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With the plain text descriptions of design patterns, developers could better learn and understand the definitions and usage scenarios of design patterns. To facilitate the automatic usage of these descriptions, e.g., recommending design patterns by free-text queries, design patterns and natural languages should be adequately associated. Existing studies usually use texts in design pattern books as the representations of design patterns to calculate similarities with the queries. However, this way is problematic. Lots of information of design patterns may be absent from design pattern books and many words would be out of vocabulary due to the content limitation of these books. To overcome these issues, a more comprehensive method should be constructed to estimate the relatedness between design patterns and natural language words. Motivated by Word2Vec, in this study, we propose DPWord2Vec that embeds design patterns and natural language words into vectors simultaneously. We first build a corpus containing more than 400 thousand documents extracted from design pattern books, Wikipedia, and Stack Overflow. Next, we redefine the concept of context window to associate design patterns with words. Then, the design pattern and word vector representations are learnt by leveraging an advanced word embedding method. The learnt design pattern and word vectors can be universally used in textual description based design pattern tasks. An evaluation shows that DPWord2Vec outperforms the baseline algorithms by 17.1%-96.5% in measuring the similarities between design patterns and words in terms of Spearman's rank correlation coefficient. Moreover, we adopt DPWord2Vec on two typical design pattern tasks. In the design pattern tag recommendation task, the DPWord2Vec based method outperforms two state-of-the-art algorithms by 6.6% and 32.7% respectively when considering Recall@10. In the design pattern selection task, DPWord2Vec improves the existing methods by 6.5%-70.7% in terms of MRR.

Built on deep networks, end-to-end optimized image compression has made impressive progress in the past few years. Previous studies usually adopt a compressive auto-encoder, where the encoder part first converts image into latent features, and then quantizes the features before encoding them into bits. Both the conversion and the quantization incur information loss, resulting in a difficulty to optimally achieve arbitrary compression ratio. We propose iWave++ as a new end-to-end optimized image compression scheme, in which iWave, a trained wavelet-like transform, converts images into coefficients without any information loss. Then the coefficients are optionally quantized and encoded into bits. Different from the previous schemes, iWave++ is versatile: a single model supports both lossless and lossy compression, and also achieves arbitrary compression ratio by simply adjusting the quantization scale. iWave++ also features a carefully designed entropy coding engine to encode the coefficients progressively, and a de-quantization module for lossy compression. Experimental results show that lossy iWave++ achieves state-of-the-art compression efficiency compared with deep network-based methods; on the Kodak dataset, lossy iWave++ leads to 17.34 percent bits saving over BPG; lossless iWave++ achieves comparable or better performance than FLIF. Our code and models are available at https://github.com/mahaichuan/Versatile-Image-Compression.

Optical coherence tomography (OCT) is one of the most commonly used ophthalmic diagnostic techniques. Macular Edema (ME) is the swelling of the macular region in the eye. Segmentation of the fluid region in the retinal layer is an important step in detecting lesions. However, manual segmentation is often a time consuming and subjective process. In this paper, an improved U-Net segmentation method is proposed. In this method, the attention mechanism is introduced to automatically locate the fluid region, which avoids the problem of excessive calculation in multi-stage methods. At the same time, the use of dense skip connections which combines high-level and low-level features makes the segmentation results more precise. The loss function is a joint loss, including weighted binary cross entropy loss, dice loss, and regression loss, where regression loss is used to avoid the problem of merging multiple fluid regions into one. The experimental results show that the proposed method can adapt to the OCT scans acquired by various imaging scanning devices, and this method is more effective than other start-of-the-art fluid segmentation methods.

The design and fabrication of anisotropic photonic crystal supraballs with multiple responses are highly desirable for versatile environmental sensing and flexible displaying. Herein, we developed an available strategy to construct a series of multi-responsive magnetic colloidal photonic crystal (CPC) supraballs with Janus and molecular-analogue structures. Initially, the humidity and temperature sensitive CPC supraballs were obtained via immobilization of polyacrylamide (PAM) and polyisopropylacrylamide (PNIPAM) hydrogels into the CPC structure, respectively, and CdTe/ZnS quantum dots endow the supraballs fluorescent signal under UV light. Furthermore, Fe3O4 nanoparticles (NPs) were served as a magnetic hemisphere to construct CPC Janus supraballs which can be subsequently assembled into three different molecular-shaped cluster particles that integrate more response types via Fe3O4 NPs hemisphere coalescence to a magnetic coupling center, recognizing multiple responses simultaneously that correspond the environmental altering. In addition, 2D polychromatic patterns with sensitive CPC pixels printed by automatic printing system were demonstrated, which could monitor the changes of temperature and humidity. The multi-responsive magnetic controlling supraballs and 2D patterns reveal the promising applications in environmental sensing, anti-counterfeiting and displaying.

In this article, a decentralized adaptive finite-time tracking control scheme is proposed for a class of nonstrict feedback large-scale nonlinear interconnected systems with disturbances. First, a practical almost fast finite-time stability framework is established for a general nonlinear system, which is then applied to the design of the large-scale system under consideration. By fusing command filter technique and adaptive neural control and introducing two smooth functions, the "singular" and "explosion of complex" problems in the backstepping procedure are circumvented, while the obstacles caused by unknown interconnections are overcome. Moreover, according to the framework of practical almost fast finite-time stability, it is shown that all the closed-loop signals of the large-scale system are almost fast finite-time bounded, and the tracking errors can converge to arbitrarily small residual sets predefined in an almost fast finite time. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed finite-time decentralized control scheme.

