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The minimum dominating set (MDS) of the network is a node subset of smallest size that every node in the network is either in this subset or is adjacent to one or more nodes of this subset. MDS has found wide applications, ranging from network monitoring, routing, to epidemic control, and text processing. However, the majority of existing studies on MDS problem are confined to single networks. In real world, more and more complex systems consist of a set of elements linked up by different types of connections, which are best modeled as multiplex networks with interacting network layers. Though vastly important, the MDS of the multiplex networks has not yet been formally defined and its application and identification remain open issues. In this article, we present the definition of the MDS of the multiplex network and show some of its possible applications. For solving the MDS problem of the multiplex network, we built a spin-glass model and solve it through the belief-propagation (BP) equations under the replica symmetry mean-field theory. As a consequence, we can predict the relative size of the MDS of the multiplex network theoretically and we can propose a BP-guided decimation algorithm to construct an approximate optimal dominating set in practice. Then the algorithm is improved in both accuracy and efficiency by embedding a novel multiplex network-oriented leaf-removal strategy. The effectiveness of the proposed algorithms is finally verified by comparing with other methods on a number of the multiplex network examples.

Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection. However, very few existing methods explicitly take into account learning the link information of the marking-points, resulting in complex post-processing and erroneous detection. In this paper, we propose an attentional graph neural network based parking-slot detection method, which refers the marking-points in an around-view image as graph-structured data and utilize graph neural network to aggregate the neighboring information between marking-points. Without any manually designed post-processing, the proposed method is end-to-end trainable. Extensive experiments have been conducted on public benchmark dataset, where the proposed method achieves state-of-the-art accuracy. Code is publicly available at \url{https://github.com/Jiaolong/gcn-parking-slot}., Comment: Accepted by RAL

The feedback vertex set (FVS) problem of network aims to find a vertex subset of smallest size that contains at least one vertex of each cycle of the network. The FVS problem has found wide applications, ranging from circuit design, network security and network control and observation etc. However, the majority of existing studies on FVS problem are confined to single networks. In this brief, under the consideration of one kind of control problem of multiplex network, we introduce the concept of FVS of multiplex network. The control problem of multiplex network is naturally mapped into the FVS problem of multiplex network. We then propose two simulated annealing (SA) algorithms to solve the directed FVS problem of multiplex network and undirected FVS problem of multiplex network respectively. The effectiveness of the SA algorithms is finally verified on both artificial multiplex networks and real-world multiplex networks.

The dismantling problem and minimum vertex cover (MVC) problem of network are two fundamental NP-hard problems where the former aims to find a minimal subset of nodes whose removal leaves the network broken in small components of sub-extensive size and the latter is also to find a minimal vertex set which contains at least one incident vertex of every edge. Both of them have a wide range of applications related to circuit design, communication, network security, transportation, epidemic control, molecular biology and economics. In this brief, we first propose a novel belief-propagation equation for the spin glass model of the MVC problem. A belief-propagation-guided decimation (BPD) algorithm is then presented which could construct approximate optimal vertex cover of network. In addition, based on the relationship between the network dismantling problem and the MVC problem, we propose a simple and fast algorithm formed by the MVC and a general node reinsertion strategy, for solving the network dismantling problem. The effectiveness of the proposed algorithms is finally verified by comparing with other well-known algorithms on a number of real world networks.

Deer species has both scientific research and economic value, and half of these species, however, are listed as endangered animals. For the conservation purpose, we designed a novel universal deer-specific PCR primer pair based on an evolutionarily conservative coding sequence (i.e., CEP295NL gene) across some deer species. This primer pair was successfully amplified and sequenced, showing around ~ 540 bp in cervids. Validation results showed that it can be utilized to develop a reliable and simple diagnostic tool for distinguishing other closely related species, as well as possibly interspecific identification amongst cervids.

Coal seam water injection is a widely used dust reduction technology in Chinese coal mines. During the process of coal seam water injection, a large amount of broken coal particles will accumulate in the pore, which will affect the flow characteristics of water. It's very important for improving the on-site coal seam water injection and dust reduction technology to study the influence of coal particles on the water migration law. In this paper, a spontaneous upward imbibition experiment was used to study the effect of coal particles stacked in front fractures on water migration in coal seam water infusion. Then, a mathematical model was established to express imbibition speed and stable imbibition height. The results show when the largest particle size is selected, theoretical calculation results of stable imbibition height is closest to the experimental data; coal particles with a size of 564–1589 μm have the greatest influence on the wetting phase of coal seam water infusion; the metamorphic grade and particle size of the samples mainly affect the initial stage of water migration, and the initial imbibition speed of each coal sample is quite different. However, with increasing imbibition height, the imbibition speed difference of each coal sample gradually decreases.

