Hierarchical Network Structure Research Articles

Topological controls on catchment‐scale sediment dynamics

AbstractThe episodic transfer of sediment from source to sink is a fundamental process in fluvial systems that influences river morphology, aquatic and riparian ecosystems, and risk from a variety of associated natural hazards. The hierarchical structure of river networks has been identified as a key control on spatiotemporal patterns of sediment routing at the catchment scale, but very few studies have systematically explored this relationship. In this paper, we investigate the role that drainage network topology plays in modulating sediment flux and morphodynamic activity. We simulate the geomorphological responses of four topologically distinct catchments from New Zealand's South Island to sequences of flood events using a landscape evolution model. Spatiotemporal variation in different types of geomorphological activity is assessed via a link‐based framework, and potential interrelationships between within‐network changes and discharge and sediment yield at the catchment outlets are explored to provide insights into relative levels of network connectivity. We also investigate the occurrence of geomorphic ‘hotspots’ in relation to network topology, and their impact on the downstream transfer of sediment in different network ‘types’. Dissected networks were found to exhibit much greater spatiotemporal variability in geomorphological activity compared to narrow, elongated networks where change was concentrated in mainstem reaches. The frequency and significance of geomorphological hotspots are shown to vary between network types, with strong contrasts evident between dissected networks with steep topography and elongated networks with more gentle gradients. Dissected networks exhibited mostly non‐linear relationships between within‐network geomorphological activity and outlet discharge and sediment yield. However, moderate to strong linear relationships between these variables were observed in mainstem‐dominated networks, indicating much greater levels of connectivity across a range of flow conditions. We discuss the implications of these findings on the transformation of environmental signals through fluvial systems with different topological structures, and the differential responses of catchments to disturbance events.

Biomimetic Approach toward Visible Light-Driven Hydrogen Generation Based on a Porphyrin-Based Coordination Polymer Gel.

There has been a widespread interest in developing self-assembled porphyrin nanostructures to mimic nature's light-harvesting processes. Herein, porphyrin-based coordination polymer gel (CPG) has been developed as a "soft" photocatalyst material for hydrogen (H2) production from water under visible light. The CPG offers a hierarchical nanofibrous network structure obtained through self-assembly of a terpyridine alkyl-amide appended porphyrin (TPY-POR)-based low molecular weight gelator with ruthenium ions (RuII) and produces H2 with a rate of 5.7 mmol g-1 h-1 in the presence of triethylamine (TEA) as a sacrificial electron donor. Further, the [Fe2(bdt)(CO)6] (dbt = 1,2-benzenedithiol) cocatalyst, which can mimic the activity of iron hydrogenase, is coassembled in the CPG and shows remarkable improvement in H2 evolution (catalytic activity; rate ∼10.6 mmol g-1 h-1 and turnover number ∼1287). The significant enhancement in catalytic activity was supported by several controlled experiments, including femtosecond transient absorption (TA) spectroscopy and also DFT calculation. The TA study supported the cascade electron transfer process from porphyrin core to [Ru(TPY)2]2+ center, and subsequently, the electron transfers to the cocatalyst [Fe2(bdt)(CO)6] for H2 production.

Large-Scale Semantic Scene Understanding with Cross-Correction Representation

Real-time large-scale point cloud segmentation is an important but challenging task for practical applications such as remote sensing and robotics. Existing real-time methods have achieved acceptable performance by aggregating local information. However, most of them only exploit local spatial geometric or semantic information dependently, few considering the complementarity of both. In this paper, we propose a model named Spatial–Semantic Incorporation Network (SSI-Net) for real-time large-scale point cloud segmentation. A Spatial-Semantic Cross-correction (SSC) module is introduced in SSI-Net as a basic unit. High-quality contextual features can be learned through SSC by correcting and updating high-level semantic information using spatial geometric cues and vice versa. Adopting the plug-and-play SSC module, we design SSI-Net as an encoder–decoder architecture. To ensure efficiency, it also adopts a random sample-based hierarchical network structure. Extensive experiments on several prevalent indoor and outdoor datasets for point cloud semantic segmentation demonstrate that the proposed approach can achieve state-of-the-art performance.