In this paper, a partial eigenstructure assignment problem for the high-order linear time-invariant (LTI) systems via proportional plus derivative (PD) state feedback is considered. By partitioning the open-loop system into two parts (the altered part and the unchanged part) and utilizing the solutions to the high-order generalized Sylvester equation (HGSE), complete parametric expressions of the feedback gain matrices of the closed-loop system are established. Meanwhile, a group of arbitrary parameters representing the degrees of freedom of the proposed method is provided and optimized to satisfy the stability of the system and robustness criteria. Finally, a numerical example and a three-axis dynamic flight motion simulator system example with the simulation results are offered to illustrate the effectiveness and superiority of the proposed method.

To reduce the redundancy among different color channels, e.g., YUV, previous methods usually adopt a linear model that tends to be oversimple for complex image content. We propose a neural-network-based method for cross-channel prediction in intra frame coding. The proposed network utilizes twofold cues, i.e., the neighboring reconstructed samples with all channels, and the co-located reconstructed samples with partial channels. Specifically, for YUV video coding, the neighboring samples with YUV are processed by several fully connected layers; the co-located samples with Y are processed by convolutional layers; and the proposed network fuses the twofold cues. We observe that the integration of twofold information is crucial to the performance of intra prediction of the chroma components. We have designed the network architecture to achieve a good balance between compression performance and computational efficiency. Moreover, we propose a transform domain loss for the training of the network. The transform domain loss helps obtain more compact representations of residues in the transform domain, leading to higher compression efficiency. The proposed method is plugged into HEVC and VVC test models to evaluate its effectiveness. Experimental results show that our method provides more accurate cross-channel intra prediction compared with previous methods. On top of HEVC, our method achieves on average 1.3%, 5.4%, and 3.8% BD-rate reductions for Y, Cb, and Cr on common test sequences, and on average 3.8%, 11.3%, and 9.0% BD-rate reductions for Y, Cb, and Cr on ultra-high-definition test sequences. On top of VVC, our method achieves on average 0.5%, 1.7%, and 1.3% BD-rate reductions for Y, Cb, and Cr on common test sequences.

In this article, an adaptive approximation-based output-feedback tracking control scheme is presented for a class of stochastic switched lower-triangular nonlinear systems with input saturation and unmeasurable state variables. First, to overcome the design obstacle caused by the nondifferential saturation nonlinearity, a carefully selected nonlinear function of the control input signal is applied to estimate the saturation function. Then, a reduced-order state observer is designed to model the unmeasured system states, which also means the error system can be established. Furthermore, the fuzzy-logic systems are utilized to approximate the unknown system nonlinearities in the adaptive backstepping-based controller design procedure. It is ensured that all the closed-loop system variables are bounded in probability and the error signal belongs to a compact set in the mean square sense. Finally, the effectiveness and the practicability of the proposed control scheme are shown by two examples.

Purpose The purpose of this paper is to use a simple method to enhance the ability of lower limb exoskeletons to restore balance under large interference conditions and to solve the problem that biped robot stability criterion cannot be fully applied to the underactuated lower limb exoskeletons. Design/methodology/approach The method used in this paper is to construct an underactuated lower extremity exoskeleton ankle joint with a torsion spring. Based on the constructed exoskeleton, the linear inverted torsion spring pendulum model is proposed, and the traditional capture point (CP) concept is optimized. Findings The underactuated exoskeleton ankle joint with torsion springs, combined with the improved CP concept, can effectively reduce the forward stepping distance under the same interference condition, which is equivalent to enhancing the balance ability of the lower extremity exoskeleton. Originality/value The contribution of this paper is to enhance the balance ability of the exoskeleton of the lower limbs under large interference conditions. The torsion spring is used as the exoskeleton ankle joint, and the traditional CP concept is optimized according to the constructed exoskeleton.

Image/video compression and communication need to serve both human vision and machine vision. To address this need, we propose a scalable image compression solution. We assume that machine vision needs less information that is related to semantics, whereas human vision needs more information that is to reconstruct signal. We then propose semantics-to-signal scalable compression, where partial bitstream is decodeable for machine vision and the entire bitstream is decodeable for human vision. Our method is inspired by the scalable image coding standard, JPEG2000, and similarly adopts subband-wise representations. We first design a trainable and revertible transform based on the lifting structure, which converts an image into a pyramid of multiple subbands; the transform is trained to make the partial representations useful for multiple machine vision tasks. We then design an end-to-end optimized encoding/decoding network for compressing the multiple subbands, to jointly optimize compression ratio, semantic analysis accuracy, and signal reconstruction quality. We experiment with two datasets: CUB200-2011 and FGVC-Aircraft, taking coarse-to-fine image classification tasks as an example. Experimental results demonstrate that our proposed method achieves semantics-to-signal scalable compression, and outperforms JPEG2000 in compression efficiency. The proposed method sheds light on a generic approach for image/video coding for human and machines.

Quantum dots (QDs)/gel composites have sparked tremendous interests due to their great potentials in sensors. However, in most cases, these sensors are focused on single signal detection, suffers from a key hurdle of low signal density. Thus, it is highly desirable to develop multi-signal QDs/gel-based sensors. In this work, we constructed dual-signal QDs/gel assemblies by a microfluidic-assisted method, which shows promising application in dual-signal sensors. Typically, self-healing QDs/gel beads with different fluorescent signals were fabricated via microfluidic techniques. In virtue of the microfluidic-assisted self-assembly, various dual-signal assemblies were constructed controllably in the confined channels, where self-healing QDs/gel beads serve as building blocks. As expected, the assemblies exhibit rapid dual-signal detection capability toward organic amines and pH values. In addition, by using self-healing gels as printable materials, the combination of the self-assembly strategy with 3D microfluidic printing allows the generation of diverse responsive dual-signal patterns and structures. The self-assembly of self-healing building blocks not only offers a promising route toward fluorescent dual-signal architectures for advanced sensors, but will also guide the development of 3D printing for controllable fabrication of functional assemblies.

