Types Of Network Structures Research Articles

Vancomycin-Loaded Polycaprolactone Electrospinning Nanofibers Modulate the Airway Interfaces to Restrain Tracheal Stenosis.

Site-specific release of therapeutics at the infected trachea remains a great challenge in clinic. This work aimed to develop a series of vancomycin (VA)-loaded polycaprolactone (PCL) composite nanofiber films (PVNF-n, n = 0, 1, and 5, respectively) via the electrospinning technique. The physiochemical and biological properties of PVNF-n were evaluated by a series of tests, such as FT-IR, XRD, SEM-EDS, and antibacterial assay. The PVNF-n samples displayed a typical network structure of fibers with random directions. VA was successfully introduced into the PCL nanofibers and could be sustained and released. More importantly, PVNF-5 showed relatively good antibacterial activity against both methicillin-resistant Staphylococcus aureus (MRSA) and Streptococcus pneumoniae (SPn). Thus, PVNF-5 was covered onto the self-expandable metallic stent and then implanted into a New Zealand rabbit model to repair tracheal stenosis. Compared to a metallic stent, a commercial pellosil matrix–covered stent, and a PVNF-0–covered metallic stent, the PVNF-5–covered airway stent showed reduced granulation tissue thickness, collagen density, α-SMA, CD68, TNF-α, IL-1, and IL-6 expression. In conclusion, this work provides an anti-infection film–covered airway stent that in site restrains tracheal stenosis.

Temporal-Spatial Structure and Influencing Factors of Urban Energy Efficiency in China’s Agglomeration Areas

Energy efficiency has proved to be effective in mitigating greenhouse gas emissions and is significant to carbon neutrality targets. Urban agglomeration is the major engine of urbanization supporting economic growth. To optimizing the spatial exchange structure to improve regional energy efficiency by integrating the total factor energy efficiency model and social network analysis, this study constructs the spatial network of energy efficiency among cities within five major urban agglomerations in China for the period 2011–2018 and investigates their spatial association characteristics. The influencing factors of each spatial network structure are also explored by the quadratic assignment procedure method. The findings show that the spatial association of energy efficiency within each urban agglomeration presents a typical network structure, but with considerable disparity among urban agglomerations. Most cities in the Yangtze River Delta and Pearl River Delta are closely connected with each other, while the surrounding cities in the areas of Beijing-Tianjin-Hebei, Chengyu and the Middle Reaches of the Yangtze River highly depend on their corresponding central cities. The spatial adjacency and GDP per capita determine the urban spatial relationship of the energy efficiency within urban agglomerations. In addition, the spatial correlation of urban energy efficiency in the Beijing-Tianjin-Hebei, Chengyu and Middle Reaches of the Yangtze River areas is also affected by the differences in energy consumption, capital stock, number of labor force and pollutant emission. Some suggestions for improving urban energy efficiency are discussed.

Characteristics and Driving Factors of Spatial Association Network of China’s Renewable Energy Technology Innovation

Based on the provincial panel data of China from 2001 to 2016, this study uses the social network analysis approach to empirically investigate the characteristics and driving factors of the spatial association network of China’s interprovincial renewable energy technology innovation. The findings are as following. 1) The spatial association of China’s interprovincial renewable energy technology innovation exhibits a typical network structure. Moreover, its network density, network hierarchy and network efficiency are 0.3696, 0.6667 and 0.7833 in 2001 and 0.4084, 0.4764 and 0.7044 in 2016, respectively, implying the spatial association network became more and more complex and the interprovincial association strengthened during the sample period. 2) This spatial association network presents a “core-edge” distribution pattern. The positions and roles of various provinces vary greatly in the spatial association network. Specifically, the developed coastal regions such as Shanghai, Beijing and Tianjin have a degree centrality, closeness centrality and betweenness centrality of above 75, 80 and 10, respectively, indicating that they always play a central role in the network. However, the northeastern regions and the relatively backward central and western regions such as Heilongjiang, Jilin, Xinjiang, Hainan and Hebei only have a degree centrality, closeness centrality and betweenness centrality of below 20, 55 and 0.1, respectively, indicating that they are at a relatively marginal position. 3) The geographical proximity and the expansion of the differences in economic development level and R&D inputs are conducive to the enhancement of the spatial association of China’s renewable energy technology innovation.