CPMF-Net: Multi-Feature Network Based on Collaborative Patches for Retinal Vessel Segmentation

As an important basis of clinical diagnosis, the morphology of retinal vessels is very useful for the early diagnosis of some eye diseases. In recent years, with the rapid development of deep learning technology, automatic segmentation methods based on it have made considerable progresses in the field of retinal blood vessel segmentation. However, due to the complexity of vessel structure and the poor quality of some images, retinal vessel segmentation, especially the segmentation of Capillaries, is still a challenging task. In this work, we propose a new retinal blood vessel segmentation method, called multi-feature segmentation, based on collaborative patches. First, we design a new collaborative patch training method which effectively compensates for the pixel information loss in the patch extraction through information transmission between collaborative patches. Additionally, the collaborative patch training strategy can simultaneously have the characteristics of low occupancy, easy structure and high accuracy. Then, we design a multi-feature network to gather a variety of information features. The hierarchical network structure, together with the integration of the adaptive coordinate attention module and the gated self-attention module, enables these rich information features to be used for segmentation. Finally, we evaluate the proposed method on two public datasets, namely DRIVE and STARE, and compare the results of our method with those of other nine advanced methods. The results show that our method outperforms other existing methods.

Microrheological properties and local structure of ι-carrageenan gels probed by using optical tweezers

Iota carrageenan (IC) is one of the most important gelling carrageenans in food and biotechnological applications; however, some of its potential applications have been constrained by its weak gelation ability. In order to better understand the origin of the weak mechanical response of IC gels, the hierarchical network structure based on meso- and microrheological properties of IC gels at 20 °C were probed via passive and active microrheology both performed by using optical tweezers (OT). Passive microrheology captures a wide spectral content of IC viscoelastic properties, revealing a rubbery plateau of the elastic modulus at relatively high frequencies for all IC concentrations; thus, uncovering the dynamics of the network. Moreover, different microstructures of IC gels were inferred by analyzing the concentration-dependent stress response when applying large deformation to the network. At low IC concentration, yielding was observed as indicated by the stress-independent response vs. strain, implying the structural rearrangement and disentanglement. As the IC concentration increased, the yielding diminished with increasing strain rate due to increased entanglement density, which limits the rearrangement of clusters. Therefore, this study presents novel insights into the meso- and microscale properties of IC gels that otherwise would not be accessible by conventional bulk rheology and submicroscopic probe diffusion measurements.

A decision aid algorithm for long-haul parcel transportation based on hierarchical network structure

With the explosion of e-commerce, optimising parcel transportation has become increasingly important. We study the long-haul stage of parcel transportation which takes place between sorting centres and delivery depots and is performed on a two-level hierarchical network. In our case study, we describe the application framework of this industrial problem faced by a French postal company: There are two vehicle types that must be balanced over the network on a daily basis, and there are two possible sorting points for each parcel, which allows a better consolidation of parcels. These industrial constraints are formalised in the Long-Haul Parcel Transportation Problem (LHPTP). We present a Mixed Integer Linear Program (MILP) and a hierarchical algorithm with aggregation of demands which uses the MILP as a subroutine. We perform numerical experiments on large-size datasets provided by a postal company, which consist of approximately 2500 demands on a network of 225 sites. These tests enable the tuning of certain parameters resulting in a tailored heuristic for the LHPTP. Our algorithm can serve as a decision aid tool for transportation managers to build daily transportation plans, modeled on solutions produced given daily demand forecasts and can also be used to improve the network design.