The degradation of Ni-based superalloys in molten salts is closely related with the evolution of corrosion scales. In this work, the evolution of corrosion scales, the characteristics of the scale/alloy matrix interface associated with the corrosion mechanisms and the corrosion kinetics of a Ni-based superalloy Ni–20Cr–18W in molten salts were investigated. The outer surface of the corrosion scales was composed of numerous flakes. From the cross-sectional view, the scales gradually presented a layered structure and its formation can be ascribed to the morphology evolution of the inclusions inserted within the scales. It was found that the corrosion progress was a cooperative process involving in both oxidation and sulfuration, and the element of sulfur (S) took the precedence to penetrate into the matrix and resulted in an obvious sulfuration zone nearby the scales/alloy matrix interface. The corrosion activation energy of the investigated alloy was calculated to be 125.27 ​kJ/mol. This work is expected to work out the structural evolution of the corrosion scales and the characteristics of the scale/matrix interface associated with the corrosion mechanisms for Ni-based superalloys.

It still is a challenge to create a superior and easily coupled bifunctional electrocatalyst for water splitting impelled by a low voltage. In this work, the controlled growth of Co2P NAs on the surface of a MXene (Ti3C2Tx)-modified self-supporting electrode is demonstrated as a competent and reliable bifunctional electrocatalyst for efficient water splitting. The heterointerface in Co2P@Ti3C2Tx with an optimized adsorption free energy of H*, H2O, and better conductivity can give enhanced HER (hydrogen evolution reaction) activity, with a low overpotential (42 mV) at 10 mA cm-2. Additionally, the OER (oxygen evolution reaction) activity has also been similarly strengthened by the synergy of Co2P and MXene with an overpotential of 267 mV to arrive at 10 mA cm-2. Furthermore, the excellent bifunctional electrode (Co2P@Ti3C2Tx∥Co2P@Ti3C2Tx) exhibits efficient engineering water-splitting performance (1.46 V@10 mA cm-2) in alkaline solution. This simple design can propose a promising approach to exploit precious-metal-free catalysts for energy conversion.

This study examines the job-access inequity between the richest 10% and poorest 40% transit-based workers across space (i.e. central city, the inner-ring/outer-ring suburb) and race (i.e. white, bl...

Hydrogen evolution reaction (HER) is a critical process in electrocatalytic water splitting for hydrogen production. However, the development of low-cost electrocatalysts for highly efficient HER is still a huge challenge. Hence, we fabricate a multi-metal phosphide on Ni foam, FeCoNiNbxP, through a facile hydrothermal reaction followed by phosphorization. We find that Nb promotes the formation of metal phosphides, and the main phases of the catalysts with Nb are multiphase phosphides. Importantly, the Nb incorporation significantly improves the HER activity of FeCoNiP. We show that FeCoNiNb0.3P has the best HER activity, which only requires an overpotential of 78 mV to achieve a current density of 10 mA cm−2 in 1 M KOH, and demonstrates excellent stability under both constant potential and varied current densities. Our findings show that the multiple-metal compounds are beneficial to the improvement of catalytic activity and provide guidance on the design of novel catalysts for applications.

Eddy current testing (ET) and eddy current thermography (ECT) are both important nondestructive testing methods that have been widely used in the field of conductive materials evaluation. Conventional ECT systems have often employed to test static specimens even though they are inefficient when the specimen is large. In addition, the requirement of high-power excitation sources tends to result in bulky detection systems. To mitigate these problems, in this article, a moving detection mode of multiphysics structured ET and ECT is proposed in which a novel L-shape ferrite magnetic yoke circumambulated with array coils is designed. The theoretical derivation model of the proposed method is developed which is shown to improve the detection efficiency without compromising the excitation current by ECT. The specimens can be speedily evaluated by scanning at a speed of 50–250 mm/s while reducing the power of the excitation current due to the supplement of ET. The unique design of the excitation-receiving structure has also enhanced the detectability of omnidirectional cracks. Moreover, it does not block the normal direction visual capture of the specimens. Both numerical simulations and experimental studies on different defects have been carried out and the obtained results have shown the reliability and detection efficiency of the proposed system.

In order to prevent the inter-molecular aggregation and retard electron recombination, blocking side substituents, i.e. alkyl and alkylphenyl, were incorporated into the bithiophene bridge and two new carbazole-based sensitizers (CzD2 and CzD3) were synthesized. A reference dye (CzD1) comprising bithiophene spacer was also synthesized for comparative purposes. Their photophysical, electrochemical, structural and electronic properties were extensively investigated for highlighting the effects of side substituents. Moreover, their photovoltaic properties were evaluated using cobalt-containing electrolyte. The CzD3 with the bulkiest side group yielded the highest power conversion efficiency (PCE) of 6.20% due to the largest open circuit voltage (Voc) and short circuit photocurrent (Jsc). Furthermore, incident photon-to-current efficiency (IPCE) spectra and electrochemical impedance spectroscopy (EIS) analysis were performed to reveal the impact of side substituents on the performance of devices. Therefore, this work demonstrates that incorporating blocking side groups into the π-bridge is an effective way to obtain high PCEs of sensitized cells with cobalt redox.

In this study, the low emission combustion technology of Rich-Quench-Lean (RQL) has been applied in Trapped-Vortex Combustor (TVC), and the combinative RQL-TVC shows a promising low emissions performance. By utilizing a quench orifice plate combined with a bluff-body, a lab-scale RQL-TVC was designed. The flow fields of RQL-TVC were measured by 2-D PIV and predicted by 3-D numerical simulation. Flow structures, radial profiles of normalized mean axial velocity, turbulence intensity and mixing level of the quench zone were analyzed. Results reveal that the dual-vortex and the single-vortex flow patterns both exist in cavities and quench zone of RQL-TVC, and the turbulence intensity is strong in the quench zone with some reverse flows. The spiral vortex was discussed by 3-D streamlines and the detail flow structures of the quench zone were analyzed based on the numerical results. The mixing level of the quench zone was determined, and results show that the quench device enhances the mixing level compared with TVC. Combustion efficiency and emissions performance were investigated experimentally, and results demonstrate that RQL-TVC has relatively higher combustion efficiency and lower emission index of CO, UHC and NOx than the same size lab-scale TVC in present work.