In this study, a systematic research was conducted to compare the performances of the regression model and artificial neural network in predicting the nugget diameter of spot-welded joints by monitoring the dynamic power signature. The TC2 titanium alloy with a thickness of 0.4 mm was used as the welding material, and a high-frequency precision spot welder was used to join the titanium alloy sheets. The dynamic welding current curve was obtained using the Rogowski coil, while the voltage curve was detected via two leads clipped onto the upper and lower electrodes during the entire welding process. The variations in the welding power signal in the welding process were investigated, and the characteristics of the power signals for different welding currents and electrode forces were analyzed. The power signals of different types of welding joints varied significantly. Five characteristics were extracted from the power signal to describe the shape of the curve. The stepwise regression analysis and back propagation neural network were respectively used to classify the welding joints into three categories: bad welds, good welds, and welds with expulsion. The performances of the two established prediction models were compared, and their behavioral discrepancies were attributed to their own data-mapping capabilities. Keywords: Nugget size, Dynamic power, Quality assessment

As an important part of coastal wetlands, tidal flat wetlands provide various significant ecological functions. Due to offshore pollution and unreasonable utilization, tidal flats have been increasingly threatened and degraded. Therefore, it is necessary to protect and restore this important wetland by monitoring its distribution. Considering the multiple sizes of research objects, remote sensing images with high resolutions have unique resolution advantages to support the extraction of tidal flat wetlands for subsequent monitoring. The purpose of this study is to propose and evaluate a tidal flat wetland delineation and classification method from high-resolution images. First, remote sensing features and geographical buffers are used to establish a decision tree for initial classification. Next, a natural shoreline prediction algorithm is designed to refine the range of the tidal flat wetland. Then, a range and standard deviation descriptor is constructed to extract the rock marine shore, a category of tidal flat wetlands. A geographical analysis method is considered to distinguish the other two categories of tidal flat wetlands. Finally, a tidal correction strategy is introduced to regulate the borderline of tidal flat wetlands to conform to the actual situation. The performance of each step was evaluated, and the results of the proposed method were compared with existing available methods. The results show that the overall accuracy of the proposed method mostly exceeded 92% (all higher than 88%). Due to the integration and the performance superiority compared to existing available methods, the proposed method is applicable in practice and has already been applied during the construction project of Hengqin Island in China.

Five published Liquid Sublayer Dryout (LSD) models are assessed using the Groeneveld Look-up table 2006 (LUT-2006) and critical heat flux (CHF) experimental data in circular tube under both subcooled boiling conditions and saturated boiling conditions. The flow regime transition from inverted annular flow to dispersed flow at post-CHF is postulated as the upper limit of the LSD model application range in saturated boiling region. Lee and Mudawar’s LSD model modified by W.X. Liu shows good agreement when void fractions is lower than 0.7. The modified L&M’s LSD model is coupled into subchannel code, COBRA-TF, to predict DNB type CHF in rod bundles. In present study, the gap clearance or thermal equivalent diameter of rod bundles is adopted as characteristic length in the modified L&M’s LSD model. For rod bundles with simplified grid spacers, Karman velocity distribution equation is utilized to calculate the velocity distribution normal to pin wall. The rod bundle CHF predictions by COBRA-TF coupled with LUT-2006, Bowring’s CHF correlation and the modified L&M’s LSD model, are compared with the CHF experimental data in 2 × 2 rod bundles with non-uniform power profile. Based on reasonable predictability on both the input power and axial position at CHF, the deviations of CHF predictions by the modified L&M’s LSD model, which adopts gap clearance instead of thermal equivalent diameter as characteristic length, and Bowring’s CHF correlation are within ±20% as void fraction approaching to 0.7.

Fabricating a high-performance photocatalyst to efficiently solve serious environmental problems is an urgent affair. Herein, a series of MoS2 /ZnO composites were successfully fabricated through a facile hydrothermal route using Na2 MoO4 , (NH2 )2 CS and urchin-like ZnO as precursors. According to the results of XRD and XPS, it was found that ZnS appeared in MoS2 /ZnO composite; meanwhile, the content was positively correlated with the weight of the precursor (NH2 )2 CS. It should be noted that the morphology and the metallic phase content of MoS2 grown in situ on the surface of ZnO were affected by the molar ratio of Na2 MoO4 and ZnO. Benefiting from the special dual II heterojunctions of MoS2 /ZnS/ZnO ternary composite, the material exhibited excellent charge separation and transfer performances. In the photocatalytic measurements, the MoS2 /ZnS/ZnO (Na2 MoO4 :ZnO 1:2 MZ2) composite not only exhibits excellent photocatalytic CrVI reduction activity of 42.3×10-3 min-1 , but also displays remarkable adsorption performance (nearly 32.1 %) for Cr2 O 2 - 7 . In addition, the ternary composite shows dominant photocatalytic CrVI reduction activities compared to other photocatalysts. This work provides a high-efficient MoS2 /ZnS/ZnO ternary photocatalyst for environmental treatment.

Amino acid as a green and non-toxic capping agent was used to regulate TiO2 crystal face exposure and morphology replaced common inorganic F− ions. In this paper, the hexagonal multi-walled tubular anatase TiO2 with hierarchical structure exposed to {010} and {101} planes were synthesized by solvothermal method via optimizing the amino acid (glutamine). The microstructure, crystal plane exposure and photocatalytic activity of TiO2 samples were tested comprehensively, and the formation mechanism of hexagonal multi-walled tubular anatase TiO2 was proposed. The {010} active plane exposure ratio was 70.76% thanks to the appropriate ratios of glutamine to SO42− and the band gap reduced to 2.95 eV for better visible region absorption. The degradation rate of the material for MO reached 94.6% within 80 min, which was 9.37 times than the sample without capping agent. Both high surface area and the synergy effect between the {010} and {101} facets contribute to the improvement of photocatalytic activity.

