fabrication strategy was implemented.

Metal-free heteroatom integrated defect engineering of flexible carbon networks on tin oxide nanoparticles to enhance lithium-ion battery performance

Metal-free heteroatom integrated defect engineering of flexible carbon networks on tin oxide nanoparticles to enhance lithium-ion battery performance

F3: Fast and Flexible Network Telemetry with an FPGA coprocessor

Traffic monitoring in the dataplane is vital for reacting to network events such as microbursts, incast, and attacks. However, current solutions are constrained by the limited resources available on modern ASICs and don't provide the flexibility required to identify repeating patterns, such as applications whose flows communicate with a server at regular intervals. While such flexibility can be achieved using a co-processing CPU, it is generally too slow to provide insights quickly enough. In this paper, we show how an FPGA co-processor placed alongside the switching pipeline enables flexible traffic monitoring at data plane rates. While FPGAs have large memory and expressive processing, their throughput is significantly lower than switch ASICs. To bridge the throughput gap, we split query execution between the switch and FPGA and present methods that prevent processing all packets in FPGA. As a result, our system misses up to 5.0x fewer DDoS attack vectors than ACC-Turbo, the state-of-the-art on-switch solution, and up to 24% fewer microburst-contributing flows for the same precision rate.

Cofilin-Mediated Filament Softening and Crosslinking Counterbalance to Enhance Actin Network Flexibility.

Filamentous-actin (F-actin) crosslinking within the cell cytoskeleton mediates the transmission of mechanical forces, enabling changes in cell shape, as occurs during cell division and cell migration. Crosslinking by actin binding proteins (ABPs) generally increases the connectivity of the F-actin network, but also increases network rigidity. As a result, there is a narrow range in the concentration of crosslinker protein at which F-actin networks are both connected and labile. Another ABP, cofilin, severs F-actin filaments at high pH through increasing their bending flexibility and concentrating mechanical stress, inducing fragmentation. By contrast, at lower pH, cofilin increases filament flexibility yet does not sever. Instead, it forms disulfide bonds, which crosslink F-actin into bundles, and bundles into networks. Here, we combine light microscopy and rheology to determine the impact of two potentially opposing effects on the mechanics of F-actin networks-increased flexibility at the filament level, and increased connectivity at the network level. Indeed, by linear rheology, we find that these mechanisms are counterbalanced, such that cofilactin network moduli are only weakly dependent on cofilin concentration over a broad range, in contrast to the dramatic stiffening that occurs with F-actin crosslinking protein. Further, by nonlinear rheology, the network stiffens at a higher stress than crosslinking protein, indicative of a broader range in which the material remains flexible. These results may enable F-actin networks to increase connectivity without heavy penalties to rigidity, and thus provide a new route to modulating active polymer mechanics unseen using traditional F-actin accessory proteins.

Elevated plasma p-tau231 is associated with reduced generalization and medial temporal lobe dynamic network flexibility among healthy older African Americans

BackgroundPhosphorylated tau (p-tau) and amyloid beta (Aβ) in human plasma may provide an affordable and minimally invasive method to evaluate Alzheimer’s disease (AD) pathophysiology. The medial temporal lobe (MTL) is susceptible to changes in structural integrity that are indicative of the disease progression. Among healthy adults, higher dynamic network flexibility within the MTL was shown to mediate better generalization of prior learning, a measure which has been demonstrated to predict cognitive decline and neural changes in preclinical AD longitudinally. Recent developments in cognitive, neural, and blood-based biomarkers of AD risk that may correspond with MTL changes. However, there is no comprehensive study on how these generalization biomarkers, long-term memory, MTL dynamic network flexibility, and plasma biomarkers are interrelated. This study investigated (1) the relationship between long-term memory, generalization performance, and MTL dynamic network flexibility and (2) how plasma p-tau231, p-tau181, and Aβ42/Aβ40 influence generalization, long-term memory, and MTL dynamics in cognitively unimpaired older African Americans.Methods148 participants (Meanage: 70.88,SDage: 6.05) were drawn from the ongoing longitudinal study, Pathways to Healthy Aging in African Americans conducted at Rutgers University–Newark. Cognition was evaluated with the Rutgers Acquired Equivalence Task (generalization task) and Rey Auditory Learning Test (RAVLT) delayed recall. MTL dynamic network connectivity was measured from functional Magnetic Resonance Imaging data. Plasma p-tau231, p-tau181, and Aβ42/Aβ40 were measured from blood samples.ResultsThere was a significant positive correlation between generalization performance and MTL Dynamic Network Flexibility (t = 3.372, β = 0.280, p < 0.001). There were significant negative correlations between generalization performance and plasma p-tau231 (t = -3.324, β = -0.265, p = 0.001) and p-tau181 (t = -2.408, β = -0.192, p = 0.017). A significant negative correlation was found between plasma p-tau231 and MTL Dynamic Network Flexibility (t = -2.825, β = -0.232, p = 0.005).ConclusionsIncreased levels of p-tau231 are associated with impaired generalization abilities and reduced dynamic network flexibility within the MTL. Plasma p-tau231 may serve as a potential biomarker for assessing cognitive decline and neural changes in cognitively unimpaired older African Americans.