Deep learning-based methods especially using convolutional neural network (CNN) and generative adversarial network (GAN) have achieved certain success for the task of image inpainting. The previous methods usually try to generate the content in the missing areas from scratch. However, these methods have difficulty in producing salient image structures that appear natural and consistent with the neighborhood, especially when the missing area is large. In this paper, we address the challenge by introducing edges into the convolutional GAN-based inpainting. We split the inpainting task into two steps: first edge generation, then edge-based image generation. We adopt CNN to accomplish the two steps and use GAN-based training, thus our method is named E2I: generative inpainting from edge to image. Specifically, we adopt a deep network-based edge detector to achieve an edgeness map of an incomplete image, then we fill-in the missing areas in the edgeness map, and finally generate the missing pixels with the assistance of the complete edgeness map. We verify the proposed method on three challenging image datasets: Places2, ImageNet, and CelebA. We also compare our method with the state-of-the-arts on the Places2 test set. Our experimental results demonstrate the superior performance of our method in producing more plausible inpainting results.

In order to study the mechanism of excavation and supporting process of equivalent circular roadway, the model of soft roadway was established firstly. The elastoplastic solutions in excavation process were deduced based on Drucker-Prager strength criterion. Then, the elastoplastic solution under supporting condition was obtained based on homogenization method under the condition of rockbolts and liner supporting. Lastly, an example was analyzed to study the effect of different factors such as “space effect,” supporting opportunity, stresses, surrounding displacement, and the radius of plastic zone. Based on theoretical research case, the change rules of considering the “space effect” and the supporting opportunity when calculating the subarea of the roadway were discussed, the control of interval distance of rockbolts on the displacement of surrounding rock mainly reflecting in the plastic residual zone and the “space effect” in excavation, and the supporting time to control the displacement of surrounding rock not being ignored are revealed. The results can provide an important theoretical basis for the stability evaluation and quantitative support design of roadway surrounding rock. Therefore, the “space effect” and the supporting time to control the displacement and stresses of surrounding rock can not being ignored in underground engineering.

Six-axis motion is essential for the evaluation of the wear failure modes of dental prostheses with complete teeth morphologies, and a high occlusal force capacity is vital for static clenching and dynamic bruxism. Additionally, the saliva environment influences abrasive particles and crack growth. The present research was aimed at the development of a six-axis masticatory and saliva simulator with these capacities. The masticatory simulator was designed based on a six-axis parallel mechanism, and the saliva simulator consisted of a saliva circuit and a temperature control loop. A control system of the masticatory and saliva simulators was constructed. The operating interface includes a centric occlusal position search, a static test, a dynamic test, a saliva supply, and data reporting. The motion and force performances of the masticatory simulator were evaluated. The flow rate and temperature change of the saliva simulator were calculated. For the occlusal position-searching, the driving amplitude is linear with the moving variables during minor one-axis motion. For the static tests, the force capacity of the driving chain is 3540 N, while for the dynamic tests, the force capacity is 1390 N. The flow rate of the saliva is 0.18–51.84 mL/min, and the saliva can effectively wet the prosthesis without the risk of overflow. Moreover, the saliva temperature can increase from room temperature (23°C) to body temperature (37°C) in about 6 min. The proposed DUT-2 simulator with six-axis motion, high force, and a salvia environment provides an in vitro testing approach to validate numerical simulation results and explain the clinical failure modes of prostheses. The centric occlusal position-searching, static tests, and dynamic tests could therefore be executed using a single testing machine. Moreover, the proposed device is more compact than previously reported six-axis masticatory simulators, including the Bristol simulator and DUT-1 simulator.

The performance of classical lithium-ion technologies based on liquid electrolytes has made great advances in the last two decades, but safety issues are inevitable for batteries assembled with liquid electrolytes, which have intrinsic instability. In response to this issue, solid polymer electrolytes are developed in the lithium batteries field extensively for improving safety issues, as well as miniaturizing and enhancing the energy densities. Considering the great advantages of polymerized ionic networks, such as facile synthesis, high charge density, and high stability, two polymerized ionic networks (PDM-TFSI-1 and PDM-TFSI-2) with different polysubstituted benzene groups have been developed. The new type of polymerized ionic network with cross-linked structure was synthesized by the simple radical copolymerization of 2-(dimethylamino)ethyl methacrylate and methyl methacrylate monomers, then the copolymer (Mₙ = 14.5 kg mol⁻¹) reacted with polysubstituted bromobenzene to form a cross-linked structure, and finally this was ion exchanged with bis(trifluoromethane)sulfonimide lithium salt (LiTFSI). The ionic networks filled with ionic liquid/LiTFSI mixture exhibit high ionic conductivity (1.0 × 10–⁴ and 1.9 × 10–⁴ S cm–¹) and high decomposition potential (4.7 V vs Li/Li⁺) at 30 °C. The batteries were assembled by sandwiching the obtained polymer electrolytes between a LiFePO₄ cathode and a lithium anode, exhibiting good cycling stability and specific capacity, suggesting that these novel kinds of solid polymer electrolyte are promising for application in next-generation lithium-ion batteries.