This paper numerically investigates bubble nucleation, growth, merger, and departure characteristics as well as the heat transfer distributions on the surface, and compared the simulation results with experimental results. Micro-heaters are utilized in experiments to provide constant temperature as well as measure the time-resolved heat flux variations. Bubble images and corresponding heat flux distributions under bubbles are obtained in experiments. A 3-dimensional computational domain with two symmetry planes is built and 5 heating elements are arranged at the bottom surface of the domain to simulate the real heaters used in experiments. The temperatures of these elements are set constant and the local heat fluxes were calculated. Liquid-vapor interface is captured with a volume of fluid (VOF) method in numerical simulations. Bubble growth and merger characteristics and heat flux distributions under bubbles calculated in numerical simulations are analyzed and compared to experimental observations. The heat transfer contributions of different heat transfer modes are calculated. The heat transfer mechanisms during nucleate boiling are better interpreted by the coupling of bubble dynamics and time- and space-resolved heat flux variations on the surface.

In this work, a systematic research was undertaken to predict the nugget size of the welding joints in real time through monitoring the dynamic power signature for TC2 titanium alloy. The welding current in the welding process was measured by a Rogowski coil and the welding voltage was obtained by means of clipping two leads onto the upper and lower electrodes during the welding process. The power signal and the corresponding dynamic resistance were compared with each other. As the constant high frequency alternating welding current was employed in the whole welding process, the power waveform indicated the similar characteristics of the dynamic resistance, besides; the power signal could better reveal an explicit physical meaning of the heat input applied to the welding zone. The disparities and differences of the power waveforms with different welding qualities were also investigated. Four features were extracted from the power waveform so as to indicate the variations of welding power profile in the welding process. A process quality prediction model based on Kriging method was set up to correlate the four extracted characteristics with nugget diameter of the welding joints. Verification test results proved that the method proposed in this paper effectively evaluated the welding qualities within the ranges of process parameters being studied, which could monitor the welding performances in the resistance spot welding process as feasibly and effectively as possible.

Herein, silver (Ag)-based urchin-like E-graphite carbon nitride/titanium dioxide (E-g-C3N4/TiO2) ternary composite was successfully synthesised via hydrothermal process and it showed excellent photocatalytic activity. The large interfacial contact between three-dimensional (3D) urchin-like TiO2 (UT) and g-C3N4 leads to high photocatalytic activity, compared with pure 3D UT. To enlarge the response of light and enhance the charge carriers’ transfer, the Ag nanoparticles with average sizes of 13 nm were loaded on the surface of g-C3N4/TiO2. Photodegradation test results of RhB shown that the 3% Ag-based urchin-like E-g-C3N4/TiO2 composite has the highest catalytic activity, which reaches 97.98%. Thus, the material has a promising application for the degradation of organics.

COVID-19 has infected millions of people, with deaths in more than 200 countries It is therefore essential to understand the dynamic characteristics of the outbreak and to design effective strategies to restrain the large-scale spread of the epidemic In this paper, we present a novel framework to depress the epidemic spreading, by leveraging the decentralized dissemination of information The framework is equivalent to finding a special minimum dominating set for a duplex network which is a general dominating set for one layer and a connected dominating set for another layer Using the spin glass and message passing theory, we present a belief-propagation-guided decimation (BPD) algorithm to construct the special minimum dominating set As a consequence, we could immediately recognize the epidemic as soon as it appeared, and rapidly immunize the whole network at minimum cost © Copyright © 2021 Wang, Zhang, Zhu, Jiang, Wu, Xu and Zhao

Silver-Silver direct bonding has been developed as an alternative interconnection technique for 3D power integration which can form high-strength and low-resistance Ag joints under solid-state conditions. However, the reliability and failure mechanisms of these direct bonded joints have not been characterized yet. In this study, four direct bonded structures were accelerated aged with two different temperature cycling tests (TCT) in the temperature range between -55 °C and 220 °C. The thermally induced stresses in the metallization during TCT can be relieved by elastic and plastic deformation of the Ag films and the debonding at the interface. At higher temperatures above 150 °C, plastic deformation mechanisms dominate the stress relaxation. Stress-driven void migration along grain boundaries may lead to cracking and interfacial failure. Metallization with a diffusion barrier and large grain size can restrict the void formation and diffusion, therefore, provide a higher temperature capability. The results indicate that both the adhesion of the Ag metallization and the Ag-Ag direct bonding remain stable after 1000 thermal cycles.

Structure heterogeneity and host nucleic acids contamination are two major problems for virus-like particles (VLPs) produced by various host cells. In this study, an in vitro optimized disassembly-purification-reassembly process was developed to obtain uniform and nucleic acid free hepatitis B core (HBc) based VLPs from E. coli fermentation. The process started with ammonium sulfate precipitation of all heterogeneous HBc structures after cell disintegration. Then, dissolution and disassembly of pellets into basic subunits were carried out under the optimized disassembly condition. All contaminants, including host nucleic acids and proteins, were efficiently removed with affinity chromatography. The purified subunits reassembled into VLPs by final removal of the chaotropic agent. Two uniform and nucleic acid free HBc-based VLPs, truncated HBc149 and chimeric HBc183-MAGE3 I, were successfully prepared. It was found that disassembly degree of HBc-based VLPs had a great influence on the protein yield, nucleic acid removal and reassembly efficiency. 4 M urea was optimal because lower concentration would not disassemble the particles completely while higher concentration would further denature the subunits into disordered aggregate and could not be purified and reassembled efficiently. For removal of strong binding nucleic acids such as in the case of HBc183-MAGE3 I, benzonase nuclease was added to the disassembly buffer before affinity purification. Through the optimized downstream process, uniform and nucleic acid free HBc149 VLPs and HBc183-MAGE3 I VLPs were obtained with purities above 90% and yields of 55.2 and 43.0 mg/L, respectively. This study would be a reference for efficient preparation of other VLPs.