Imperfection-insensitive flexible random network materials with horseshoe microstructures

Flexible network materials with periodic constructions of bioinspired wavy microstructures are of focusing interest in recent years, because they combine outstanding mechanical performances of low elastic modulus, high stretchability, biomimetic stress-strain responses, and strain-limiting behavior. In practical applications (e.g., bio-integrated devices and tissue engineering), small holes are often strategically designed in flexible network materials to accommodate functional chips and other individual electronic components. The design of imperfection insensitive flexible network materials is therefore of pivotal importance. While random structural constructions are believed to play crucial roles in the excellent mechanical properties of many biological materials, the effect of randomness on mechanical performances of flexible network materials has not yet been explored. In this work, a class of two-dimensional (2D) flexible random network materials consisting of horseshoe microstructures is introduced. Their node distance distributions, which can be characterized by a parameter related to randomness, follow well the Weibull probability density function. Combined numerical and experimental studies were performed to elucidate the effect of randomness on nonlinear mechanical responses of flexible network materials. Simple analytical equations are obtained for their key mechanical properties (e.g., strength, stretchability, and initial modulus). Flexible random network materials (with randomness ≥ 0.4) were found to exhibit approximately isotropic J-shaped stress-strain responses, even in the high-strain regime. Finally, we study the reduction of stretchability and strength in random network materials induced by different types of imperfections (e.g., a missing filament, a missing node, or many missing filaments). In comparison to periodic network materials, random network materials (e.g., with randomness ≥ 0.6) show much smaller reductions of stretchability/strength when the imperfection appears, and are therefore more imperfection-insensitive. Such an imperfection-insensitive behavior can be mainly attributed to a relieved stress concentration around the imperfection of random network materials.

A minimum cost and maximum fairness-driven multi-objective optimization consensus model for large-scale group decision-making

The rise of social media and e-democracy has driven a paradigm shift in decision-making, notably reflected in the changing ways of public participation and policymaking within decision-making processes. The increased focus on fairness and efficiency not only complicates the consensus-building process among diverse interests and perspectives, but also significantly adds to the complexity of decision-making and its implementation. In this context, balancing the interests of all parties while ensuring fairness and improving decision-making effectiveness becomes crucial. To address these challenges, this study develops a multi-objective optimization consensus framework for large-scale group decision-making (LSGDM) that integrates the interests of decision-makers (DMs) and a moderator, providing a more comprehensive tool. Specifically, this study first designs a DM weight determination method based on structural hole theory within fuzzy social networks. The proposed DM weight determination method effectively leverages the flexibility of fuzzy social networks and the comprehensiveness of structural hole theory to enhance the accuracy and reliability of weight assignment. Building on this, a novel clustering method based on the maximum group consensus level is developed, taking into account the varying importance of different DMs. Furthermore, a minimum cost and maximum fairness-driven multi-objective optimization LSGDM consensus model, referred to as MCMF-MO-LSGDM, is explored in this study. Finally, the utility and superiority of the constructed model are confirmed through comparative analysis and simulation experiments against existing related works.