The negative photoconductivity (NPC) effect originating from the surface shell layer has been considered as an efficient approach to improve the performance of optoelectronic nanodevices. However, a scientific design and precise growth of NPC-effect-caused shell during nanowire (NW) growth process for achieving high-performance photodetectors are still lacking. In this work, GeS NWs with a controlled sulfur-rich shell, diameter, and length are successfully prepared by a simple chemical vapor deposition method. As checked by transmission electron microscopy, the thickness of the sulfur-rich shell ranges from 10.5 ± 1.5 to 13.4 ± 2.5 nm by controlling the NW growth time. The composition of the sulfur-rich shell is studied by X-ray photoelectron spectroscopy, showing the decrease of S in the GeSx shell from the surface to core. When configured into the well-known phototransistor, a featured NPC effect is observed, benefiting the high-performance photodetector with high responsivity of 105 A·W-1 and detectivity of 1012 Jones for λ = 405 nm with ultralow intensity of 0.04 mW·cm-2. However, the thicker-shell NW phototransistor shows an unstable photodetector behavior with smaller negative photocurrent because of more hole-trapping states in the thicker shell. All results suggest a careful design and controlled growth of an NPC-effect-caused shell for future optoelectronic applications.

Axial closed-die rolling (ACDR), as a new type of local loading and labor-saving forming method, has a broad application prospect in the production of large-scale rotary parts, compared with traditional upsetting forming. However, the different evolutions of thermal and mechanical parameters and microstructure between the two forming modes are still ambiguous. In this study, the thermal and mechanical parameters and microstructure evolutions of 30Si2MnCrMoVE UHSS after ACDR and upsetting deformation were studied by means of numerical simulation, metallographic observation, and scanning electron microscope (SEM). The temperature and effective strain evolution of the upper surface, central axis, and middle height (1/2h) of the workpiece were analyzed by numerical simulation. Combined with the evolution of temperature and equivalent strain, the mechanisms of original austenite microstructure evolution of the two deformation modes were analyzed. The results show that there is only one deformation dead zone at the lower end of the workpiece after ACDR deformation which is different from upsetting. The deformation temperature is the primary reason for the difference of microstructure evolution at 1/2h between ACDR and upsetting deformation. The microhardness at 1/2h of the sample after ACDR deformation and direct quenching is lower than that of upsetting.

As one of the most important narrow bandgap ternary semiconductors, GaAs1−xSbx nanowires (NWs) have attracted extensive attention recently, due to the superior hole mobility and the tunable bandgap, which covers the whole near-infrared (NIR) region, for technological applications in next-generation high-performance electronics and NIR photodetection. However, it is still a challenge to the synthesis of high-quality GaAs1−xSbx NWs across the entire range of composition, resulting in the lack of correlation investigation among stoichiometry, microstructure, electronics, and NIR photodetection. Here, we demonstrate the success growth of high-quality GaAs1−xSbx NWs with full composition range by adopting a simple and low-cost surfactant-assisted solid source chemical vapor deposition method. All of the as-prepared NWs are uniform, smooth, and straight, without any phase segregation in all stoichiometric compositions. The lattice constants of each NW composition have been well correlated with the chemical stoichiometry and confirmed by high-resolution transmission electron microscopy, X-ray diffraction, and Raman spectrum. Moreover, with the increase of Sb concentration, the hole mobility of the as-fabricated field-effect-transistors and the responsivity and detectivity of the as-fabricated NIR photodetectors increase accordingly. All the results suggest a careful stoichiometric design is required for achieving optimal NW device performances.

In this paper, we study a category of restricted modules for the Ovsienko-Roger algebra, which is an extension of the Virasoro algebra of its tensor density module of degree one. We construct and characterize simple modules in this category and give natural free field realizations of certain restricted modules using the Weyl vertex algebra.

Finite Element Method (FEM) and Smoothed Particle Hydrodynamics (SPH) method are effective methods to study the interaction between marine structures and sea ice. However, both FEM and SPH methods have their own shortcomings in numerical simulations. There are mesh distortions and disappearance in FEM, and tensile instability, difficulty in applying boundary conditions, and low computational efficiency in SPH. Thus, to make up those problems, it is essential to develop a new numerical method. In the present study, the FEM–SPH adaptive method was applied in the numerical simulation of icebreaking, which could convert finite elements into SPH particles based on given conditions. Numerical models of cone and icebreaker interactions with level ice were established. Then, numerical simulation results were compared with empirical formula and model test results. In addition, the effects of cone angle and ice strength were analyzed. It was demonstrated that this algorithm could accurately predict the icebreaking resistance, which is the most concerned parameter in practice. Simultaneously, it could effectively simulate the accumulation process of ice rubbles. Thus, the FEM–SPH adaptive method is considered to be an effective way to simulate the interaction between marine structures and sea ice, and has great potential in the numerical simulation of icebreaking.

Amidst the material development for solar water splitting, silicon has been known as a promising photocatalyst candidate given its natural abundance and high processability toward future scale-up a...

A vein structure, which becomes more pronounced with increasing ion dose, was found on the surface of polycrystalline HOPG (highly oriented pyrolytic graphite) implanted by ex situ C+ (up to 1.8 × 1017 ions/cm2), and in situ Ar+ in a transmission electron microscope (TEM). These veins are found to be independent of the crystallographic orientations and are associated with the formation of pores. Underneath the veins, a triangular-shaped core was formed with the graphite platelet inside the core displaced up towards the surface. A macro-scale ‘ruck&tuck’ geometry was thus generated at these triangle structure boundaries. Progressive movement of dislocations along basal planes during irradiation was observed, and a mechanistic model was proposed on this basis to explain the vein formation. A small increase of c-spacing was observed with irradiation but it is believed that macro-scale vein formation plays a more vital role in the dimensional and property changes in polycrystalline graphite, especially when a stress gradient is present. The model proposed also explains the change of thermal expansion in HOPG with irradiation. Together with Heggie’s ‘ruck&tuck’ and Barsoum’s ‘ripplocations’ models, the present model is considered to have provided an additional experimentally proven mechanism responsible for irradiation behaviour in graphite materials.