There is currently enormous and growing demand for flexible electronics for personalized mobile equipment, human-machine interface units, wearable medical-healthcare systems, and bionic intelligent robots. Cellulose is a well-known natural biopolymer that has multiple advantages including low cost, renewability, easy processability, and biodegradability, as well as appealing mechanical performance, dielectricity, piezoelectricity, and convertibility. Because of its multiple merits, cellulose is frequently used as a substrate, binder, dielectric layer, gel electrolyte, and derived carbon material for flexible electronic devices. Leveraging the advantages of cellulose to design advanced functional materials will have a significant impact on portable intelligent electronics. Herein, the unique molecular structure and nanostructures (nanocrystals, nanofibers, nanosheets, etc.) of cellulose are briefly introduced, the structure-property-application relationships of cellulosic materials summarized, and the processing technologies for fabricating cellulose-based flexible electronics considered. The focus then turns to the recent advances of cellulose-based functional materials toward emerging intelligent electronic devices including flexible sensors, optoelectronic devices, field-effect transistors, nanogenerators, electrochemical energy storage devices, biomimetic electronic skins, and biological detection devices. Finally, an outlook of the potential challenges and future prospects for developing cellulose-based wearable devices and bioelectronic systems is presented.

Designing and fabricating an efficient and reusable photocatalyst has become a pursuit, as photocatalyst plays a key role in solving the energy crisis and the environmental pollution. However, the major stumbling blocks such as low photo-efficiency, non-reusability and serious electron–hole recombination hinder practical application of photocatalyst. Herein, a novel self-growing ZnO with numerous nanosheets grown on the surface of dumbbell-shape have been successfully synthesized via a simple hydrothermal growth, meanwhile, the nanosheet was exposed high-energy (101) crystal plane. Electrochemical impedance spectroscopy measurements show that nanosheets with exposed (101) facet were dominantly promote charge generation, separation and transfer. In order to inhibit the photocorrosion of ZnO, the graphene was loaded on the surface of ZnO via secondary hydrothermal process. Moreover, in the measurements of degradation RhB, the rate constant of samples follow the sequence: RGO@ZnO-10 (0.0568 min−1) > ZnO-10 (0.0460 min−1) > ZnO-9 (0.0148 min−1), which is demonstrated that nanosheet enhances the photoelectrocatalytic properties. Therefore, the novel ZnO structure not only has a promising application in dealing with environmental and industrial pollutants, but also provides a new strategy to design and fabricate reusable and high efficiency photocatalysts.

The critical heat flux (CHF) is one of the main thermal-hydraulics safety limits in water-cooled reactors. At CHF, a film of vapor is formed on the heated wall and the wall temperature can sharply increase in a short period of time, which may lead to damage of the heater surface. In spite of many studies, the CHF mechanisms are still not well understood due to the complexity of this two-phase phenomenon and its dependence on local thermal-hydraulic conditions. Two-phase boiling experiments have been conducted to observe CHF and post-CHF behavior in prototypical conditions for small modular reactors (mass flux: 560–1570 kg/m2.s, pressure: 7.6–16.4 MPa, inlet subcooling: 160–440 kJ/kg) in a 2 × 2 square rod bundle geometry with a chopped-cosine power profile at the University of Wisconsin-Madison. The fuel rods temperature history during and following CHF were used in the solution of the two-dimension inverse heat transfer problem to estimate the wall temperatures and wall heat fluxes. This analysis showed different transient boiling curves for different flow regimes that differ qualitatively and quantitatively from the typical boiling curve considered in steady-state two-phase heat transfer analysis. The experiments suggest that the inverse heat transfer analysis approach can be used to estimate the post-CHF heat transfer and to better understand the CHF mechanisms at high pressure typical of water-cooled reactors.

A temperature controlled low field nuclear magnetic resonance (LF-NMR) T2 relaxometry technique based on the mobility changes of water trapped in hydrogels, was successfully used for on-line determination of the sol-gel transition temperature for chitosan/β-glycerophosphate (CS/GP) hydrogels in real time. The LF-NMR results indicated that the gelation temperature decreased gradually with increasing GP concentration, and the results were supported by both thermogravimetric differential scanning calorimetry (DSC) and rheological findings; however, LF-NMR allows non-destructive monitoring of samples during continuous heating. Moreover, as the mobility of water molecules varies greatly during the sol-gel phase transition, the LF-NMR measurement was more sensitive and accurate (RSD ≤ 0.1 %, n = 5) compared with DSC (RSD: 1.2 %–3.7 %, n = 5) and rheology (RSD: 1.1 %–2.3 %, n = 5).

© 2018 Elsevier Ltd A parallel algorithm is proposed for scalable generation of large-scale tetrahedral meshes. The key innovation is the use of a mesh-simplification based domain decomposition approach. This approach works on a background mesh with both its surface and its interior elements much larger than the final elements desired, and decomposes the domain into subdomains containing no undesirable geometric features in the inter-domain interfaces. In this way, the most time-consuming part of domain decomposition can be efficiently parallelized, and other sequential parts consume reasonably limited computing time since they treat a very coarse background mesh. Meanwhile, the subsequent parallel procedures of mesh generation and improvement are most efficient because they can treat individual subdomains without compromising element quality. Compared with published state-of-the-art parallel algorithms, the developed parallel algorithm can reduce the clock time required by the creation of one billion elements on 512 computer cores from roughly half an hour to less than 4 minutes.

