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Electrochemistry and X-ray absorption spectroscopy (XAS) of MnO2/reduced graphene oxide (RGO) were made to evaluate capacitive performance and electronic structure as a supercapacitor. MnO2 was deposited on the surface of RGO by a spontaneous redox reaction. At low current density (1 A g−1), the specific capacitance of MnO2/RGO (MRGO) greatly increased with increasing growth time from 281 to 462 F g−1. Electrochemical results show that the specific capacitance of RGO and MRGO arises from the combined contributions of electrochemical double-layer capacitance (EDLC) and pseudo-capacitance. To gain insights into the charge storage mechanism of RGO and MRGO, it was characterized using ex-situ soft XAS techniques at Mn L-edge and C, O K-edges in the charging/discharging process. The ex-situ soft XAS results provide evidence that the contribution of RGO to specific capacitance involves an oxygen-functionality-related contribution of pseudocapacitance and EDLC. In addition, the specific capacitance of MRGO reveals pseudocapacitive contributions from the redox processes that involve the Mn(III)/Mn(IV) redox reaction in MnO2. This work utilizes electrochemical and ex-situ XAS techniques to elucidate charge storage mechanism for supercapacitor applications.

Viscous and gravitational flow instabilities cause a displacement front to break up into finger-like fluids. The detection and evolutionary analysis of these fingering instabilities are critical in multiple scientific disciplines such as fluid mechanics and hydrogeology. However, previous detection methods of the viscous and gravitational fingers are based on density thresholding, which provides limited geometric information of the fingers. The geometric structures of fingers and their evolution are important yet little studied in the literature. In this work, we explore the geometric detection and evolution of the fingers in detail to elucidate the dynamics of the instability. We propose a ridge voxel detection method to guide the extraction of finger cores from three-dimensional (3D) scalar fields. After skeletonizing finger cores into skeletons, we design a spanning tree based approach to capture how fingers branch spatially from the finger skeletons. Finally, we devise a novel geometric-glyph augmented tracking graph to study how the fingers and their branches grow, merge, and split over time. Feedback from earth scientists demonstrates the usefulness of our approach to performing spatio-temporal geometric analyses of fingers., Published at IEEE Transactions on Visualization and Computer Graphics

A wireless power transfer system for multi-user charging is proposed to demonstrate a robust quasi-uniform power efficiency in a 3-D space. It employs a set of balanced magnetic coils excited with phase-shifted currents on the transmitting side to feature omnidirectionality. In this article, two transmitter structures, 2-coil and 3-coil systems are discussed and compared. These systems produce a quasi-uniform magnetic field magnitude regardless of the locations of the receiving coils provided that the coils point to the center of the transmitting system sphere at the same distance. A simulation platform is established for the proposed method. Experimental results at 2MHz substantiate a uniform spatial efficiency and bound the average error of analytical calculations within 10%. Furthermore, the proposed method with rotating fields based on phase-shifted currents shows that the 3-coil system yields a higher efficiency and a lower efficiency spread, 6.8% improvement on average efficiency and 88.7% reduction on efficiency variance, compared to the 2-coil system.

In this article, we present an ultrawideband and high-gain antenna based on a 3-D impedance-matching metamaterial (IMM) lens. First, we design a 3-D IMM lens which can convert spherical-like waves to plane ones and is nearly perfect impedance-matching with the free space. To realize the 3-D lens, we propose and design two kinds of ultrawideband sandwich-structured metamaterial unit cells that can cover the refraction index ranges from 1.25 to 1.4 and from 1.4 to 3.8, respectively. We also design a double-ridged pyramid horn antenna as an ultrawideband feed source. Measured results verify that the presented lens antenna can work in an ultrawide bandwidth from 5.2 to 15.13 GHz, and the relative bandwidth is 97.7%. Moreover, it achieves a high aperture efficiency of more than 66% from 5.2 to 14.3 GHz, with a relative bandwidth of 93.3%. A maximum aperture efficiency of 85.6% is obtained at 11.5 GHz. The proposed lens antenna has an average of 2 dB gain enhancement versus optimal horn with the same aperture area. And the length of the lens antenna reduces to 22% compared to a bare horn antenna with the same gain and aperture efficiency.

By employing a rational approach, we prepared a novel kind of luminescent europium-centered hybrid material named Eu(tta)3NCP-TiO2. The resulting material was characterized by FT-IR spectra, SEM, X-ray diffraction, thermogravimetric analysis, and photoluminescence spectra. The hybrid material features the combined advantages of the europium complex and the titania host, exhibiting not only good thermostability, but also long luminescence lifetime. Owing to the excellent luminescence of this material, the application in detecting organic small molecule solvents and metal ions was explored systematically. Significantly, Eu(tta)3NCP-TiO2 exhibits superior detection for nitrobenzene molecule and Cu2+ ion in DMF (N,N-dimethylformamide) medium. Furthermore, the limit of detection (LOD) of Eu(tta)3NCP-TiO2 for nitrobenzene and Cu2+ ion can be counted as 5.593 × 10−5 and 9.566 × 10−5 mol/L, respectively. The results demonstrate that Eu(tta)3NCP-TiO2 can serve as an efficient fluorescence probe for the detection of sensing of nitrobenzene and Cu2+ ion.

In recent years, polymer nanocomposites based on 2D materials have aroused widespread research interest. MoS2 exhibits many excellent properties due to its unique structure, such as mechanical properties, thermal stability, and fire resistance properties. It has great potential as a polymer reinforcement material and functional nanofiller. However, it is well known that the exfoliated MoS2 layers have a tendency to aggregate during mixing with polymers, which results in the polymer performance cannot be effectively improved. In this work, clay was introduced into molybdenum disulfide (MoS2) to improve the dispersion of exfoliated MoS2. After co-deposition, two different layered fillers co-stacked to form a novel clay/MoS2 composite. The presence of clay in the MoS2 slurry prevented aggregation of the individual clay and MoS2 layers. The resulting clay/MoS2 composite was further used as fillers to form epoxy/clay/MoS2 nanocomposites. The effects of clay, MoS2 and clay/MoS2 mixture on the properties of epoxy nanocomposites were investigated and compared. It was found that combining clay and MoS2 allows the enhancement of tensile strength and tensile modulus. Additionally, due to the well co-dispersion of clay and MoS2, the thermal stability and mechanical properties of epoxy/clay/MoS2 nanocomposites are higher than those of epoxy/clay and epoxy/MoS2 nanocomposites. Therefore, this work provides a facile way to produce high-performance epoxy nanocomposites.