The Influence of Grid-connected Distributed Generation on the Power Supply Reliability of Distribution Network

This paper analyzes the typical distribution network structure and the operating characteristics of distributed generation (DG). By analyzing the impact of grid-connected DG on equipment such as reclosing and spare power automatic switching, we calculate the power supply reliability indexs after the DG is connected to the grid under various operation modes such as reclosing, spare power automatic switching, de-sequencing distributed energy and islanding. Finally, the results of the reliability indexs can provide the theory and data for planning and improving the operating mode of grid-connected DG.

Spillovers of environmental regulation on carbon emissions network

Spatial association of carbon emissions between countries has more to do with trade activities. This paper applies the social network analysis to give a clear picture on the evolutionary characteristics of carbon emissions network of Organization for Economic Co-operation and Development (OECD) countries and investigates the technology spillovers of environmental regulation on the carbon emissions network. Empirical results show that: First, the carbon emissions network of OECD countries presents a typical network structure with increasing network density, correlation and decreasing network efficiency. Second, carbon is mainly transferred among European countries along with the international trade, and the impact of countries on the edge of the network is still small. Finally, the U-shape relationship between environmental regulation and carbon emissions network is identified. The innovation inhibition effect exists when the environmental regulation is in a low level, and after arriving at the threshold value, the innovation compensation effect begins to appear. Our findings implied that when making policies for carbon emissions reduction, countries should strengthen the cross-regional communication and fully consider the spatial correlation effect. Some international organizations are supposed to play a leading role in reducing global carbon emissions by controlling the carbon transfer.

Nanofibrillated Cellulose-Based Aerogels Functionalized with Tajuva (Maclura tinctoria) Heartwood Extract.

Aerogels are 3-D nanostructures of non-fluid colloidal interconnected porous networks consisting of loosely packed bonded particles that are expanded throughout their volume by gas and exhibit ultra-low density and high specific surface area. Cellulose-based aerogels can be obtained from hydrogels through a drying process, replacing the solvent (water) with air and keeping the pristine three-dimensional arrangement. In this work, hybrid cellulose-based aerogels were produced and their potential for use as dressings was assessed. Nanofibrilated cellulose (NFC) hydrogels were produced by a co-grinding process in a stone micronizer using a kraft cellulosic pulp and a phenolic extract from Maclura tinctoria (Tajuva) heartwood. NFC-based aerogels were produced by freeze followed by lyophilization, in a way that the Tajuva extract acted as a functionalizing agent. The obtained aerogels showed high porosity (ranging from 97% to 99%) and low density (ranging from 0.025 to 0.040 g·cm−3), as well a typical network and sheet-like structure with 100 to 300 μm pores, which yielded compressive strengths ranging from 60 to 340 kPa. The reached antibacterial and antioxidant activities, percentage of inhibitions and water uptakes suggest that the aerogels can be used as fluid absorbers. Additionally, the immobilization of the Tajuva extract indicates the potential for dentistry applications.

ОСОБЛИВОСТІ МЕРЕЖЕВОЇ ОРГАНІЗАЦІЇ ІННОВАЦІЙНОЇ ДІЯЛЬНОСТІ

The article examines the features of a network organization of innovative activity. A definite trend of development of a knowledge economy is the shift towards partnerships in the innovation sphere. In the work it is determined that many problems associated with creating an enabling environment for activating innovative processes on the basis of network interaction are still poorly studied. It makes appropriate to carry out further research in this sphere. The network form of interaction bases on the principles of equal partnership, agreed in time and space of behavior of participants. It brings together the whole process of developing, producing and disseminating innovations. A typical network structure is an open system that consists of a set of interconnected elements (participants). They interact and exchange information to create and enhance competitive advantage, have roughly equal access to core resources, and have equal direct relations with other participants. They all get benefit from network interaction, regardless of their size and type of activity. The formation of innovation networks is a complex process that involves the selection and voluntary integration of stakeholders into a single network. Modern innovation networks are based on the concept of open innovation, which involves the use of external ideas and technologies while allowing other stakeholders to use their own ideas. The process of formation of innovation networks involves the emergence of the initiator bringing together a number of enterprises in the efficient production of innovation. The network structure is a modern model for the redistribution of tangible and intangible assets, allowing its participants to reduce time and material costs to adapt to market conditions. The presence of horizontal links and the sharing of available resources significantly accelerates the process of transforming scientific knowledge into innovation and its commercialization. Increasing the interaction between participants leads to the accumulation of innovation potential and the activation of innovative processes.