Seeking nodes whose removal can effectively dismantle networks is closely related to the robustness of networks under targeted attacks or malicious software. Modern systems, such as critical infrastructure networks and cyber-physical systems, however, become increasingly dependent on others, which can be encapsulated into the framework of interdependent networks. In this brief, we focus on the optimal dismantling of interdependent networks, and propose a novel algorithm, named inverse explosive percolation (IEP), to find the optimal removal nodes. The IEP proceeds by first identifying the nodes which make the least contribution to the giant mutual connected cluster of interdependent networks. When nodes have the same contribution, the one with the smallest overlapping degree is selected. We apply the IEP algorithm to interdependent networks composed of real-world power grids, Internet networks and artificial networks, and find IEP performs much better than the existing methods in various kinds of networks. Based on its efficiency, IEP can be applied to large-scale systems.

In this paper, we explore the effects of rational conformity behavior on the evolution of cooperation in prisoner's dilemma. In general, we think individual updates strategy is based on the difference in income between himself and his neighbors. In real life, in order to avoid risks, they may be consistent with most individuals in the group, because they are not the worst. Therefore, we divide the players into two categories, one is traditional players and the other is rational conformists who update their strategies are based on the two factors: payoffs and the behavior of most individuals in their nearest neighbors. Through a large number of simulations, we find that, rational conformity behavior can promote cooperation in the prisoner's dilemma game, and the greater the proportion of rational players, the more obvious the promotion of cooperation. Our work may provide further insight in understanding the evolution of cooperation, players selectively follow others and make some adjustments according to the current environment to make their own situation better.

The hollow flower-like Gd-TiO2 (FGT) photocatalytic materials have been synthesized through a facile hydrothermal process by using gadolinium nitrate hexahydrate as a Gd source. The FGT was characterized with scanning electron microscopy, transmission Electron Microscope, X-ray diffraction, X-ray photoelectron spectroscopy, and UV–Vis diffuse reflectance spectroscopy. The results show that the crystal structure of the synthesized material is rutile, the morphology is three-dimensional hollow flower-like. Gd-TiO2 material with the doping amount of 0.125 mol% exhibit an enhanced phoocatalytic activities on the degradation of RhB under simulated sunlight irradiation, which reaching to 95%. Therefore, the FGT shows favorable photocatalytic performance toward organic pollutant degaradation field.

Presently, a tetrahedral mesher based on the Delaunay triangulation approach may outperform a tetrahedral improver based on local smoothing and flip operations by nearly one order in terms of computing time. Parallelization is a feasible way to speed up the improver and enable it to handle large-scale meshes. In this study, a novel domain decomposition approach is proposed for parallel mesh improvement. It analyses the dual graph of the input mesh to build an inter-domain boundary that avoids small dihedral angles and poorly shaped faces. Consequently, the parallel improver can fit this boundary without compromising the mesh quality. Meanwhile, the new method does not involve any inter-processor communications and therefore runs very efficiently. A parallel pre-processing pipeline that combines the proposed improver and existing parallel surface and volume meshers can prepare a quality mesh containing hundreds of millions of elements in minutes. Experiments are presented to show that the developed system is robust and applicable to models of a complication level experienced in industry.

Recent improvements in flexible electronics have increased the need to develop flexible and lightweight power sources. However, current flexible electrodes are limited by low capacitance, poor mechanical properties, and lack of cycling stability. In this article, we describe an ionic liquid-processed supramolecular assembly of cellulose and 3,4-ethylenedioxythiophene for the formation of a flexible and conductive cellulose/poly(3,4-ethylenedioxythiophene) PEDOT:poly(styrene sulfonate) (PSS) composite matrix. On this base, multiwalled carbon nanotubes (MWCNTs) were incorporated into the matrix to fabricate an MWCNT-reinforced cellulose/PEDOT:PSS film (MCPP), which exhibited favorable flexibility and conductivity. The MCPP-based electrode displayed comprehensively excellent electrochemical properties, such as a low resistance of 0.45 Ω, a high specific capacitance of 485 F g–1 at 1 A g–1, and good cycling stability, with a capacity retention of 95% after 2000 cycles at 2 A g–1. An MCPP-based symmetric solid...

Spatial epidemic spreading, a fundamental dynamical process upon complex networks, attracts huge research interest during the past few decades. To suppress the spreading of epidemic, a couple of effective methods have been proposed, including node vaccination. Under such a scenario, nodes are immunized passively and fail to reveal the mechanisms of active activity. Here, we suggest one novel model of an observer node, which can identify infection through interacting with infected neighbors and inform the other neighbors for vaccination, on multiplex networks, consisting of epidemic spreading layer and information spreading layer. In detail, the epidemic spreading layer supports susceptible-infected-recovered process, while observer nodes will be selected according to several algorithms derived from percolation theory. Numerical simulation results show that the algorithm based on large degree performs better than random placement, while the algorithm based on nodes' degree in the information spreading layer performs the best (i.e., the best suppression efficacy is guaranteed when placing observer nodes based on nodes' degree in the information spreading layer). With the help of state probability transition equation, the above phenomena can be validated accurately. Our work thus may shed new light into understanding control of empirical epidemic control.