Frame extrapolation is to predict future frames from the past (reference) frames, which has been studied intensively in the computer vision research and has great potential in video coding. Recently, a number of studies have been devoted to the use of deep networks for frame extrapolation, which achieves certain success. However, due to the complex and diverse motion patterns in natural video, it is still difficult to extrapolate frames with high fidelity directly from reference frames. To address this problem, we introduce reference frame alignment as a key technique for deep network-based frame extrapolation. We propose to align the reference frames, e.g. using block-based motion estimation and motion compensation, and then to extrapolate from the aligned frames by a trained deep network. Since the alignment, a preprocessing step, effectively reduces the diversity of network input, we observe that the network is easier to train and the extrapolated frames are of higher quality. We verify the proposed technique in video coding, using the extrapolated frame for inter prediction in High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC). We investigate different schemes, including whether to align between the target frame and the reference frames, and whether to perform motion estimation on the extrapolated frame. We conduct a comprehensive set of experiments to study the efficiency of the proposed method and to compare different schemes. Experimental results show that our proposal achieves on average 5.3% and 2.8% BD-rate reduction in Y component compared to HEVC, under low-delay P and low-delay B configurations, respectively. Our proposal performs much better than the frame extrapolation without reference frame alignment.

End-to-end image compression using trained deep networks as encoding/decoding models has been developed substantially in the recent years. Previous work is limited in using a single encoding/decoding model, whereas we explore the usage of multiple encoding/decoding models as an ensemble. We propose several methods to obtain multiple models. First, we adopt the boosting strategy to train multiple networks with diversity as an ensemble. Second, we train an ensemble of multiple probability distribution models to reduce the distribution gap for efficient entropy coding. Third, we present a geometric transform-based self-ensemble method. The multiple models can be regarded as the multiple coding modes, similar to those in non-deep video coding schemes. We further adopt block-level model/mode selection at the encoder side to pursue rate-distortion optimization, where we use hierarchical block partitioning to improve the adaptation ability. Compared with single-model end-to-end compression, our proposed method improves the compression efficiency significantly, leading to 21% BD-rate reduction on the Kodak dataset, without increasing the decoding complexity. On the other hand, when keeping the same compression efficiency, our method can use much simplified decoding models, where the floating-point operations are reduced by 70%.

In order to alleviate the dust pollution in the fully mechanized mining face, the novel negative pressure entrainment dust collector was optimized by the method of orthogonal experimental design and numerical simulation. The influences of nozzle distance, throat diameter and diffusion angle on the atomization efficiency of the proposed dust collector was investigated. The obtained results showed that the optimum ejection dust removal performance of the developed negative pressure entrainment dust collector was obtained for throat diameter 80 mm, throat distance 60 mm and diffusion angle 20 degrees. Under these conditions, air suction rate at the entrance of contraction section was 17.48 m3/min and the average droplet size and mass concentration were 62.99 μm and 0.034 kg/m3, respectively. Here, we have proposed a local closed atomization control dust removal technology for hydraulic support areas based on the optimized negative pressure entrainment dust collector. After the application of the developed system in 2307 fully mechanized mining face in Tangkou Coal Mine, the average total dust of measuring points was decreased from 337.90 to 49.53 mg/m3 and average exhaust dust was decreased from 271.80 to 41.40 mg/m3. The average dust removal rates of total dust and exhaled dust were 85.17% and 84.74% respectively, and dust removal efficiency of the proposed system was obviously confirmed.

The Triassic igneous complexes in the east flank of the East Kunlun orogenic belt (E-KOB) provide a window through which to investigate the Mesozoic magmatism in the E-KOB. Here, we report the prec...

Bisphenol-A polycarbonate (PC) is an intrinsically crystallizable polymer. Its industrial parts, however, invariably remain in an amorphous state due to its extremely slow crystallization kinetics....

A good combination of strength and fracture toughness is required for aerospace applications of laser additively manufactured (LAM) AerMet100 steel. To understand the role of microstructure in the combination of strength and fracture toughness of LAM AerMet100 steel, microstructures, fracture toughness properties and corresponding crack-tip fracture behaviors of the as-deposited specimen and five types of heat-treated specimens were mainly investigated. The results indicate the as-deposited microstructure of LAM AerMet100 steel with a high amount of retained austenite has a high KIC value of ~ 81.7 MPa m1/2. After a short process of cryogenic treatment and subsequent tempering treatment, the KIC value of the steel has the lowest value of ~ 60 MPa m1/2 due to the apparent decrease of austenite and weak cracking resistance, associating with intergranular cracking along the prior austenite columnar grain boundaries and transgranular cracking of intercellular regions. In contrast, the crack-tip fracture mode of the equiaxed-grain tempered martensite microstructure of LAM AerMet100 steel becomes completely transgranular cracking with the main mechanisms of micro-void nucleation and coalescence. Homogenization treatment can apparently decrease the amount and size of large-size alloy carbides and effectively improve the fracture toughness of the steel. Furthermore, proper microstructure modification (including control of large-size carbide, grain refinement and a decrease of retained austenite) by a longer treatment process can prompt tempered LAM AerMet100 steel to achieve a good combination of yield strength (~ 1827 MPa) and fracture toughness (~ 113 MPa m1/2) compared with the wrought one.

Herein, chemical activation of chitosan gel by KNO3 to prepare nitrogen-doped carbon. The sol-gel method enabled the activation agent KNO3 to be evenly dispersed in the carbon precursor, thereby ge...