CNSC-26. NETWORK SIGNATURES OF SURVIVAL IN BRAIN TUMOUR GENETICS

Abstract Tumor heterogeneity is increasingly recognized as a major obstacle to therapeutic innovation across neuro-oncology. Gliomas typically exhibit a spectrum of possible genetic mutations, reflecting multiple, potentially complex interactions across distinct molecular pathways. Predicting disease evolution and prescribing individually optimal treatment plausibly requires statistical models complex enough to capture the intricate genetic structure underpinning oncogenesis. Here we formalize this task as the discovery of distinct patterns of connectivity within network representations of the interactions between genetic loci. Evaluating multi-institutional clinical, genetic, and outcome data from 7847 consecutive patients over a 14-year period, we employ Bayesian stochastic block modelling to reveal a hierarchical network structure of tumor genetic interactions spanning (molecularly confirmed) IDH-wildtype glioblastoma, IDH-mutant oligodendroglioma and astrocytoma. We show that this structure not only identifies the patients’ diagnosis, but predicts individual survival with greater fidelity (Cox’s proportional hazard cross-validated (CPH-CV) concordance = 0.78; Bayesian 12, 24 and 36-month survival models = R2 0.31, 0.40 and 0.46, respectively) than the latest WHO tumor classification (CPH-CV concordance = 0.77; Bayesian 12, 24 and 36-month survival models = R2 0.26, 0.36 and 0.39, respectively) or linear models of genetic features (CPH-CV concordance = 0.76; Bayesian 12, 24 and 36-month survival models = R2 0.26, 0.35 and 0.37, respectively). For example, a diagnosis of IDH-wildtype glioblastoma yielded median survivals varying from 289 days in patients with the signature of marked EGFR amplification, MGMT methylation, and histone mutations, to 425 days in those with the signature of TERT mutations, histone and EGFR wild-types, and variable MGMT methylation. These findings suggest network-based analysis can reveal distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories.

Synchronization Control of Complex Dynamical Networks: Invariant Pinning Impulsive Controller With Asynchronous Actuation

This article revisits the pinning impulsive control issue for complex dynamical networks (CDNs) using a new invariant pinning impulsive strategy. The word <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">invariant</i> means that the set of pinned nodes is fixed and does not change throughout the control process. First, it is revealed that each pinning strategy can naturally induce a hierarchical network structure. Based on that, a new multi-layer method is proposed to realize the invariant pinning. Next, under some mild assumptions, distinct Zeno-free controllers are designed using the event-triggered fashion in each layer of the network. Different from most existing works, the design of events here allows asynchronous actuation of the impulsive controllers. Then, the proposed strategy is proved to be also effective for impulsive sequences subject to average impulsive interval ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AII</i> ). Finally, numerical examples are provided to show the validity of the main results, even for large scale networks or chaotic systems.

Multi-Function of the Ni Interlayer in the Design of a BiVO4-Based Photoanode for Photoelectrochemical Water Splitting.

BiVO4 with an appropriate band structure is considered to be an ideal candidate for photoanodes. However, slow water oxidation kinetics and low charge separation efficiency seriously restrict its application. To address these issues, an NF/N/BVO photoanode with a hierarchical network structure was successfully constructed by direct-current magnetron sputtering of Ni followed by electrochemical deposition of nickel-iron layered double hydroxide (NiFe-LDH) on BiVO4. A photocurrent density of 4.50 mA/cm2 was obtained for NF/N/BVO, which was 2.4 times that for pristine BiVO4. The introduction of the Ni layer contributed to the following growth of NiFe-LDH nanosheets with larger size, which acted as active sites and speeded up water oxidation kinetics. Furthermore, surface photovoltage microscopy revealed that Ni and NiFe-LDH acted as the electron collector and hole reservoir, respectively. The co-existence of the two components constituted a highly efficient surface charge separation structure, which was one of the important issues for the excellent water oxidation activity.