Real-time hand gesture recognition in complex environments has many challenges, such as poor real-time performances and robustness to environmental changes. This paper takes the hand gesture control of the unmanned vehicle as the application background, and focuses on the gesture detection and recognition of video streams based on deep learning in the complex environment. In this paper, we detect the hand in a complex environment by training the ssd_mobilenet model, and initialize the tracking with kalman filter. Then, we detect the hand keypoints by following the architecture of Convolutional Pose Machines (CPMs), in order to obtain the belief maps for all keypoints that are used as the train sets of Convolutional Neural Networks (CNNs). Finally, based on the results obtained by our classification, this paper proposes a method of multi-frame recursion to minimize the influences of redundant frames and error frames. In this paper, eight kinds of gestures for controlling vehicle are identified. The experimental results show that our method can successfully realize real-time hand gesture recognition in the video streams. The recognition accuracy can reach 96.7%, and the average recognition speed reaches 12 fps, which basically meets the real-time requirements and successfully applys to mobile terminals such as TX2 for engineering practice.

In recent years, renewable energy generations, such as wind power generation, have a rapid development in a large scale all over the world. The development trend of renewable energy, from auxiliary power supply to main power supply even leading power supply, is becoming increasing obviously. Due to the fluctuation and intermittence of wind power, the safe and stable operation of power system has been threatened. The modeling of wind power generation system has become a key factor and hotspot issue that influence the interaction between wind plant and power grid, with scholars domestic and overseas all weight in. Hence institutions and organizations, such as GE, IEC, WECC, EPRI, CEPRI, also have attached great importance to this issue, relevant research guideline or report about wind power generation system modeling have been published continuously. The existing research both in domestic and outside is presented first in this paper, then the problems in modeling are analyzed, suggestions and prospects for future research are given at last.

In recent decades, many countries have suffered power outages and these accidents were often caused by a small disturbance, but because of the connection structure between the circuits, even a small mistake will cause the power grid cascade to fail and paralyze the network in a large area. In order to find a way to enhance the network robustness, this paper proposes three defense methods based on different ratio: the k-shell value ratio, degree ratio, and residual load ratio. We would compare the quality of the three defense methods by the relative size of the largest connected component after cascading failure. Besides, we also compare the time, the total number of crashed nodes, and the speed of cascading failure progress under three defense methods. From the experimental results, it is found that the defense methods based on the k-shell value ratio and the degree ratio have their own advantages in different situations.

In this paper, relevant twenty features were extracted from the dynamic resistance signal of the welding operation and subsequent mathematical models incorporating the qualifying features were developed to investigate the relationship among dynamic resistance, welding quality and welding parameters based on the stepwise regression method. The results of the analysis of variance indicated the peak point location t 1 , the initial resistance value r 0 , and the first slope k 1 had little correlation with welding process parameters. The welding current is the most significant factor affecting the dynamic resistance signal, followed by welding time and electrode pressure. The welding current has significant effects on all the extracted features, while the electrode pressure only affects seven features. The model utilizing the features of r mean , r e , r α , r β and r med was developed to predict the welding quality. r mean is the most significant factor in the model whose maximum relative error is less than 10%.

The effect of the coal seam water infusion to reduce dust is directly related to the development degree of the coal structure under the action of the effective stress. Therefore, this paper first summarizes several basic effective-stress models and proposes the effective-stress model considering the capillary force. Thereafter, the nuclear magnetic resonance (NMR) experiment system high-voltage seepage module is used to achieve the saturation coal sample T[Formula: see text] and water infusion coal sample T2test under different mechanical environments. And the fractal dimension was calculated based on the results of porosity and pore size test. Finally, the parameters were sequentially substituted into effective-stress models. The calculation results were compared and analyzed. The results show that the saturation levels of the irreducible fluid of Sample DLT and Sample XLZ are 92.93% and 93.66%, respectively, indicating that these samples are dense porous media structures. Meanwhile, the porosity test shows the overall porosity with the water infusion pressure and confining pressure changes being consistent with the conventional law; the partial porosity caused a slight fluctuation due to different sensitivities of different pores to stress. The effective stresses of the capillary force and the calculated effective stress of the body are closest to the fractal effective stress, so the difference between the two values is negligible due to the small capillary force, which indicates that during the high-pressure water infusion, the capillary force in the meso-structure hardly affects the mechanical environment. Based on the theoretical research results, this paper can provide a theoretical reference for the determination of water infusion parameters under different on-site mechanical environment during the process of coal seam water infusion, which is conducive to the popularization and application of this technology, thus, providing the scientific basis for safe mining of the deep coal seam and ensuring the safe and efficient production of mines.

Improving the conductivity stability of flexible conductors has a positive impact on the working performance and service life of portable electronic devices. However, designing a conductor material with both conductivity-stable and flexible performance is still not an easy task because of the conductive network damage under large deformation or high humidity conditions. Here, we surmount this challenge by developing an interfacial assembly/encapsulation integration strategy for the construction of flexible, transparent, and conductivity-stable film. Under the synergistic effect of coordination complexation and hydrogen bonding, silver nanowires (AgNWs) are sandwiched encapsulated by regenerated cellulose film (as flexible substrate) and poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (PEDOT:PSS) nanosheets (as sealing coat). The resulting hybrid film has a robust interfacial structure and prominently stable properties. Even when subjected to harsh condition such as a high-moisture environment of 90% relative humidity and 65 °C temperature for up to 60 days, repeated bending for 500 times, peeling over 400 times or soaking in water for 30 days, the film still exhibits a high and stable conductivity, better than most previously reported values for AgNWs-based films. With this strategy as base, we demonstrate a flexible, transparent and biocompatible strain-to-electricity sensor with high working stability and performance.