Ring rolling technology is widely used in the production of GH4738 aerospace rings, but the ring produced by ring rolling often has the phenomenon of uneven stress and unqualified strength. To solve these problems, the thick-walled ring bulge forming technology was proposed. In this paper, a 3D-coupled thermo-mechanical FE model of the GH4738 rectangular ring bulging process was developed, which is verified to be in good agreement with the experimental process. Based on this model, the thermal parameter distribution and evolution of GH4738 rectangular ring during the bulging process were investigated systematically. Then, the evolution of the minimum bulging displacement (BD) required for rings with the change of ring size was obtained by comparing the stress distribution of rings during the bulging process in seven cases. All these provide a theoretical basis for the selection of optimal BD in practical production. Finally, the influence of the bulging process on the microstructure and mechanical properties of GH4738 alloy ring was analyzed by experimental means, and it was verified that the bulging process can play a good role in improving the strength of the ring from the perspective of the average geometrically necessary dislocation (GND) density. The experimental results indicate that with the increase of BD, the grain size of the ring does not change that much, but the average GND density increases gradually, and the yield strength and tensile strength of the ring increase significantly.

Preparation of high-activity and earth-abundant bifunctional catalysts for efficient electrochemical water splitting are crucial and challenging. Herein, Co-doped Ni3N nanosheets loaded on nickel foam (Co-Ni3N) were synthesized. The as-prepared Co-Ni3N exhibits excellent catalytic activity toward both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) in alkaline media. Density functional theory (DFT) calculation reveals that Co-Ni3N with redistribution of electrons not only can facilitate the HER kinetics but also can regulate intermediates adsorption energies for OER. Specifically, the Co-Ni3N exhibits high efficiency and stable catalytic activity, with an overpotential of only 30 and 270 mV at a current density of 10 mA cm-2 for the HER and OER in 1 M KOH, respectively. This work provides strong evidence to the merit of Co doping to improve the innate electrochemical performance in bifunctional catalysts, which might have a common impact in many similar metal-metal nitride electrocatalysts.

Effects of cooling rate and 0.25 at.% TiB2 addition on solidification microstructure and mechanical properties of Ti–48Al–2Cr–2Nb alloys fabricated by the investment casting with different thicknesses were studied. The results show that with the cooling rate increasing from 37 to 2×102 K/s, the solidification path of the studied alloys is unchanged. The grain size of the matrix alloy is refined from 650 to 300 μm, while the grain size of Ti–48Al– 2Cr–2Nb–TiB2 is reduced from 550 to 80 μm. The lamellar spacing of matrix alloy is reduced from 360 to 30 nm with increasing the cooling rate from 37 to 2×102 K/s, while TiB2 addition shows little refinement effect on the lamellar spacing. Ti–48Al–2Cr–2Nb–TiB2 sample under medium cooling rate (69 K/s) exhibits superior microhardness (HV 550) and ultimate tensile strength (570 MPa) among the studied alloys. The refined grain size, lamellar spacing and fine TiB2 particles could account for the favorable mechanical properties of the studied TiB2-containing alloy. The microstructure evolution was discussed in light of cooling rate, constitutional supercooling and borides addition.

This work develops a new four-node quadrilateral displacement-based Trefftz-type plate element for bending analysis of orthotropic plates within the framework of the unsymmetric finite element method (FEM). In the present formulation, the modified isoparametric interpolations are employed to formulate the element’s test functions in which the deflection is effectively enriched by the nodal rotation degrees of freedom (DOFs). Meanwhile, the element’s trial functions are determined based on the Trefftz functions that can a prior satisfy the governing equations of orthotropic Mindlin–Reissner plates. Numerical benchmark tests reveal that the new unsymmetric plate element is free of shear locking problem and can produce satisfactory results for both the displacement and stress resultant. In particular, it exhibits quite good tolerances to the gross mesh distortion.

The exploration of nonlinear optical (NLO) materials with a large NLO response promotes a diverse range of photonic applications. With excellent optoelectronics property, perovskite semiconductors ...

Posterior capsule opacification (PCO) is one of the most frequent complications in cataract surgery and likely to cause the second loss of vision. Proliferation and migration of postoperative remnants of lens epithelial cells (LECs) on the implanted intraocular lens (IOL) are the leading causes of PCO. Antiproliferative drugs can be an effective solution but also possess some problems including sudden release and accompanying adverse effects to surrounding normal tissues, which greatly limit the clinical trials. In this study, an antiproliferative drug Paclitaxel (Pac) -sustained released hyaluronic acid (HA) and chitosan (CHI) multilayer modified IOL with postoperatively long-term PCO prevention was fabricated

Cascading failure inevitably emerges in complex power grids that can cause enormous economic losses. Over the past few decades, a great deal of effort has been devoted to failure cascade modeling, with the aim of understanding and preventing this complex process. Significant achievements include reproducing the uneven cascade propagation profiles and the power-law distribution of the size of the cascades. At the same time, power grids keep evolving, as driven by technology advancements and practical needs. Wide deployment of information technologies and high penetration of power electronics devices are two important features of future grids. These developments have profound influences on the cascading failure characteristics in future grids, which cannot be well addressed by existing models. In this tutorial, we provide an overview of the state-of-art approaches in studying cascading failure and discuss the strengths and weaknesses of these methods. The explicit changes brought by the new features to the failure cascade processes are analyzed and the limitations of existing approaches are identified. Challenges in the modeling and analysis of cascading failure and possible research directions targeting the future power grids in the next research phase are presented.

Rational design of efficient, low-cost, and durable electrocatalysts for the hydrogen evolution reaction (HER) in various pH media is highly desirable but remains challenging. Herein, for the first time, we present a novel hybrid of ruthenium diphosphide encapsulated in P-doped porous carbon (denoted as RuP2@PC) utilizing metal–organic frameworks (MOFs) as templates by a multi-step strategy. Unexpectedly, benefitting from the synergistic contribution of highly dispersed ultrafine RuP2 nanoparticles, porous carbon skeleton, and P-dopant, the as-synthesized RuP2@PC exhibits exceptional catalytic activity and superior durability as pH-universal electrocatalysts for the HER. Particularly, a low overpotential of 78.9 mV to deliver a current density of 10 mA cm−2 and a small Tafel slope of 36.7 mV dec−1 can be achieved in 1.0 M KOH toward the HER. Such outstanding electrocatalytic properties exceed those of commercial Pt/C and rank the catalyst among the best HER electrocatalysts reported until now. Importantly, this work paves a new route in elaborately fabricating efficient and stable electrocatalysts based on MOFs for energy-related fields.