Preparation of in-situ N/O co-doped lily-derived porous carbon framework material and its application in supercapacitors

The choice of an activator and activation method is crucial to the preparation of high-quality activated carbon. Herein, inspired by a foaming strategy, we selected KMnO4 as activator, and hierarchical porous framework carbon material was prepared via lily powder being fully foamed and activated in the KMnO4 hot solution. The Lily-derived Porous Carbon Material (LPCM) possesses the characteristics of in-situ co-doping of N and O. The maximum specific surface area of the obtained sample was 1852.5 m2g-1, and the corresponding total pore volume was 1.11 cm3g-1. Due to the interconnected hierarchical pore network structure and rich O (13.16 at%) & N (3.31 at%) contents, at 600 °C, the specific capacitance of the LPCM was 352 F g−1 at 0.5 A g1 and the cycling reliability was 100% after 10,000 charges and discharges. In addition, the voltage window of All-solid-state Symmetric Flexible Supercapacitor (ASFS) fabricated with PVA-KOH gel electrolyte was 1.4 V. And the fabricated ASFS device had energy density of 23.82 Wh kg−1 at 700 W kg−1. At the same time, two ASFSs in series could light up a small red bulb and maintained for more than 6 min.

Catalytic Activity for ORR of Pt Supported on Ordered Mesoporous Carbon with Network Structure

IntroductionIn order to realize carbon neutrality, working toward becoming a hydrogen-based society using renewable energy is essential, and hydrogen fuel cell vehicles will be one of the key devices. For further widespread deployment of fuel cell vehicles, cost reduction and performance enhancement of polymer electrolyte fuel cell (PEFC) systems are urgently needed. Recently, Pt/C-based electrocatalysts using mesoporous carbon as a support material have attracted attention, because well-designed mesopores can act as “accessible pores,” in which direct contact between ionomer and Pt surface is avoided, while the transport of protons, oxygen and generated water is not suppressed. Ordered mesoporous carbon (OMC) with a uniform nanopore structure is one of the ideal candidate materials for a catalyst support with accessible pores. However, with conventional synthesis methods, the typical OMC primary particle size is on the order of micrometers, and mass transport within the nanopores could be a difficult issue. In the present study, we focused on the development of OMC nanoparticles with a network structure. By use of nonionic surfactant micelles with phenol-formaldehyde resole resin as a carbon source, we obtained OMC nanoparticles with a novel hierarchical network structure (ns-OMC). To the best of our knowledge, there are no reports of the synthesis of ordered mesoporous carbon nanoparticles with a rigid network structure. In this study, in addition to the effect of synthesis conditions on the ns-OMC structure, a newly developed technique for the selective deposition of Pt nanoparticles within the ns-OMC nanopores, and the ORR activity of the Pt/ns-OMC catalysts, will be discussed in detail.ExperimentalNs-OMC powder was synthesized with resol-nonionic surfactant micelles as a carbon source and structure directing agent. Resol-F127 (nonionic surfactant) micelles were synthesized by mixing phenol, formaldehyde, water and F127 with sodium hydroxide. After hydrothermal treatment of the mixture, the resulting solid was filtered and washed thoroughly and dried under vacuum. The obtained powder was carbonized at 700 ºC followed by annealing at 1000 ºC. Pt was deposited on the ns-OMC powder by the reverse micelle and colloid techniques. Cyclic voltammetry and linear sweep voltammetry were carried out in N2-saturated and O2-saturated 0.1 M HClO4 solution at 25 ºC using the conventional rotating disk electrode (RDE) technique. Potential step cycling measurements simulating load cycling between 0.6 V and 1.0 V vs. RHE were carried out according to the FCCJ protocol. Coating of the catalyst on a glassy carbon disk substrate was carried out by an electrospray (ES) coating technique developed by our group [1]. N2 adsorption measurement at liquid nitrogen temperature was carried out. The morphology of the Pt/ns-OMC was observed by scanning transmission electron microscope (STEM).Results and discussionAs seen in the STEM images of the ns-OMC powder (Figure 1), the network structure formed by connection of OMC primary nanoparticles to form developed macropores is clearly observed. The OMC nanoparticles were tightly connected via a thick necking structure. The primary particle size of the OMC and neck thickness were controllable via the resol-F127 micelle concentration and the temperature during the hydrothermal treatment. Highly ordered nanopores were observed on the surface of the OMC particles, with a pore size of ca. 5 nm and an interpore distance of ca. 8 nm. The nanopores and the network structure were stable even after high temperature annealing at 1400 ºC. The BET specific surface area for the ns-OMC annealed at 1000 ºC was 814 m2 g-1, and the BJH pore size distribution curve of the ns-OMC showed a mesopore peak at 4.5 nm, which is in good agreement with the STEM observation. When Pt was deposited on the ns-OMC support, most of the Pt particles, with diameters of 4 to 5 nm, were dispersed on the support and, interestingly, located inside or right above the nanopore entrances. Careful STEM measurements revealed that the Pt particles were not deposited deeply within the OMC nanopores, but most were deposited at relatively shallow positions. RDE measurements revealed that the Pt/ns-OMC catalysts showed higher specific activity and mass activity for the ORR and much higher stability for the ECSA values during potential step cycling measurements in comparison with a commercial Pt/CB catalyst. The higher catalytic activity and durability observed for the Pt/ns-OMC catalyst may be due to a near-ideal distance between neighboring Pt particles deposited on the novel support. Aggregation and sintering of the Pt particles were successfully suppressed due to the nanopore structure.[1] S. Cho, K. Tamoto and M. Uchida, Energy Fuels, 34, 14853 (2020).Acknowledgement: This work was supported by the ECCEED’30-FC project from NEDO. Figure 1