The model based on the velocity and acceleration of temperature variation (MBOTV) was established for spindle thermal growth error. However, the thermophysical parameters of MBOTV, such as the thermal expansion coefficient related to the velocity of temperature variation, Hv, the thermal expansion coefficient related to the acceleration of temperature variation, Ha, and the thermal inertia coefficient of the spindle, τ, may change during actual working. In order to analyze the influence of thermophysical parameters on the prediction accuracy of MBOTV, it is necessary to calculate the reliability of MBOTV. Actually, it is difficult to obtain the function of MBOTV. Although the improved response surface method can be used to calculate the reliability of the model with implicit function, the accuracy is not enough obviously when the implicit function is replaced by linear polynomials. Therefore, an improved response surface method based on quadratic polynomial approximation was proposed in this paper to calculate the reliability of MBOTV. In this method, the linear polynomial was replaced by a quadratic polynomial without cross product terms to replace the implicit function, which can improve the accuracy of reliability calculation. The predicted residual errors of MBOTV with single-parameter fluctuation and multi-parameter fluctuation were given in this paper. Studies reveal that MBOTV has strong robustness when the thermophysical parameters change.

Magnetic skyrmions are versatile topological excitations that can be used as nonvolatile information carriers. The confinement of skyrmions in channels is fundamental for any application based on the accumulation and transport of skyrmions. Here, we report a method that allows effective position control of skyrmions in designed channels by engineered energy barriers and wells, which is realized in a magnetic multilayer film by harnessing the boundaries of patterns with modified magnetic properties. We experimentally and computationally demonstrate that skyrmions can be attracted or repelled by the boundaries of areas with modified perpendicular magnetic anisotropy and Dzyaloshinskii-Moriya interaction. By fabricating square and stripe patterns with modified magnetic properties, we show the possibility of building reliable channels for confinement, accumulation, and transport of skyrmions, which effectively protect skyrmions from being destroyed at the device edges. Our results are useful for the design of spintronic applications using either static or dynamic skyrmions.

In this study, hydroxyl-modified/Na-intercalated g-C3N4 was used as an effective material for the removal of gaseous formaldehyde (HCHO) through adsorption and photocatalytic oxidation. In a simple reflux reaction using different NaOH concentrations, the surface-grafted hydroxyl (‒OH) groups of g-C3N4 formed internal hydrogen bonds with gaseous HCHO, while the Na atoms intercalated into the layered structure of g-C3N4 to create an electron transfer path (layer‒Na‒layer) to improve charge separation for photocatalytic oxidation. The hydroxylation as well as Na intercalation of g-C3N4 significantly increased its HCHO removal efficiency compared with that of bare g-C3N4. The HCHO removal capacity of the modified g-C3N4 reached approximately 0.12 ppm g−1 min−1, which was twice that of bare g-C3N4 (

In this letter, a compact dual-band dual-mode substrate integrated waveguide (SIW) balanced band-pass filter (BPF) with intrinsic common-mode (CM) suppression is designed by using a single perturbed SIW circular cavity. The vias and slot perturbations are introduced to tune the resonant modes TE010, TE110 (a pair of degenerate modes), and TE210 of a circular cavity. Thus, they can be used to form two differential-mode (DM) passbands with independently adjustable center frequencies and bandwidths. The SIW can be seen as a natural balun with intrinsic CM suppression. Furthermore, the planar differential excitation structure is designed to excite the perturbed SIW cavity. The CM noise cannot excite SIW cavity due to the characteristics of hybrid electromagnetic boundary. Finally, a compact dual-band dual-mode SIW balanced BPF at 8 and 10 GHz is designed and fabricated, and the measured results show good agreement with the simulated ones.

AIM To examine the role of palmitic acid in lipopolysaccharide (LPS)-stimulated chemotaxis of macrophages and the potential contribution of saturated fatty acid in signalling during the pathogenesis of apical periodontitis. METHODOLOGY J774, a mouse macrophage cell line, was used in the experiments. After treatment with LPS, proteolytic maturation of sterol regulatory element-binding protein-1c (SREBP-1c) and expression of fatty acid synthase (FASN) were examined by Western analysis. Levels of palmitic acid were measured by reverse phase-high performance liquid chromatography-mass spectrometry. Knockdown of SREBP-1c and FASN was accomplished by small interfering RNA technology. Secretion of CC-chemokine ligand 2 (CCL2) and cellular chemotaxis were assessed by enzyme-linked immunosorbent assay and transwell migration assay, respectively. Sulfo-N-succinimidyl oleate (SSO) treatment was used to inhibit fatty acid signalling in vitro and also in a rat model of apical periodontitis. All data were first subjected to Levene's test. In vitro data were then analysed using ANOVA followed by Tukey's multiple comparison test. Data from animal experiments were analysed by independent t-tests. The significant level was set at 0.05. RESULTS LPS stimulated proteolytic maturation of SREBP-1c and FASN expression in macrophages and significantly enhanced palmitic acid synthesis (P

Satellites detect the distribution of meteorological data worldwide. However, due to the orbital constraints, the satellite can only reach the same area again after one orbiting cycle. The interval between two detections in the same area is long, and the variation of meteorological data between the two detections is unknown. Moreover, meteorological satellite data are only located near the orbit in one cycle, while the global distribution of meteorological data is unknown. Our method allows to train a regression model with only few meteorological satellite data by taking advantage of the recent advances in deep learning. In detail, we train a model-agnostic meta-learning (MAML) model with data from ground stations instead of meteorological satellites and get the initial network parameters. Based on the initial network parameters trained by MAML, we train the regression models again for different areas. We sample the regression curves of all areas by time and get a time series of global meteorological data distribution. Through case studies conducted together with domain experts, we validate the effectiveness of our method.