Comparison of River Basin Water Level Forecasting Methods: Sequential Neural Networks and Multiple-Input Functional Neural Networks

To precisely forecast downstream water levels in catchment areas during typhoons, the deep learning artificial neural networks were employed to establish two water level forecasting models using sequential neural networks (SNNs) and multiple-input functional neural networks (MIFNNs). SNNs, which have a typical neural network structure, are network models constructed using sequential methods. To develop a network model capable of flexibly consolidating data, MIFNNs are employed for processing data from multiple sources or with multiple dimensions. Specifically, when images (e.g., radar reflectivity images) are used as input attributes, feature extraction is required to provide effective feature maps for model training. Therefore, convolutional layers and pooling layers were adopted to extract features. Long short-term memory (LSTM) layers adopted during model training enabled memory cell units to automatically determine the memory length, providing more useful information. The Hsintien River basin in northern Taiwan was selected as the research area and collected relevant data from 2011 to 2019. The input attributes comprised one-dimensional data (e.g., water levels at river stations, rain rates at rain gauges, and reservoir release) and two-dimensional data (i.e., radar reflectivity mosaics). Typhoons Saola, Soudelor, Dujuan, and Megi were selected, and the water levels 1 to 6 h after the typhoons struck were forecasted. The results indicated that compared with linear regressions (REG), SNN using dense layers (SNN-Dense), and SNN using LSTM layers (SNN-LSTM) models, superior forecasting results were achieved for the MIFNN model. Thus, the MIFNN model, as the optimal model for water level forecasting, was identified.

Phase formation and microstructure analyses of La0.6Sr0.4CoO3-δ-BaCe0.54Zr0.36Y0.1O2.95 composite material prepared by solid state mixing method

A composite material of La0.6Sr0.4CoO3-δ-BaCe0.54Zr0.36Y0.1O2.95 (LSC-BCZY) was prepared by mixing sol-gel derived LSC and BCZY powders in different weight percentage (wt%) ratio of LSC to BCZY (LSC:BCZY). The prepared composite powders were denoted as S1 (30:70), S2 (50:50) and S3 (70:30). The powders were characterized by an X-ray diffractometer (XRD), a Brunauer-Emmett-Teller (BET) surface area and porosity analyzer and a scanning electron microscope (SEM) equipped with an energy dispersive X-ray (EDX) spectrometer. XRD analysis confirmed that all of the powders were not pure enough due to the presence of impurity phases such as barium carbonate (BaCO3) and strontium carbonate (SrCO3) and unknown phases. S1 powder has the highest amount of impurity phases (81.19 %) and the largest BET surface area (4.82 m2 g−1). All of the powders formed typical clump-like network structure as proven by SEM analysis. EDX analysis revealed that the elemental compositions of La, Sr and Zr were deviated from their nominal mole fractions in all powders due to the Zr-rich clusters formation. The results indicate that the formation of pure and homogenous LSC-BCZY composite powder prepared by solid state mixing method requires further modification and improvement of the preparation method.