A Cuckoo Filter-Based Name Resolution and Routing Method in Information-Centric Networking

Information-centric networking (ICN) is a new network architecture that routes content based on names to improve transmission performance. Therefore, the efficiency of name resolution and routing becomes a critical issue in ICN. The bloom filter-based routing scheme has gained significant attention for its ability to improve the memory efficiency of routing nodes in the network, but it cannot handle the movement or deletion of content and has a high false positive rate, which increases bandwidth consumption. In this paper, we propose a cuckoo filter-based name resolution and routing method where resolution requests are forwarded through a hierarchical network structure to the node closest to the content copy as much as possible to minimize latency. This method achieves reliable content removal and allows summaries of content to be exchanged between nodes for resolution error correction and information synchronization based on a modified cuckoo filter. The simulation results show that our method can effectively reduce the number of false positives, and it can reduce the additional overhead caused by processing false positives for a large-scale network by 50% compared with the bloom filter-based scheme.

Carbon Emission Reduction Effect of Spatial Correlation Network Structure Characteristics: From the Perspective of Urban Agglomeration

Based on social network and regression analyses, this study conducted research with regard to the spatial correlation and effect of carbon emissions in urban agglomerations of the Pearl River Delta (PRD) with the data of prefecture-level cities in Pearl River Delta urban agglomerations from 2001 to 2019. As the results suggested, ① the carbon emission links among cities in PRD urban agglomerations were relatively loose, the subordinate structure changed frequently, and the carbon emission exchanges and cooperation among cities need to be strengthened. ② The core-periphery structure of the spatial correlation network of carbon emissions was loose to a certain extent, the control ability of the central city to the overall network structure was weakened, and the marginal cities gradually changed from being controlled to adopting a controlling role. ③ The improvement in the overall network tightness and stability and the breaking of the network hierarchical structure significantly reduced regional carbon emissions, whereas the improvement in individual network centrality significantly promoted urban carbon emission reduction. The above analysis will help to provide reference for coordinating carbon dioxide emission reduction and low-carbon city development policies.

3D mesh transformer: A hierarchical neural network with local shape tokens

Self-attention networks have revolutionized Natural Language Processing (NLP) and are making impressive strides in image analysis tasks such as image classification and object detection. Inspired by this success, we specifically design a novel self-attention mechanism between local shapes and build a shape Transformer. We split the 3D mesh model into shape patches, which we call shape tokens, and provide polynomial fitting representations of these patches as input to the shape Transformer. The shape token encodes local geometric information and resembles the token (word) status in NLP. The simplification of the mesh model provides a hierarchical multiresolution structure, which allows us to realize the feature learning of a multilayer Transformer. We set high-level features formed by the shape Transformer as visual tokens and propose a vector-type self-attention mechanism to construct a 3D visual Transformer. Finally, we realized a hierarchical network structure based on local shape tokens and high-level visual tokens. Experiments show that our fusion network of 3D shape Transformer with explicit local shape context augmentation and 3D visual Transformer with multi-level structural feature learning achieves excellent performance on shape classification and part segmentation tasks.