Interventional therapies such as drug-eluting stents (DES) and drug-coated balloons (DCB) have significantly improved the clinical outcomes of patients with coronary occlusions in recent years. Despite this marked improvement, ischemic cardiovascular disease remains the most common cause of death worldwide. To address this, research efforts are focused on improving the safety and efficacy of the next generation of these devices. However, current experimental methods are unable to account for the influence of atherosclerotic lesions on drug uptake and retention. Therefore, in this study, we used an integrated approach utilizing both in vitro and in silico methods to assess the performance of DCB therapy. This approach was validated against existing in vivo results before being used to numerically estimate the effect of the atheroma. A bolus release of sirolimus was observed with our coating matrix. This, coupled with the rapid saturation of specific and non-specific binding sites observed in our study, indicated that increasing the therapeutic dose coated onto the balloons might not necessarily result in greater uptake and/or retention. Additionally, our findings alluded to an optimal exposure time, dependent on the coating matrix, for the DCBs to be expanded against the vessel. Moreover, our findings suggest that a biphasic drug release profile might be beneficial for establishing and maintaining the saturation of bindings sites within severely occluded vessels. Ultimately, we have demonstrated that computational methods may be capable of assessing the efficacy of DCB therapy as well as predict the influence of atherosclerotic lesions on said efficacy.

In this paper, the effects of dimensionless gas area fraction (δ) and normalized groove-rib periodic spacing (Λ) on developing flow for Newtonian fluid past circular tube having alternating superhydrophobic transverse grooves were numerically investigated. Fluid flow at Re ranging from 0.001 to 10 over various superhydrophobic microstructures scales (0.05 < Λ < 1, 0

The research on the diffusion of water in coatings has been a hot topic in many fields, such as chemistry, coating structure and hydrogen bonding. In this paper, DGEBA epoxy coating was used as a sample to explore the diffusion process of 3 wt.% NaCl solution at different temperatures. Two-stage diffusion was established by immersion experiment and MD simulation. The synergistic effect of temperature and water on DGEBA properties was revealed by FTIR method and adsorption-desorption cyclic testing. The results showed that as the temperature increased, the saturation water content in coatings increased. At different temperatures, the diffusion process of water presented two-phase characteristics, and the influence of temperature on the diffusion process was mainly manifested in the cross-linking density at higher locations. Based on the variation law of swelling coefficient in per unit time (24 h), the conditions for water-coating interaction and formation of water clusters in this paper were proposed. The synergistic effect of water and temperature on DGEBA properties was reflected in two aspects: at lower temperature (20 °C), water would only change the physical structure of the coatings, while the water broke the DGEBA chains at higher temperature (60 °C).

We report an ultrawideband array antenna charactered by coding method. The Vivaldi antenna is chosen as radiating element and the array antenna is fed by a 1 to 8 ultrawideband power divider network. The desired coding states are realized by position reverse of the feeding ports and the power divider network is matched by loading three optimized stepped circuited stubs. A prototype was fabricated and measured and the results show the working bandwidth is approximately 85.7% (from 4 to 10 GHz). More importantly, accurate dual-beam frequency scanning feature is demonstrated and the scanning angle ranges from 21° to 54° (–21° to –54°) when the frequency changes from 4 to 9 GHz. Also, the 3 dB gain bandwidth is ultrawide, from 4 to 10 GHz. The measured results agree well with the simulated ones. Such an ultrawideband array antenna is expected to have potential applications in near future.

The analysis and visualization of data created from simulations on modern supercomputers is a daunting challenge because the incredible compute power of modern supercomputers allow scientists to generate datasets with very high spatial and temporal resolutions. The limited bandwidth and capacity of networking and storage devices connecting supercomputers to analysis machines become the major bottleneck for data analysis such that simply moving the whole dataset from the supercomputer to a data analysis machine is infeasible. A common approach to visualize high temporal resolution simulation datasets under constrained I/O is to reduce the sampling rate in the temporal domain while preserving the original spatial resolution at the time steps. Data interpolation between the sampled time steps alone may not be a viable option since it may suffer from large errors, especially when using a lower sampling rate. We present a novel ray-based representation storing ray based histograms and depth information that recovers the evolution of volume data between sampled time steps. Our view-dependent proxy allows for a good trade off between compactly representing the time-varying data and leveraging temporal coherence within the data by utilizing interpolation between time steps, ray histograms, depth information, and codebooks. Our approach is able to provide fast rendering in the context of transfer function exploration to support visualization of feature evolution in time-varying data.

Given a research field, there are usually multiple conferences with different sizes and characteristics, and not all researchers have equal participations in all those conferences. For a conference organizer, knowing the level of involvements by the researchers in the field will allow them to put effort into broadening the participation and enrich the research scope. For researchers, knowing the different strengths and focuses of those conferences also allows them to enhance the effectiveness of their effort and the visibility of their works. In recent years, analyzing scientific literature by visualization has become a popular topic, but most of them focus only on the overall characteristics of publications such as authors, citations, and topics. This study focuses on the analysis and comparisons of academic conferences. In this paper, we present the Conference Orange, a new visual metaphor for analyzing and comparing the characteristics of multiple conferences. First, we propose an assessment framework to describe the maturity of the conferences. Second, we map the metrics in the framework to different diagrams for visual analysis and comparison. Third, we design and implement ConfVisExplorer, a multi-view interactive visual system that combines these diagrams, which helps users to explore the structure of the conferences and the attributes in context, such as authors and institutions. Finally, we conduct case studies to demonstrate the effectiveness of the system.