Designing from biobased to closed-loop circularity: Flexible dynamic polyimine-amide networks

Dynamic polyimine-amide networks with exceptional properties, including high flexibility, excellent thermal stability and dual closed-loop circularity were designed by combining dynamic covalent imine-functionalities with amide-chemistry. The solvent free up-scalable synthesis started from carboxyl-functionalization of lignin-derivable aldehydes followed by melt polycondensation with a triamine to form two dynamic networks (PIAX1 and PIAX2, respectively). While previously reported vanillin-derived polyimine thermosets were typically non-flexible and brittle, our polyimine-amides are flexible with elongation at break 380 % for PIAX1 and 65 % for PIAX2, where the higher flexibility of PIAX1 is deduced to the lower glass transition temperature and crosslinking density. Both materials illustrate fast stress relaxation even at low temperature, down to 50 °C in the case of PIAX1. Retained and even improved mechanical properties were observed after several cycles of thermal reprocessing, e.g., after three reprocessing cycles by hot pressing, PIAX1 recovered 116 % of original tensile stress and 126 % of original elongation at break, while chemical recycling under acidic conditions at room temperature yielded repolymerizable trialdehydes and triamines. Furthermore, rapid self-healing and shape memory behaviour at low temperature were demonstrated for PIAX1. A promising molecular design, further tuneable by choice of aldehyde and triamine, is demonstrated enabling high performance and dual closed-loop circularity.

A single-particle energy-conserving dissipative particle dynamics approach for simulating thermophoresis of nanoparticles in polymer networks.

The transport of nanoparticles in polymer networks has critical implications in biology and medicine, especially through thermophoresis in response to temperature gradients. This study presents a single-particle energy-conserving dissipative particle dynamics (seDPD) method by integrating a single-particle model into the energy-conserving DPD model to simulate the mesoscopic thermophoretic behavior of nanoparticles in polymer matrices. We first validate the newly developed seDPD model through comparisons with analytical solutions for nanoparticle viscosity, thermal diffusivity, and hydrodynamic drag and then demonstrate the effectiveness of the seDPD model in capturing thermophoretic forces induced by temperature gradients. The results show that nanoparticles driven by the Soret forces exhibit unique transport characteristics, such as drift velocity and diffusivity, leading to a significant acceleration of nanoparticle diffusion in the polymer network, which has been known as the giant acceleration of diffusion. Quantifying how nanoparticles move in flexible polymer networks sheds light on the interaction dynamics of nanoparticles within polymer networks, providing insight into nanoparticle behavior in complex environments that could be leveraged in various applications from drug delivery to material design.

Enhancing Flexibility and Reliability in Smart Distribution Networks: A Self-Healing Approach

This paper presents a pioneering approach that integrates a Self-Healing System through a Smart Distribution Network, which employs a two-step implementation of a comprehensive Energy Management algorithm and a multi-agent system with an autonomous distributed regeneration method. The novel method improves network operation through optimized management of local microgrids, which include diverse components comprising renewable energy sources, electric vehicle charging infrastructure, and energy storage systems. The first phase concentrates on minimizing network operation costs by adhering to system utilization restrictions and optimal load distribution, while the second phase addresses the optimization of regeneration processes, which decreases discrepancies between recoverable priority loads and switching operations. The evaluation introduces a stochastic programming approach to model and manage uncertainties, and utilizes the Kantorovich method and the roulette wheel mechanism to improve reliability. ...

A collaborative planning of PCCs siting and transmission network expansion for super large‐scale offshore wind clusters

AbstractThe integration of super large‐scale offshore wind clusters into the onshore power grid brings challenges for the flexibility of the transmission network. Therefore, this paper proposes a two‐stage collaborative planning of points of common coupling siting and transmission network expansion for super large‐scale offshore wind clusters, based on a multi‐voltage‐level stratified integration mode which enables offshore wind power to transmit to larger regions for accommodation. At first, the transmission network flexibility indexes are established. Then, a two‐stage collaborative planning model for points of common couplings siting and transmission network expansion is proposed, which comprises a collaborative planning model and an optimal power flow operation model. The proposed model reduces wind curtailment and load shedding by optimizing the annual total cost and flexibility transmission capability of the network. Finally, the effectiveness of the proposed method is verified using a modified IEEE 30‐bus system and a 65‐bus system.