Impact of finite size effect on applicability of generalized fractal and spectral dimensions to biological networks

In recent years, researchers have explored fractal dimension, spectral dimension, and multifractal analysis as ways of describing the emergent hierarchical structure of complex networks. However, fractality implies an infinite recursion that is impossible for finite networks describing real-world biological systems. We show that there is a substantial finite size effect on two widely used empirical methods (box-covering and sandbox) of estimating generalized fractal dimensions. As a partial solution to this issue we introduce here a generalized method for calculating network spectral dimension using a memory-biased random walk (MBRW). To observe the impact of network size, we start with an ensemble of networks representing a variety of biological systems, identify their community structures using Infomap, and use a modified stochastic block model to generate networks with similar community structure but varying size. We find that, compared to shortest-path-based generalized fractal dimension methods, the MBRW generalized spectral dimension (Dq) shows a clearer and more consistent ordering of networks by community structure for all orders (q) considered. We also find that, among the measures of multifractality and multispectrality, only MBRW multi-spectrality (range of Dq values) changes in a consistent direction under randomization of each level of community structure. Our results show that network size is an important consideration when comparing the fractal or spectral dimensions of real-world networks and that observing the interaction between network structure and an agent acting in time with memory provides insights into network structure not available through calculations based on purely topological features.

Multiscale lineament analysis and permeability heterogeneity of fractured crystalline basement blocks

Abstract. The multiscale analysis of lineament patterns helps define the geometric scaling laws and the relationships between outcrop- and regional-scale structures in a fracture network. Here, we present a novel analytical and statistical workflow to analyze the geometrical and spatial organization properties of the Rolvsnes granodiorite lineament (fracture) network in the crystalline basement of southwestern Norway (Bømlo Island). The network shows a scale-invariant spatial distribution described by a fractal dimension D≈1.51, with lineament lengths distributed following a general scaling power law (exponent α=1.88). However, orientation-dependent analyses show that the identified sets vary their relative abundance and spatial organization and occupancy with scale, defining a hierarchical network. Lineament length, density, and intensity distributions of each set follow power-law scaling laws characterized by their own exponents. Thus, our multiscale, orientation-dependent statistical approach can aid in the identification of the hierarchical structure of the fracture network, quantifying the spatial heterogeneity of lineament sets and their related regional- vs. local-scale relevance. These results, integrated with field petrophysical analyses of fracture lineaments, can effectively improve the detail and accuracy of permeability prediction of heterogeneously fractured media. Our results also show how the geological and geometrical properties of the fracture network and analytical biases affect the results of multiscale analyses and how they must be critically assessed before extrapolating the conclusions to any other similar case study of fractured crystalline basement blocks.

Measurement and evaluation of multi-function parallel network hierarchical DEA systems

Many organisations are composed of multiple departments connected either in series or in parallel, which may be further decomposed into a number of functions arranged in a hierarchical structure. Several researchers have successfully used appropriate Data Envelopment Analysis (DEA) modelling techniques to assess complex structures. However, to our knowledge, no-one has yet examined the case of measuring and evaluating a parallel network structure combined with a hierarchical one. This paper discusses the development of a multi-function parallel system with embedded hierarchical network structures. A linear additive decomposition DEA model and a non-linear multiplicative aggregation DEA model are proposed as alternatives to evaluate the operating performance of such a structure. The system, the sub-systems, and the efficiencies of their internal units, as well as their relationships, are identified. The system efficiency of the additive model is shown to be greater than or equal to that of the multiplicative model. To verify the applicability of our proposed models, we consider a hypothetical example of the measurement and evaluation of the performances of several Business Schools across a number of universities. Other envisaged areas of application of our structure could include supporting the evaluation of the supply chain management of a firm, or the determination of the most desirable ship design considering maintenance issues.