Visual examination of the workplace and in-time reminder to the failure of wearing a safety helmet is of particular importance to avoid injuries of workers at the construction site. Video monitoring systems provide a large amount of unstructured image data on-site for this purpose, however, requiring a computer vision-based automatic solution for real-time detection. Although a growing body of literature has developed many deep learning-based models to detect helmet for the traffic surveillance aspect, an appropriate solution for the industry application is less discussed in view of the complex scene on the construction site. In this regard, we develop a deep learning-based method for the real-time detection of a safety helmet at the construction site. The presented method uses the SSD-MobileNet algorithm that is based on convolutional neural networks. A dataset containing 3261 images of safety helmets collected from two sources, i.e., manual capture from the video monitoring system at the workplace and open images obtained using web crawler technology, is established and released to the public. The image set is divided into a training set, validation set, and test set, with a sampling ratio of nearly 8 : 1 : 1. The experiment results demonstrate that the presented deep learning-based model using the SSD-MobileNet algorithm is capable of detecting the unsafe operation of failure of wearing a helmet at the construction site, with satisfactory accuracy and efficiency.

This paper proposes an approach to strength adjustment and assessment for Monte-Carlo tree search based game-playing programs. We modify an existing softmax policy with a strength index to choose moves. The most important modification is a mechanism which filters low-quality moves by excluding those that have a lower simulation count than a pre-defined threshold ratio of the maximum simulation count. Through theoretical analysis, we show that the adjusted policy is guaranteed to choose moves exceeding a lower bound in strength by using a threshold ratio. Experimental results show that the strength index is highly correlated to the empirical strength. With an index value between ?2, we can cover a strength range of about 800 Elo ratings. The strength adjustment and assessment methods were also tested in real-world scenarios with human players, ranging from professionals (strongest) to kyu rank amateurs (weakest). For amateur levels, we tested our mechanism on two popular Go online platforms - Fox Weiqi and Tygem. The result shows that our method can adjust program strength to different ranks stably. In terms of strength assessment, we proposed a new dynamic strength adjustment method, then used it to evaluate human professionals, predicting reliably their playing strengths within 15 games. Lastly, we collected survey responses asking players about strength perception, entertainment, and general comments for different aspects of analysis. To our best knowledge, this result is state-ofthe- art in terms of the range of strengths in Elo rating while maintaining a controllable relationship between the strength and a strength index.

The capture of SO2 from industrial tail gas is of great significance to control air pollution and reserve sulfur resource. In this work, we proposed the use of deep eutectic solvents (DESs) formed ...

Traditionally, Pitot rake test models have been used to take time- and spatially resolved impact pressure measurements in impulse facilities for flow characterisation. When expansion tubes are used for the study of hypervelocity planetary entry phenomena, generally the post-shock flow in the test section strongly radiates. This paper outlines a simple method which uses a high-speed camera in addition to a conventional Pitot rake to improve estimates of experimental test time and core flow size by imaging post-shock radiative emission over the probes in the Pitot rake. This method can also be used with specific narrow bandpass optical filters to examine when the emission from key species remains constant in the test flow in both time and space. The selection and suitability of various optical filters for a high-enthalpy Earth entry scenario are examined in this paper, as well as the effect of radiation from flow contamination. Experimentally, it was found that the radiative emission generally rises quite abruptly at the end of the test time and sometimes in situations where the pressure remains constant. It was also seen that different optical filters focusing on different spectral features can show their abrupt rise at different times, giving differing values for the end of the test time. Unfiltered measurements were found to be compromised by contaminant radiative emission which was seen to be randomly distributed in time and space towards the end of the test time.

Uncontrolled bleeding and infection can cause significant increases in mortalities. Hydrogel sealants have attracted extensive attention for their ability to control bleeding. However, because interfacial water is a formidable barrier to strong surface bonding, a challenge remains in finding a product that offers robust tissue adhesion combined with anti-infection properties. Inspired by the strong adhesive mechanism of biofilm and mussels, we report a novel dual bionic adhesive hydrogel (DBAH) based on chitosan grafted with methacrylate (CS-MA), dopamine (DA), and N-hydroxymethyl acrylamide (NMA) via a facile radical polymerization process. CS-MA and DA were simultaneously included in the adhesive polymer for imitating the two key adhesive components: polysaccharide intercellular adhesin (PIA) of staphylococci biofilm and 3,4-dihydroxy-l-phenylalanine (Dopa) of mussel foot protein, respectively. DBAH presented strong adhesion at 34 kPa even upon three cycles of full immersion in water and was able to withstand up to 168 mm Hg blood pressure, which is significantly higher than the 60–160 mm Hg measured in most clinical settings. Most importantly, these hydrogels presented outstanding hemostatic capability under wet and dynamic in vivo movements while displaying excellent antibacterial properties and biocompatibility. Therefore, DBAH represents a promising class of biomaterials for high-efficiency hemostasis and wound healing., Graphical abstract Image 1, Highlights • We put forward a novel method inspired form staphylococcal biofilm for fabricating hydrogel hemostasis material. To the best of our knowledge, this is the first report of designing staphylococcal biofilm inspired adhesive hydrogel for in vivo sealing hemostasis. • Unlike the traditional acrylamide (AM) based hydrogels, the as-prepared DBAH were purified before further measurements and application by repetitive cycles of deionized water and ethyl alcohol to prevent the potential cytotoxicity of unreacted NMA monomers. • The as-prepared DBAH can intimately integrate with biological tissue and exhibit superior capacity of in vivo hemostatic by sealing the bleeding wound even in wet and dynamic rabbit's heart.

We propose a deep learning approach to collectively compare two or multiple ensembles, each of which is a collection of simulation outputs. The purpose of collective comparison is to help scientists understand differences between simulation models by comparing their ensemble simulation outputs. However, the collective comparison is non-trivial because the spatiotemporal distributions of ensemble simulation outputs reside in a very high dimensional space. To this end, we choose to train a deep discriminative neural network to measure the dissimilarity between two given ensembles, and to identify when and where the two ensembles are different. We also design and develop a visualization system to help users understand the collective comparison results based on the discriminative network. We demonstrate the effectiveness of our approach with two real-world applications, including the ensemble comparison of the community atmosphere model (CAM) and the rapid radiative transfer model for general circulation models (RRTMG) for climate research, and the comparison of computational fluid dynamics (CFD) ensembles with different spatial resolutions.