Multi-object tracking is a crucial problem for autonomous vehicle. Most state-of-the-art approaches adopt the tracking-by-detection strategy, which is a two-step procedure consisting of the detection module and the tracking module. In this paper, we improve both steps. We improve the detection module by incorporating the temporal information, which is beneficial for detecting small objects. For the tracking module, we propose a novel compressed deep Convolutional Neural Network (CNN) feature based Correlation Filter tracker. By carefully integrating these two modules, the proposed multi-object tracking approach has the ability of re-identification (ReID) once the tracked object gets lost. Extensive experiments were performed on the KITTI and MOT2015 tracking benchmarks. Results indicate that our approach outperforms most state-of-the-art tracking approaches.

We investigate the structural vulnerability of complex networks under different edge-based attacks. The attacks are induced by removing a certain ratio of edges according to four kinds of weighting methods strategies introduced, which also can describe the characteristics of links. Two metrics (the relative size of the giant component in network, the average length of shortest path) are measured. The simulations demonstrate that, the edges with higher product of betweenness centrality of the two endpoints of one edge connected are more important for SF network, ER random network, autonomous system network(AS), Italy power grid and US airport networks, but the edges with higher edge betweenness centrality are more important for WS networks. Moreover, the edges with higher edge betweenness centrality for SF network and those with higher k-shell index for AS networks are also important. The findings highlight the identification of structural key links for protecting real-world networks.

Camptothecin (CPT) is a cytotoxic quinoline alkaloid that is used clinically as an anticancer drug. However, the clinical application of CPT is limited due to its low solubility as well as serious and unfathomable side-effects. In the present study, we created a novel 10-hydroxy CPT prodrug, ZBH-ZM‑06. Its cellular cytotoxic activity was analyzed in terms of cellular viability, acetylcholinesterase (AchE) inhibition, DNA relaxation, cellular cycling and apoptosis properties. Our results showed that the AchE inhibition rate of 10 µmol/l ZBH-ZM-06 was 12.5%, compared to 96.5% for carbonyl-oxycamptothecin (CPT-11). In a chemical stability assay, only 4.9% of ZBH-ZM-06 remained after 4 h at pH 7.4. In addition, 10 µmol/l ZBH-ZM-06 significantly inhibited the tumor cell viability of nine tumor cell lines, compared to CPT-11 and the CPT active ingredient, 7-ethyl-10-hydroxy-camptothecin (SN38) (p

A porous structure is the key factor to successful chromatography separation. Agarose gel as one of the most popular porous media has been extensively used in chromatography separation. As the cooling process in the agarose gelation procedure can directly influence the pore structure, ten kinds of 4% agarose media with different cooling rates from 0.132 to 16.7°C/min were synthesized, and the pore structure was determined accurately by using low-field NMR spectroscopy. The curves of pore structure and cooling rate can be divided into two stages with the boundary of 6°C/min. In stage I, the pore structure met a power equation with the decrease of the cooling rate, and in stage II, the process reached a plateau. Confirmatory experiments proved that, by adjusting the cooling rate, a precise control of the pore structure of agarose media can be realized, furthermore, cooling rate optimization was an effective way to control the pore size of agarose media and can further tailor the pore structure for more effective separation of different proteins.

Sol–gel transition properties play a key role in various applications of thermosensitive hydrogels, but conventional methods for studying the sol–gel transition have some limitations. For extensive characterization of the water–polymer interaction and microstructure change during sol–gel transition, we propose a rapid and nondestructive method based on monitoring water mobility through low field NMR (LF-NMR), and this was applied to chitosan/β-glycerophosphate (CS/GP) hydrogels. The spin–spin relaxation time (T2) that depicted water mobility was measured by LF-NMR within 90 s. The T2 component corresponding to water protons trapped in polymer networks (T21) was very sensitive to sol–gel transition. A remarkable decrease of T21 value indicated obvious variations of water mobility when CS/GP was heated, and a turning point was observed on the T21–time curve. The gel point associated with this turning point could be easily determined by fitting the T21–time curves to a bilinear regression model, and the results showed good accuracy and repeatability owing to the nondestructive nature of LF-NMR. Variations in water components and microstructure of CS/GP caused by water migration after solidification were also analyzed by monitoring dynamic changes of T2. This rapid, nondestructive method provides a powerful tool for studying the sol–gel transition of hydrogels.

A new method for studying swelling behavior of hydrogels was developed based on low field NMR (LF-NMR). This method is established on these facts: firstly, internal water (water trapped in hydrogel) and external water (water outside of hydrogel) correspond to different components of transverse relaxation time (T2); secondly, T2 component amplitude is proportional to relative water content; and finally, T2 value is closely related to mesh size of hydrogel network, the main effect being due to the overall concentration (degree of swelling). This method was successfully applied to swelling ratio determination of chitosan/glutaraldehyde (CS/GA) hydrogels in situ, and the results had better accuracy and repeatability compared with that of weighing method. Furthermore, swelling kinetics at different pH and microstructure of CS/GA hydrogels was well elucidated based on T2. It is clearly showed that LF-NMR provides a powerful tool for probing processes related to water transport and microstructure variation of hydrogels.