Effect of Cu addition and heat treatment on the microstructure and microwear of selective laser melted Mg–Al–Zn alloy

In this study, the microstructure, intermetallic phase, microhardness and wear resistance of unheated and heat-treated Mg-6Al-1Zn-xCu (x = 0, 0.5, 1, 2, and 4) alloys were investigated. The selective laser melted Mg-6Al-1Zn alloy that contained Cu had a typical continuous network structure that consisted of fine α-Mg grains and a eutectic phase. Uniformly distributed Mg2Cu was the main strengthening phase before heat treatment, and the analysis showed that the Mg2Cu was mainly present in the form of particles in the alloy. Most of the eutectic dissolved into the α-Mg matrix, and a granular Al2Cu phase was formed in the matrix after solution treatment. Al2Cu played a significant role in improving the high-temperature properties and wear resistance of the alloy. During the aging treatment, a helical phase (H phase) was formed that was rich in Cu and Al. Compared with those for the other Cu contents, the Mg-6Al-1Zn-2Cu alloy had the best wear resistance and antifriction properties.

VTAAN: Visual Tracking with Attentive Adversarial Network

Existing tracking methods might suffer from the performance degradation due to insufficient positive samples. A typical network structure is proposed to enrich positive samples by generating masks during the tracking process. Although this structure has achieved good results, it ignores the drift problem that occurs when the tracked object is very similar to the surrounding objects. This problem is particularly significant when background interference exists and similar objects appear. To handle this problem, in this paper, we propose a novel attentive adversarial network for visual tracking. Inspired by human visual cognitive system, we propose to employ an attention mechanism to focus on each region differing the target object from the background. At the same time, we use a variant of the cross entropy (CE) function to deal with the class imbalance problem. Our network shows favorable performance compared with state-of-the-art methods on existing tracking benchmark datasets. We conclude that our novel attentive adversarial network not only enriches positive samples in the feature space but also prevents the similarity drift problem.

Thermal decomposition, phase formation and microstructure analysis of surfactant assisted sol-gel derived La0.6Sr0.4CoO3-δ material

A single-phase of perovskite-type oxide material can be prepared at a processing temperature relatively lower than 1000 °C through a sol-gel method. However, it is affected by the nature of chemical additives employed during the synthesis process. In the present work, sol-gel derived lanthanum strontium cobaltite, La0.6Sr0.4CoO3-δ (LSC64) material is prepared using various non-ionic surfactants namely polyoxyethylene (10) oleyl ether (Brij-97), polyoxyethylene octyl phenyl ether (Triton-X-100) and polyoxyethylene (20) sorbitan monooleate (Tween-80). The prepared powders of the LSC material is subsequently subjected to the thermal decomposition, phase formation and microstructure analysis by a thermal gravimetric analyzer, an X-ray diffractometer and a scanning electron microscope (SEM), respectively. The as-synthesized powders are calcined at different temperatures based on the thermal gravimetric analysis results. X-ray diffractometer results reveal that all of the calcined powders consist of more than 90 % perovskite phase of LSC64 and other secondary phases such as cobalt oxide, lanthanum oxide and strontium carbonate. The Brij-97-based and Tween-80-based calcined powders have morphology of typical clump-like network structure, while the Triton-X-100-based calcined powder has morphology of flake-like network structure.

Deep learning in environmental remote sensing: Achievements and challenges

Various forms of machine learning (ML) methods have historically played a valuable role in environmental remote sensing research. With an increasing amount of “big data” from earth observation and rapid advances in ML, increasing opportunities for novel methods have emerged to aid in earth environmental monitoring. Over the last decade, a typical and state-of-the-art ML framework named deep learning (DL), which is developed from the traditional neural network (NN), has outperformed traditional models with considerable improvement in performance. Substantial progress in developing a DL methodology for a variety of earth science applications has been observed. Therefore, this review will concentrate on the use of the traditional NN and DL methods to advance the environmental remote sensing process. First, the potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed. A typical network structure will then be introduced. Afterward, the applications of DL environmental monitoring in the atmosphere, vegetation, hydrology, air and land surface temperature, evapotranspiration, solar radiation, and ocean color are specifically reviewed. Finally, challenges and future perspectives will be comprehensively analyzed and discussed.