Generating compact and effective numerical representations of data is a fundamental step for many machine learning tasks. Traditionally, handcrafted features are used but as deep learning starts to show its potential, using deep learning models to extract compact representations becomes a new trend. Among them, adopting vectors from the model’s latent space is the most popular. There are several studies focused on visual analysis of latent space in NLP and computer vision. However, relatively little work has been done for music information retrieval (MIR) especially incorporating visualization. To bridge this gap, we propose a visual analysis system utilizing Autoencoders to facilitate analysis and exploration of traditional Chinese music. Due to the lack of proper traditional Chinese music data, we construct a labeled dataset from a collection of pre-recorded audios and then convert them into spectrograms. Our system takes music features learned from two deep learning models (a fully-connected Autoencoder and a Long Short-Term Memory (LSTM) Autoencoder) as input. Through interactive selection, similarity calculation, clustering and listening, we show that the latent representations of the encoded data allow our system to identify essential music elements, which lay the foundation for further analysis and retrieval of Chinese music in the future.

Taking active component Pd-Ag alloy and catalyst carrier Al2O3 as the research object, the catalytic mechanism of acetylene hydrogenation reaction was explored by quantum mechanics theory and Monte...

An mnk-game is a kind of k-in-a-row game played on an m × n board, where two players alternatively mark empty squares with their own colors and the first player who gets k-consecutive marks (horizontally, vertically, or diagonally) wins. In this paper, we present an AND/OR search tree algorithm specifically for proving mnk-games. We first propose three novel methods to reduce the branching factor of AND/OR search trees. We also propose a new method to find pairing strategies, which further accelerate the proof of mnk-games. The combined methods drastically speed up the proof for the 7,7,5-game, which is solved in 2.5 seconds. Moreover, this paper is the first to solve the 8,8,5-game, which is proven as a draw within 17.4 hours.

In numerical simulations of the blast furnace, the discrete element method (DEM) is a quite novel method. The accuracy of physical parameters of the method is a key issue for the reliability of the results. In this study, we first measured the parameters describing the interaction between pellets, sinter and coke, as well as between these three materials and a steel plate. The experimentally determined parameters include the Young's modulus, Shear modulus and Poisson's ratio, particle density, coefficient of restitution, as well as coefficients of static and rolling friction. The coefficients of rolling friction of non-spherical particles were obtained by comparing the repose angle of bulk materials in experiment and simulation. After these calibration steps, the DEM parameters were validated by a comparison of free surfaces of the conical piles formed in simulations and experiments. The experimental and calculated results show good agreement, which demonstrates that the measured DEM parameters are of sufficient accuracy to be used in simulation of burden distribution and descent in the blast furnace.

We propose a low-profile and high-gain pattern-reconfigurable planar array antenna (PRPAA) based on digital coding characterization. The radiating elements in the PRPAA are designed ingeniously to realize digital “0” and “1” elements. By dynamically encoding the digital radiating elements with different coding patterns, various radiation beams can be achieved and switched in real time based on one antenna aperture. More importantly, for different coding patterns, the number and direction of the radiation beams of the digital PRPAA can be well predicted. We fabricate such a PRPAA and validate its digital-controllable radiation patterns experimentally. This communication provides many opportunities to realize low-cost, lightweight, high-gain, and multi-unit PRPAA, which offers promising applications in massive multi-in multi-out (MIMO) antenna array system, smart antenna, radar system, and dynamic microwave imaging.

饶河杂岩作为那丹哈达增生杂岩的主体，是古太平洋板块西向俯冲的直接证据。饶河增生杂岩组成与增生过程的研究对限定古太平洋板块向欧亚大陆的俯冲与增生过程具有重要的指示意义。本文在野外地质调查和饶河大岱地区大比例尺填图基础上，明确了饶河杂岩主要由枕状玄武岩、辉长岩以及大洋板块沉积地层（OPS）组成，这些岩石均呈构造透镜体状分布在海沟沉积物中，并被中生代花岗质岩脉所穿切，因此为限定饶河增生杂岩的组成、增生和就位时代提供了关键制约。地球化学数据表明玄武岩具有洋岛玄武岩（OIB）的地球化学属性。LA-ICPMS锆石测试结果表明该地区玄武岩和辉长岩的形成时代分别为166±2Ma和214±5Ma，限定了饶河杂岩中镁铁质-超镁铁质岩石的形成时代为晚三叠世至中侏罗世。结合该区粉砂质泥岩和砂岩的沉积时代下限分别为167±3Ma和133±4Ma，表明饶河杂岩的增生时代为167~133Ma，此外样品的碎屑年龄信息表明基质的物源为邻近的佳木斯地块和中亚造山带东段，其中前寒武的碎屑年龄在中国东北的多个陆块均有出现，可能源于早前存在的前寒武纪基底。本文测得侵入饶河杂岩的2个二长花岗岩形成年龄分别为126±1Ma和105±2Ma，表明饶河杂岩中的花岗岩脉主要形成于两个阶段，其中较老的花岗岩侵入体进一步限定了饶河杂岩的最终就位时代为133Ma至126Ma，表明古太平洋板块在中侏罗世至早白垩世存在西向俯冲-增生作用，为古太平洋板块的构造演化提供了重要的制约。

Porosity is an important parameter to reflect the internal structure of the heap, and it is related to the permeability of a packed bed. Thus, understanding and further adjusting the porosity distribution will help to control the gas-solid reaction in a chemical reactor. In this work, sandpiles formed by ternary size particles with variable mass ratio were researched by discrete element method. The porosity increases with the increase of the mass fraction of small particles. Different dump methods (Drop Test and Chute Test) both cause four distinct porosity regions (Zone A, B, C and D), but the locations are not the same. Changing the drop height in Drop Test and chute properties in Chute Test both have influence on the porosity zone, especially for the upper porosity. Furthermore, size segregation will lead to uneven porosity distribution, but there is no evident relationship between porosity and size segregation in sandpile.