Epidemic spreading on physical contact network will naturally introduce the human awareness information diffusion on virtual contact network, and the awareness diffusion will in turn depress the epidemic spreading, thus forming the competing spreading processes of epidemic and awareness in a multiplex networks. In this paper, we study the competing dynamics of epidemic and awareness, both of which follow the SIR process, in a two-layer networks based on microscopic Markov chain approach and numerical simulations. We find that strong capacities of awareness diffusion and self-protection of individuals could lead to a much higher epidemic threshold and a smaller outbreak size. However, the self-awareness of individuals has no obvious effect on the epidemic threshold and outbreak size. In addition, the immunization of the physical contact network under the interplay between of epidemic and awareness spreading is also investigated. The targeted immunization is found performs much better than random immunization, and the awareness diffusion could reduce the immunization threshold for both type of random and targeted immunization significantly.

A flexible, transparent, and renewable mesoporous cellulose membrane (mCel-membrane) featuring uniform mesopores of ≈24.7 nm and high porosity of 71.78% is prepared via a facile and scalable solution-phase inversion process. KOH-saturated mCel-membrane as a polymer electrolyte demonstrates a high electrolyte retention of 451.2 wt%, a high ionic conductivity of 0.325 S cm−1, and excellent mechanical flexibility and robustness. A solid-state electric double layer capacitor (EDLC) using activated carbon as electrodes, the KOH-saturated mCel-membrane as a polymer electrolyte exhibits a high capacitance of 110 F g−1 at 1.0 A g−1, and long cycling life of 10 000 cycles with 84.7% capacitance retention. Moreover, a highly integrated planar-type micro-supercapacitor (MSC) can be facilely fabricated by directly depositing the electrode materials on the mCel-membrane-based polymer electrolyte without using complicated devices. The resulting MSC exhibits a high areal capacitance of 153.34 mF cm−2 and volumetric capacitance of 191.66 F cm−3 at 10 mV s−1, representing one of the highest values among all carbon-based MSC devices. These findings suggest that the developed renewable, flexible, mesoporous cellulose membrane holds great promise in the practical applications of flexible, solid-state, portable energy storage devices that are not limited to supercapacitors.

Cascading failures and epidemic dynamics, as two successful application realms of network science, are usually investigated separately. How do they affect each other is still one open, interesting problem. In this letter, we couple both processes and put them into the framework of interdependent networks, where each network only supports one dynamical process. Of particular interest, they spontaneously form a feedback loop: virus propagation triggers cascading failures of systems while cascading failures suppress virus propagation. Especially, there exists crucial threshold of virus transmissibility, above which the interdependent networks collapse completely. In addition, the interdependent networks will be more vulnerable if the network supporting virus propagation has denser connections; otherwise the interdependent systems are robust against the change of connections in other layer(s). This discovery differs from previous framework of cascading failure in interdependent networks, where better robustness usually needs denser connections. Finally, to protect interdependent networks we also propose the control measures based on the identification capability. The larger this capability, more robustness the interdependent networks will be.

The variety of isotopes in cosmic rays allows us to study different aspects of the processes that cosmic rays undergo between the time they are produced and the time of their arrival in the heliosphere. In this paper, we present measurements of the isotopic ratios [superscript 2]H/[superscript 4]He, [superscript 3]He/[superscript 4]He, [superscript 6]Li/[superscript 7]Li, [superscript 7]Be/([superscript 9]Be+[superscript 10]Be), and [superscript 10]B/[superscript 11]B in the range 0.2-1.4 GeV of kinetic energy per nucleon. The measurements are based on the data collected by the Alpha Magnetic Spectrometer, AMS-01, during the STS-91 flight in 1998 June., United States. Dept. of Energy, Massachusetts Institute of Technology

The Alpha Magnetic Spectrometer (AMS) was flown on the space shuttle Discovery during flight STS-91 (June 1998) in a 51.7 deg orbit at altitudes between 320 and 390 km. A search for antihelium nuclei in the rigidity range 1–140 GV was performed. No antihelium nuclei were detected at any rigidity. An upper limit on the flux ratio of antihelium to helium of < 1.1×10^−6 was obtained. The high energy proton, electron, positron, helium, antiproton and deuterium spectra were accurately measured. For each particle and nuclei two distinct spectra were observed: a higher energy spectrum and a substantial second spectrum. Positrons in the second spectrum were found to be much more abundant than electrons. Tracing particles from the second spectra shows that most of them travel for an extended period of time in the geomagnetic field, and that the positive particles (p and e+) and negative ones (e−) originate from two complementary geographic regions. The second helium spectrum flux over the energy range 0.1–1.2 GeV/nucleon was measured to be (6.3 ± 0.9) × 10^−3(m^2 s sr)^−1. Over 90 percent of the helium flux was determined to be 3He at the 90% confidence level

The lepton spectra in the kinetic energy ranges 0.2 to 40 GeV for e − and 0.2 to 3 GeV for e + were measured by the Alpha Magnetic Spectrometer (AMS) during space shuttle flight STS–91 at altitudes near 380 km. From the origin of the leptons two distinct spectra were observed: a higher energy spectrum and a substantial second spectrum with positrons much more abundant than electrons. Tracing leptons from the second spectra shows that most of these leptons travel for an extended period of time in the geomagnetic field and that the e + and e − originate from two complementary geographic regions.

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination - one of the most important preventive measures of modern times - is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research., 150 pages, 42 figures; published in Physics Reports