Characterization of a Class of Generalised Core-satellite Graphs Using Average Degree

Network equilibrium models are significantly distinct in supply chain networks, traffic networks, and e-waste flow networks. The idea of network equilibrium is strongly perceived while determining the tuner sets of a graph (network). Tuner sets are subsets of vertices of the graph G whose degrees are lower than the average degree of G, d(G) that can compensate or balance the presence of vertices whose degrees are greater than d(G). Generalised core-satellite graph <img src=image/13428150_01.gif> comprises <img src=image/13428150_02.gif> copies of <img src=image/13428150_03.gif> (the satellites) meeting in K_c (the core) and it belongs to the family of graphs of diameter two. It has a central core of vertices connected to a few satellites, where all satellite cliques need not be identical and can be of different sizes. Properties like hierarchical structure of large real-world networks, are competently modeled using core-satellite graphs [1, 2, 5]. This family of graphs exhibits the properties similar to scale-free network as they possess anomalous vertex connectivity, where a small fraction of vertices (the core) are densely connected. Since these graphs possess such a structural property, interesting results are obtained for these graphs when tuner sets are determined. In this paper, we have considered the graph <img src=image/13428150_04.gif>, with p > q, a subclass of the generalized core-satellite graph which is a join of η copies of the clique K_q and γ copies of the clique K_p with the core K₁. We have obtained the tuner set for this subclass and established the relation between the Top T(G) and the cardinality of the tuner set <img src=image/13428150_05.gif> through necessary and sufficient conditions. We analyze and characterize these graphs and obtain some interesting results while simultaneously examining the existence of tuner sets.

One-pot electrochemical synthesis of ultrathin trimetallic Mn-Sn-Co oxide coral-like nanostructure as free-standing active electrode material for energy storage applications

The present study proposes a facile and controllable approach for the design and fabrication of an ultrathin binder-free trimetallic oxide nanostructure on nickel foam as an efficient hybrid supercapacitor. The morphological and physicochemical characteristics of the as-prepared Mn-Sn-Co-1 oxide (MSCO-1) electrode were validated using various techniques. The integrated MSCO-1 electrode displayed a high area-specific capacitance of 4.15 F cm−2 at 1 A g−1, good rate capability, and excellent cycling stability of 92.9 % capacitance retention after 3000 CD cycles in a three-electrode system. Its superior electrochemical performance is related to the distinctive coral-like morphology with a hierarchical porous network structure and synergistic effects of the multi-component metal oxides, effectively improving the electroactive sites and assisting the ease penetration of both electrolyte ions and electrons. Likewise, the corresponding asymmetric supercapacitor (ASC) was successfully fabricated by assembling MSCO-1 and activated carbon (AC) as cathode and anode, respectively, exhibiting a high energy density of 39.6 Wh kg−1 at 800 W kg−1 along with superior cycling stability. Moreover, the asymmetric supercapacitor suggests a good state of health with a small leakage current and low self-discharge, which can maintain 1.2 V of its initial voltage (1.6 V) after eight hours of self-discharge test. We also demonstrated the illumination of a yellow LED indicator for more than 480 s. These exciting results render that the MSCO-1 nanostructure could be a promising electrode in developing energy storage systems for feasible applications.

Optical transmittance and electrical conductivity of silica glass with biserial and hierarchical network structures made of carbon nanofibers

Silica glass composites, with biserial and hierarchical percolative network made of carbon nanofibers (CNFs), was fabricated using a layer-by-layer technique and spark plasma sintering to obtain high optical transmittance and electrical conductivity. Owing to the network, the critical volume fraction, Vc, for the CNF percolation in the silica glass-matrix composite (0.5–0.7 vol%), when the electrical conductivity of the composite drastically increased with change from insulator (∼10−10 S/m) to conductor (∼10−1 S/m), is smaller than theoretical Vc predicted for the three-dimensional random orientation of CNFs (2.6 vol% for the CNF aspect ratio of 30). The conductivity of the composite with above the Vc of CNFs (∼10 S/m) is higher than that reported for the polymer-matrix composite (∼10−5–∼10−3 S/m). Furthermore, high optical transmittance was observed for the electrically conductive composite with Vc of CNFs.