Multi-period optimisation of flexible natural gas production network infrastructure with an operational perspective: A mixed integer linear programming approach

With the increased complexities in end markets, advanced quantitative tools became essential for efficient decision-making. Traditional methods that implement average forecasted demand and prices often fail to account for the dynamic nature of network behaviour. Hence, this study introduces a multi-period mixed integer linear programming (MILP) model for flexible natural gas allocation in complex networks. The model was formulated based on Qatar as a case study, utilising a Qatar-based natural gas monetisation network that includes four direct and two indirect monetisation processes. By integrating flexible operational boundaries with annual demand and price forecasts, the model optimises annual allocation strategies to maximise profitability. A scenario-based evaluation of three cases demonstrated the model’s robustness and potential for enhancing profitability. Scenarios limited to operational constraints achieved the highest profitability, especially in allocating high-value commodities such as ammonia and urea. However, the proposed production capacities surpass the current infrastructure capabilities, requiring further infrastructure investments. The validation of results showed a slight profitability decrease of 1.2% when these investment costs were included. Notably the case considering fixed and operating cost variables together as a single cost variable in the objective function, referred to as annualised cost (case C), offered optimal cost quantification with relaxed technical constraints. Overall, the multi-level multi-period MILP model aids decision-makers in understanding system capabilities and explores the benefits of flexible operation in proactively addressing endogenous and exogenous uncertainties.

Reliability assessment of distribution networks with medium-voltage and low-voltage flexible interconnections considering graded transfer

Abstract For flexible interconnection, flexible interconnected devices (FID) are utilized to upgrade or build connection nodes, which can provide more ways for load recovery after faults, thereby improving the reliability level of the distribution network. Based on the characteristics of connection switch, medium-voltage (MV), and low-voltage (LV) flexible interconnection transfer methods, a reliability assessment method is proposed in this paper for medium-voltage and low-voltage flexible interconnection distribution networks (MLFIDN) considering graded transfer. First, a multi-state reliability model of the LV substation area and discrete probability distribution of DG output are established based on the system structure and composition. Then, the fault impact analysis based on graded transfer mode is proposed. A transfer recovery model that takes into account power balance, the node voltage, line current safety constraints, and flexible interconnection operation constraints is established. On this basis, a simulation method based on non-sequential Monte Carlo sampling is used for system reliability assessment. Finally, the effectiveness of the proposed method is verified by the IEEE-33 distribution network with MV and LV flexible interconnection reliability assessment. The reliability assessment results can quantify the improvement of flexible interconnection on the reliability of distribution networks.

Polymer nanocomposites based on flexible BaTiO3 networks anchored with gold nanoparticles towards simultaneous high dielectric permittivity and low loss

Polymer composites filled with conductive fillers have been proven to have ultra-high effective permittivity near percolation, but they usually have high dielectric loss due to the easy contact between the conductive fillers. Herein, a polymer composite with a three-dimensional BaTiO3 ceramic skeleton loaded with gold nanoparticles, which effectively avoids the direct contact of gold nanoparticles, is proposed. Benefiting from numerous equivalent micro-capacitors and the Coulomb blockade effect of gold nanoparticles, obviously enhanced permittivity and low loss are concurrently achieved. Particularly, with a gold sputtering time of 12 min, a high permittivity of 47.7@10 kHz, which is approximately 454 % (i.e. from 8.2 to 47.7@10 kHz) of the PVDF matrix, is achieved in the composite. Meanwhile, a low loss of about 0.032, which is merely about 65 % that of pure PVDF (0.0492@10 kHz) is maintained. This work provides an effective approach for designing polymer composites with high permittivity and low loss.

MEA-NAP: A flexible network analysis pipeline for neuronal 2D and 3D organoid multielectrode recordings

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and thus can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches. VIDEO ABSTRACT.