Modular multilevel converter (MMC) shows prominent advantages on supplying passive networks, which are usually classified by power supply shortage and relatively weak structure. In order to reduce the unnecessary outage time and improve the system stability, an adaptive ac auto-reclosing scheme for passive networks is proposed based on characteristic signal injection. Additional MMC controller is designed to inject the characteristic signals into the healthy line, which in turn induces corresponding signals to the faulty line. Then fault properties are assessed by the travelling wave based fault location method. Finally, an adaptive auto-reclosing scheme is proposed to shorten the reclosing time and avoid the second fault shock to a large extent. The proposed scheme could be applied for both single-circuit and multi-circuit transmission lines. The simulation results obtained from PSCAD/EMTDC verify the effectiveness of the proposed method.

This study investigates the non-isothermal reduction of iron ore fines with two different carbon-bearing materials using the thermogravimetric technique. The iron ore fines/carbon composites were heated from room temperature up to 1100 °C with different heating rates (5, 10, 15, and 20 °C/min) under an argon atmosphere. The effect of heating rates and carbon sources on the reduction rate was intensively investigated. Reflected light and scanning electron microscopes were used to examine the morphological structure of the reduced composite. The results showed that the heating rates affected the reduction extent and the reduction rate. Under the same heating rate, the rates of reduction were relatively higher by using charcoal than coal. The reduction behavior of iron ore-coal was proceeded step wisely as follows: Fe2O3 → Fe3O4 → FeO → Fe. The reduction of iron ore/charcoal was proceeded from Fe2O3 to FeO and finally from FeO to metallic iron. The reduction kinetics was deduced by applying two different methods (model-free and model-fitting). The calculated activation energies of Fe2O3/charcoal and of Fe2O3/coal are 40.50–190.12 kJ/mol and 55.02–220.12 kJ/mol, respectively. These indicated that the reduction is controlled by gas diffusion at the initial stages and by nucleation reaction at the final stages.

Nanocomposites of the binary transition metal sulfide Zn-Co-S/graphene (Zn-Co-S@G) were synthesized through a one-step hydrothermal method. They may be useful in the construction of an electrochemical immunosensor for carbohydrate antigen 19-9 (CA19-9) detection. Zn-Co-S dot-like nanoparticles uniformly covered the surface of graphene to form an interconnected conductive network, ensuring strong interaction between transition metal sulfide and graphene, which can expose numerous electroactive sites leading to the improvement of the amplified electrochemical signal toward a direct reduction of H2O2. Thus, the construction of an electrochemical immunosensor using Zn-Co-S@G nanocomposites showed outstanding sensing properties for detecting CA19-9. The constructed electrochemical immunosensor exhibited a good linear relationship in the range of 6.3 U·mL−1–300 U·mL−1, with the limit of detection at 0.82 U·mL−1, which makes it a promising candidate for an electrochemical immunosensor.

The evolution rule of temperature-induced deflection in main girders is an important index to evaluate the service performance of long-span cable-stayed bridges, which directly reflects th...

The effects of Au cores in Ag shells in enhancing surface-enhanced Raman scattering (SERS) were evaluated with samples of various Au/Ag ratios. High-density Ag shell/Au core dendritic nanoforests (Au@Ag-DNFs) on silicon (Au@Ag-DNFs/Si) were synthesized using the fluoride-assisted Galvanic replacement reaction method. The synthesized Au@Ag-DNFs/Si samples were characterized using scanning electron microscopy, energy-dispersive X-ray spectroscopy, reflection spectroscopy, X-ray diffraction, and Raman spectroscopy. The ultraviolet-visible extinction spectrum exhibited increased extinction induced by the addition of Ag when creating the metal DNFs layer. The pure Ag DNFs exhibited high optical extinction of visible light, but low SERS response compared with Au@Ag DNFs. The Au core (with high refractive index real part) in Au@Ag DNFs maintained a long-leaf structure that focused the illumination light, resulting in the apparent SERS enhancement of the Ag coverage.

Metal–organic frameworks (MOFs) have recently garnered consideration as an attractive solid substrate because the highly tunable MOF framework can not only serve as an inert host but also enhance the selectivity, stability, and/or activity of the enzymes. Herein, we demonstrate the advantages of using a mechanochemical strategy to encapsulate enzymes into robust MOFs. A range of enzymes, namely β-glucosidase, invertase, β-galactosidase, and catalase, are encapsulated in ZIF-8, UiO-66-NH2, or Zn-MOF-74 via a ball milling process. The solid-state mechanochemical strategy is rapid and minimizes the use of organic solvents and strong acids during synthesis, allowing the encapsulation of enzymes into three prototypical robust MOFs while maintaining enzymatic biological activity. The activity of encapsulated enzyme is demonstrated and shows increased resistance to proteases, even under acidic conditions. This work represents a step toward the creation of a suite of biomolecule-in-MOF composites for application in a variety of industrial processes., Metal–organic frameworks (MOFs) are attractive for encapsulating enzymes for industrial purposes because they can increase selectivity, stability, and/or activity of the enzymes. Here, the authors developed an economical solid-state mechanochemical method to encapsulate enzymes during MOF synthesis.