Facile synthesis of molybdenum disulfide/poly (3,4-ethylenedioxythiophene) composite electrodes for high-performance flexible supercapacitors

In the research of flexible microsupercapacitors, particular structure electrodes with favorable electrochemical performances are in high demand. Here we report the synthesis and characterization of nanocomposite by vapor phase polymerization of 3,4-ethylenedioxythiophene with molybdenum disulfide in the presence of an oxidizing agent. The effects of exfoliated molybdenum disulfide in common organic solvents are investigated on the electrochemical properties of molybdenum disulfide/poly(3,4-ethylenedioxythiophene) composites. It is found that molybdenum disulfide/poly(3,4-ethylenedioxythiophene) film prepared by isopropanol solvent assisted exfoliated molybdenum disulfide nanosheets exhibits a typical porous network structure and superior electrochemical performances. On account of synergetic effects between uniformly distributed molybdenum disulfide and PEDOT polymer, the hybrid film shows a high volumetric capacitance of 423.6 F/cm3 and good cycling stability with 92% of its initial charge after 5000 cycles. Moreover, flexible interdigitated micro-supercapacitor assembled by molybdenum disulfide/poly(3,4-ethylenedioxythiophene) electrodes is evaluated, which displays a volumetric capacitance of 15.32 F/cm3. The assembled flexible device shows a huge potential for application in flexible electronic devices.

High-temperature deformation behavior of TiNi-Nb hypoeutectic alloy

The hot deformation behavior of a TiNi–Nb hypoeutectic alloy (Ti–16Ni–5Nb) was investigated via isothermal compression tests in the temperature range of 700−900 °C and strain rate range of 0.01−10 s−1. The effect of the deformation parameters on the deformation mechanism and microstructural evolution was characterized by means of scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, and differential scanning calorimetry. The results revealed that the as-cast TiNi–Nb hypoeutectic alloy consisted of NiTi2 intermetallic, α-Ti + NiTi2 lamellar eutectic microstructure, and transformed β-Ti. With increasing strain, the flow stress increased sharply to a peak value due to the work-hardening effect during the initial stage of deformation. The stress then decreased continuously owing to dynamic softening, reaching a steady state, which exhibited the typical features of dynamic recrystallization (DRX). With deformation, the NiTi2 phase transformed from a continuous network-type structure to a discontinuous dispersed structure. Flow localization bands induced the formation of microcracks as well as flow softening below the β transition temperature (Tβ), and DRX was typically observed above Tβ, especially at high temperatures. In addition, the thermal deformation activation energy increased initially and then decreased in the range of 229.05−261.71 kJ/mol. A developed constitutive model was established to predict the flow stress under high temperature deformation conditions. The correlation between the experimental data and predicted values of the flow stress confirmed the capability of the model to accurately predict the flow stress of the TiNi–Nb alloy.

Dual modification of silica aerogel monoliths

A facile and versatile method based on hydrolysis and subsequent condensation of silica alkoxides through sol–gel approach is proposed to obtain an internal-hydrophobic and surface-hydrophilic silica aerogel monoliths. Wet gel monoliths were modified in turn by the hydrophobic agent trimethychlorosilane (TMCS) and hydrophilic modifier 3-aminopropylsilanetriol and finished with supercritical CO2 drying. The silica aerogels were characterized by scanning electron microscopy (SEM), fourier transform infrared spectroscopy (FT-IR), water contact angle, and microcomputer-controlled electron universal tests. The characterization on microstructure of prepared internal-hydrophobic and surface-hydrophilic silica aerogel monoliths indicated that the monoliths have a typical network structure with a porous interior, which exhibits hydrophobic performance (Max. contact angle = 137°) and a hydrophilic surface (Min. contact angle = 60°). The dually modified silica aerogel monoliths were coated with a pure acrylic emulsion layer through which the compressive strength was enhanced greatly which proved that the proposed approach is valuable to silica aerogel modification.

Rational fabrication of chitosan/alginate/silica ternary aerogel beads adsorbent with free separation

Novel chitosan/alginate/silica ternary aerogel beads were successfully prepared by the orifice-coagulation bath with the ambient pressure drying method and utilising chitosan, sodium alginate, and tetraethoxysilane as the initial raw materials. The morphologies, porosity characteristics, and dye absorbencies of the ternary aerogel beads were also investigated, respectively. The obtained samples are milky light solid, the size of the spherical aerogel is uniform, its diameter can be controlled ~5 mm, the specific surface area is 169.4 m 2 g -1 , pore size distribution is concentrated in the 2-100 nm, which is a typical nanofilamentous network structure. Meanwhile, the ternary beads were tested for dyestuffs adsorption; the results showed that the obtained samples exhibit excellent adsorption properties, and can keep good sphericity as a class of novel adsorbents.