The monitoring data makes it feasible to quickly evaluate the cracking of the prestressed concrete box-girder bridge. The live-load strain can accurately quantify the load effect and cracking of bridges due to its explicit datum point of signal. Based on the live-load strain data from bridge monitoring system, this study develops a comprehensive data-driven method of state evaluation and cracking early warning for the prestressed concrete box-girder bridge. The feature of vehicle-induced strain is extracted using the deep learning and classification of long short-term memory network. The vehicle-induced strain features are clustered via Gaussian mixture model, and the cracking early warning of the bridge is conducted according to the reliability of heavy vehicle clustering data. This method can be used as an indicator for the bridge inspection, truck-weight-limit and reinforcement work. The results demonstrate that (1) using the long short-term memory network, a deep learning model can be trained to intelligently classify the non-stationary and stationary sections of vehicle-induced strains, of which the test accuracy of classification surpasses 99%, and (2) according to the Gaussian mixture model probability distribution of data, the vehicle-induced strain features can be clustered by the corresponding Gaussian mixture model crest, which is the premise for reflecting relational mapping between vehicle loading and strain response.

Semidry flue gas desulfurization ash (FGDA) is a kind of solid waste, which is mainly composed of CaSO3 and produced by coal-fired power plants. Thermal decomposition has been considered as a promi...

We have developed a one-pot synthesis of bio-carbon nanowires from the natural product sodium alginate at low temperature, without using any catalyst, for anticoagulation applications. Sodium alginate is carbonized and sulfated/sulfonated in situ by solid state heating of a mixture of sodium alginate and ammonium sulfite. By regulating the heating temperature and the ratio of ammonium sulfite to sodium alginate, we modulated the degree of sulfation/sulfonation and carbonization, as well as the morphology of the carbon nanomaterials. The core-shell sulfated/sulfonated bio-carbon nanowires (CNWsAlg@SOx) made by the reaction of a mixture of ammonium sulfite and sodium alginate with a mass ratio of 5 (ammonium sulfite to sodium alginate) at 165 °C for 3 h, exhibit strong inhibition of thrombin activity due to their ultrahigh binding affinity towards it (dissociation constant (Kd) = 8.7 × 10−11 M). The possible formation mechanism of the carbon nanowires has been proposed. The thrombin-clotting time delay caused by CNWsAlg@SOx is ∼ 170 times longer than that caused by sodium alginate. Hemolysis and cytotoxicity assays demonstrated the high biocompatibility of CNWsAlg@SOx. Furthermore, the thromboelastography of whole-blood coagulation and rat-tail bleeding assays further reveal that CNWsAlg@SOx have a much stronger anticoagulation activity than sodium alginate and naturally sulfated polysaccharides (e.g., fucoidan). Our results suggest that the low-temperature prepared, cost-effective, and highly biocompatible CNWsAlg@SOx show great potential as an efficient anticoagulant for the prevention and treatment of diseases associated with thrombosis.

We present an efficient and scalable solution to estimate uncertain transport behaviors-stochastic flow maps (SFMs)-for visualizing and analyzing uncertain unsteady flows. Computing flow maps from uncertain flow fields is extremely expensive because it requires many Monte Carlo runs to trace densely seeded particles in the flow. We reduce the computational cost by decoupling the time dependencies in SFMs so that we can process shorter sub time intervals independently and then compose them together for longer time periods. Adaptive refinement is also used to reduce the number of runs for each location. We parallelize over tasks-packets of particles in our design-to achieve high efficiency in MPI/thread hybrid programming. Such a task model also enables CPU/GPU coprocessing. We show the scalability on two supercomputers, Mira (up to 256K Blue Gene/Q cores) and Titan (up to 128K Opteron cores and 8K GPUs), that can trace billions of particles in seconds.

Over the last decade, ensemble visualization has witnessed a significant development due to the wide availability of ensemble data, and the increasing visualization needs from a variety of disciplines. From the data analysis point of view, it can be observed that many ensemble visualization works focus on the same facet of ensemble data, use similar data aggregation or uncertainty modeling methods. However, the lack of reflections on those essential commonalities and a systematic overview of those works prevents visualization researchers from effectively identifying new or unsolved problems and planning for further developments. In this paper, we take a holistic perspective and provide a survey of ensemble visualization. Specifically, we study ensemble visualization works in the recent decade, and categorize them from two perspectives: (1) their proposed visualization techniques; and (2) their involved analytic tasks. For the first perspective, we focus on elaborating how conventional visualization techniques (e.g., surface, volume visualization techniques) have been adapted to ensemble data; for the second perspective, we emphasize how analytic tasks (e.g., comparison, clustering) have been performed differently for ensemble data. From the study of ensemble visualization literature, we have also identified several research trends, as well as some future research opportunities.

For an unfamiliar field, researchers who are looking for interdisciplinary collaboration or students who are going to start their research career often need to look for top research affiliations and domain experts according to the publication of top conferences or journals in this field. Further comparative analysis of affiliations or experts with similar achievements is also needed in order to find suitable collaborators or supervisors. In this work, we provide comprehensive visual analysis of research affiliations and domain experts based on papers accepted by the IEEE VIS from 1990 to 2018. First, we extract multi-word keywords from title and abstract automatically and then extract topics using LDA model based on these keywords. Second, we extract relationship between authors and affiliations based on co-author analysis. Third, we design and implement VISPubComPAS, a requirement-driven analysis system to (1) help users discover top affiliations and experts of required keywords; (2) visualize the relationships and statistics of these affiliations and experts; (3) compare two selected affiliations or experts of interest in detail by visualization. Finally, we conduct use cases and user reviews to demonstrate the effectiveness of VISPubComPAS.

This paper considers an integrated problem of soaking pit heating and hot rolling scheduling that arises in steel plants. Both soaking pit and hot rolling mill are batching machines with capacity considerations. The processing time of a batch in the soaking pit is equal to the longest processing time of the jobs in the batch, whereas the processing time of a batch on the hot rolling mill is equal to the sum of the processing times of the jobs in the batch. Setup time is required whenever a batch is formed on the hot rolling mill. For this problem, we must determine the formation of batches and the sequences of these batches in the soaking pit and on the hot rolling mill such that makespan is minimized. First, we formulate the problem as a mixed integer linear programming model (MILPM) and analyze its computational complexity. Second, we provide a fast heuristic algorithm with worst-case bound to generate near-optimal solutions, and a branch and bound algorithm to solve the problem to optimality. Finally, computational experiments using randomly generated data demonstrate that our heuristic algorithm is very efficient and effective, and the branch and bound algorithm significantly outperforms CPLEX on MILPM in terms of computational time and the number of instances solved to optimality within the given time limit.