Construction of a Neural Network and its Application on Target Classification

With the arrival of big data age, the deep convolutional neural networks (DCNNs) with more hidden layers have a more complex network structure and more powerful ability than traditional machine learning methods for feature learning and feature expression. This paper first proposes a model of the DCNN to discuss the basic structure of model, convolutional feature extraction and learning algorithm of convolutional neural network; then, mainly introduces several aspects, that is, the construction of the typical network structure, the training methods, and the parameter settings of network model to be improved and optimized. Moreover, the network model is applied to the classification and recognition of Antarctic hydrological features and compared with some existing classification methods. The novelty of this paper mainly includes two aspects, i.e., the one is that the design and construction of the structure of deep neural network based on deep learning method are performed, namely, connection, weight, calculator, learning training of network, and other design. The other is classifying hydrological characteristics of Pritz Bay in Antarctica’s images by the DCNN. The results show that the correct recognition rate of the model method constructed by this paper is the highest. Finally, some problems in the current research are briefly summarized and discussed, and the new direction in the future development is forecasted.

The competitiveness of Ukraine’s banks in the world banking market

The article examines the market of banking services in Ukraine through comparing indicators of competitiveness of Ukrainian banks and banks with foreign capital in the domestic market and global market. Taking into account the network-type structure of banks, six groups of banks are determined according to the degree of branching, namely: systemically important banks, all-Ukrainian equilibrium banks, all-Ukrainian concentrated banks, regional banks, local individual banks, closed banks. A particular emphasis is placed on a range of banking services and pricing policies of banks groups. The classification of factors developed by M. Yokoi-Arai and N. Yoshino is used in order to assess the competitiveness of Ukraine’s banks in terms of effectiveness and volume of services provided, information technology and resource management. About fifty indicators of banking activites performed by groups of banks with domestic and foreign capital are compared, and also best-performing banks in these groups are described based on the analysis of 82 operating banks in Ukraine. The article presents evidence that, under current conditions in Ukraine, banks with domestic and foreign capital are represented in all categories of banking services. It is pointed out that the highest competitiveness of Ukraine’s banks is observed in developing the network of ATM terminals, promoting Internet banking and, thus, a wide coverage of banking services. It is noted that Ukraine’s banks are less competitive in providing services for big businesses, international companies, funding projects, innovations and start-ups. The findings of the research paper can be useful for educational purposes as well as for professionals in the banking sector.

Diamondoid Supramolecular Coordination Frameworks from Discrete Adamantanoid Platinum(II) Cages.

Recently, porous framework materials with various network-type structures have been constructed via several different approaches, such as coordination interactions, reversible covalent bonds, and non-covalent interactions. Here, we have combined the concepts of supramolecular coordination complex (SCC) and metal-organic framework to offer a new strategy to construct a diamondoid supramolecular coordination framework (SCF) from an adamantanoid supramolecular coordination cage as the tetrahedral node and a difunctional Pt(II) ligand as the linear linker via stepwise orientation-induced supramolecular coordination. The adamantanoid supramolecular coordination cage has four uncoordinated pyridyl groups, which serve as the four vertexes of the tetrahedral geometry in the diamondoid framework. As a result, this diamondoid SCF exhibits an adamantanoid-to-adamantanoid substructure with two sets of pores, including the interior cavity of the adamantanoid cage and the extended adamantanoid space between the individual cages in the framework. In addition, the shape-controllable and highly ordered self-assembly of nanometer-sized diamondoid SCF is observed as micrometer-sized regular octahedrons by evaporation under heating in DMSO. This study demonstrates the potential application of supramolecular coordination complexes in the precise construction of highly regulated porous framework materials.