This paper proposes an approach to strength adjustment for MCTS-based game-playing programs. In this approach, we use a softmax policy with a strength index z to choose moves. Most importantly, we filter low quality moves by excluding those that have a lower simulation count than a pre-defined threshold ratio of the maximum simulation count. We perform a theoretical analysis, reaching the result that the adjusted policy is guaranteed to choose moves exceeding a lower bound in strength by using a threshold ratio. The approach is applied to the Go program ELF OpenGo. The experiment results show that z is highly correlated to the empirical strength; namely, given a threshold ratio 0.1, z is linearly related to the Elo rating with regression error 47.95 Elo where −2≤z ≤2. Meanwhile, the covered strength range is about 800 Elo ratings in the interval of z in [−2,2]. With the ease of strength adjustment using z, we present two methods to adjust strength and predict opponents’ strengths dynamically. To our knowledge, this result is state-of-the-art in terms of the range of strengths in Elo rating while maintaining a controllable relationship between the strength and a strength index.

Novel ultralight all-metallic truncated conical sandwich shells (TCSS) with corrugated cores are designed and fabricated using the molding process. Quasi-static axial compression tests are performed to investigate the crushing behaviors of the proposed structure, with focus placed upon its peak strength and energy absorption capacity as well as the interaction effect of its face-sheets and corrugated core during the crushing process. Detailed parametric study with three-dimensional finite element simulations is subsequently carried out to further explore the failure mechanisms as well as the influence of key geometrical parameters. Finally, the response surface models of specific energy absorption (SEA) and peak force (PF) are established with key design variables, and the Pareto fronts in terms of maximum SEA and minimum PF are obtained. Relative to monolithic conical shells having equal mass, the SEA of the TCSS is significantly greater while the PF is smaller, attractive for energy absorption applications demanding high levels of crashworthiness.

The pore structure and fractal characteristic of tight gas sandstone from Cretaceous Bashijiqike Formation in Kuqa depression were investigated, using thin section, scanning electronic microscope (SEM), mercury intrusion porosimetry (MIP), nuclear magnetic resonance (NMR) and NMR cryoporometry (NMRC) measurements. The pores of tight sandstone samples mainly contain residual primary intergranular pores, intergranular and intragranular dissolved pores, micropores related to clay aggregates and microfractures. The MIP, NMR and NMRC analyses show that the pore structure of tight sandstone is complex and heterogeneous at different pore scales, displaying as a multi-fractal feature. Fractal dimension Dg-s (with respect to small pore throats) from MIP and Dn-l (long T2 relaxation time, representing large pores and fractures or movable-fluid pores) are negatively correlated with porosity, permeability, median pore throat radius and skewness, and have inverse relationships with bound water saturation (Sbnd). Fractal dimension Dg-b (>3.0, with respect to larger pore throats) and Dn-s ( 3.0) and Dn-s (

Spherical (pellet), cylindrical and conical iron ore particles were produced to study the effect of particle shape on repose angle and bottom porosity distribution of a heap formed by natural piling. The effects of coefficient of static friction, rolling friction and restitution and particle shape on the repose angle and porosity of the pile for three particle shapes were numerically studied by the discrete element method. The results show that the primary influential factors on repose angle and porosity is the static friction, followed by particle shape. The repose angle of piles of spheres is smaller than that of non-spheres, but there is little difference in repose angle between non-spherical particles. The value of the porosity in the three kinds of piles decreases from spheres to cones to cylinders, but all show a V-shape distribution, with lowest porosity along the axis through the apex. The porosity shows a minimum value when the sphericity is about 0.85. Piles of cylindrical particles show the smallest porosity due to parallel planes of the particle shape.

Electrochemical and ex-situ X-ray absorption spectroscopic (XAS) measurements of NiCo2O4 that have been annealed in different gases (Ar and air) were made to evaluate the efficiency of its electrocatalysis of hydrogen generation by the electrolysis of urea. The electrochemical and XAS measurements demonstrated that NCO–Ar contain significant amounts of Co2+ in octahedral coordination was more favorable than NCO-air for urea electrolysis owing to the effective exposure of electrochemical active sites to participate in the Faradic redox reaction. Further insight into the electrocatalytic mechanism of NiCo2O4-based materials during urea electrolysis was obtained using ex-situ soft XAS. The results of ex-situ soft XAS revealed that the variation of the octahedral and tetrahedral coordinations of Co2+ in NCO–Ar and NCO-air importantly contributes to electrocatalytic behavior. NCO–Ar exhibits excellent electrocatalytic activity towards the Co redox couples in a repetitive cycling process owing to the creation of highly efficient active sites (octahedral Co2+). This study presents importantly x-ray spectroscopic determination of the change in electronic structure of catalyst for hydrogen production.

Neural embeddings are widely used in language modeling and feature generation with superior computational power. Particularly, neural document embedding - converting texts of variable-length to semantic vector representations - has shown to benefit widespread downstream applications, e.g., information retrieval (IR). However, the black-box nature makes it difficult to understand how the semantics are encoded and employed. We propose visual exploration of neural document embedding to gain insights into the underlying embedding space, and promote the utilization in prevalent IR applications. In this study, we take an IR application-driven view, which is further motivated by biomedical IR in healthcare decision-making, and collaborate with domain experts to design and develop a visual analytics system. This system visualizes neural document embeddings as a configurable document map and enables guidance and reasoning; facilitates to explore the neural embedding space and identify salient neural dimensions (semantic features) per task and domain interest; and supports advisable feature selection (semantic analysis) along with instant visual feedback to promote IR performance. We demonstrate the usefulness and effectiveness of this system and present inspiring findings in use cases. This work will help designers/developers of downstream applications gain insights and confidence in neural document embedding, and exploit that to achieve more favorable performance in application domains.