The idea of accounting for a priori shape information is gaining increasing levels of interest and traction in the electrical impedance tomography (EIT) reconstruction. A supershape-based reconstruction approach is proposed for EIT. We begin by assuming that the conductivity profile to be reconstructed in the measured domain is piecewise constant distributed. Using this formulation, the image reconstruction problem is transformed into a shape reconstruction problem. The inclusion boundary (e.g., interface between the inclusion and the background) to be estimated is treated as a supershape and expressed through a single equation—the so-called super formula. We illustrate the performance of the proposed approach using synthetic and experimental water tank data. In addition, robustness studies considering different regularization types ( $L_{1}$ and $L_{2}$ norms), varying noise levels and different initial settings are tested of the proposed approach. Four evaluation matrices relative size coverage ratio (RCR), structural similarity index (SSIM), relative contrast (RCo), and correlation coefficient (CC) are used to quantitatively evaluate the performance of the proposed method. Both the simulation and experimental results show that the supershape-based method leads to reliable and robust reconstructions and improves the ability to simultaneously reconstruct objects with smooth boundaries and/or sharp features. The proposed approach provides an excellent opportunity for the shape reconstruction framework to express a wide variety of shapes, including geometric primitives. Supershapes can simply represent regular polygons and natural shapes with various symmetries while maintaining a parametric representation.

Solving complex optimization problems in engineering and the physical sciences requires repetitive computation of multi-dimensional function derivatives. Commonly, this requires computationally-demanding numerical differentiation such as perturbation techniques, which ultimately limits the use for time-sensitive applications. In particular, in nonlinear inverse problems Gauss-Newton methods are used that require iterative updates to be computed from the Jacobian. Computationally more efficient alternatives are Quasi-Newton methods, where the repeated computation of the Jacobian is replaced by an approximate update. Here we present a highly efficient data-driven Quasi-Newton method applicable to nonlinear inverse problems. We achieve this, by using the singular value decomposition and learning a mapping from model outputs to the singular values to compute the updated Jacobian. This enables a speed-up expected of Quasi-Newton methods without accumulating roundoff errors, enabling time-critical applications and allowing for flexible incorporation of prior knowledge necessary to solve ill-posed problems. We present results for the highly non-linear inverse problem of electrical impedance tomography with experimental data.

Spacecraft materials are a key limiting factor for the rapid development of the aerospace exploration field. In an advanced spacecraft, superior multi-functional material with thermal management and electromagnetic shielding can ensure the normal operation of its equipment in space. Currently, graphene thin film can’t satisfy a high heat flux and excellent through-plane thermal conduction. In this contribution, a full-carbon dual-functional graphene/carbon nanotubes (CNTs) thick film with high heat flux was successfully prepared, and the structure and composition evolution was investigated after hot-pressing carbonization and graphitization of 2800 °C, which indicates the existence of a compact defects-free and high crystalline carbon structure. Molecular dynamics simulations further confirm the formation of C–C covalent bonds between graphene sheets and CNTs after 2800 °C graphitization, enhancing the phonons transfer in through-plane. Simultaneously, the axially adjacent graphene sheets are connected by the CNTs, which endow excellent thermal conductive properties. The in-plane and through-plane thermal diffusivity are as high as 1188.2 mm2/s and 8.0 mm2/s, respectively. Moreover, the electrical conductivity up to 1819.17 S/cm and EMI SE reach 75 dB in Ku-band. The results provide a bright prospect for spacecraft materials preparation and application.

When quantum dots are used as fluorescent probes or drug tracers for in vivo imaging, the quantum dots in the blood will come into direct contact with vascular endothelial cells. Thus, it is necessary to study whether quantum dots can affect endothelial function after being injected into blood vessels as imaging agents. In recent years, there have been numerous studies on the toxicity of quantum dots. Herein, we focused on five types of quantum dots (Cd-containing quantum dots, CuInS2 quantum dots, black phosphorus quantum dots, MXene quantum dots, and carbon-based quantum dots) for cell imaging and their toxicity in vivo and in vitro. Although current research on the toxicity of quantum dots has not reached a consistent conclusion, it can guide the next step in evaluating their cytotoxicity.

For the study of various retinal diseases, an accurate quantitative analysis of the retinal layer is essential for assessing the severity of the disease and diagnosing the progression of the disease. Optical coherence tomography (OCT) images can clearly show each layer of the retinal structure and detect subtle early lesions, thus providing a gold standard for the diagnosis of retinal diseases. In this article, we propose a coarse-to-fine retinal layer boundary segmentation method based on the embedded residual recurrent network (ERR-Net) and the graph search (GS). Considering the integrity of information transmission and the dependence of features, we first design a novel end-to-end residual recurrent network to roughly segment the retinal layer boundaries. The proposed ERR-Net not only solves the gradient problem brought by the depth but also fully captures the global spatial structure of the image. Second, we employ a GS algorithm for boundary continuous to make the layer boundary segmentation results more accurate. Finally, we evaluate the effectiveness of the proposed method on three publicly available datasets and compare it with the state-of-the-art methods on each dataset. The quantitative results and visual effects show that the proposed method outperforms the state-of-the-art approaches in the segmentation of retinal layer boundaries in OCT images. Moreover, the results also show that the proposed method has good algorithm stability on three datasets with different sizes, characteristics, and segmentation difficulties.

Controlling the products, i.e. achieving high selectivity, is essential yet challenging for many chemical processes. So far, ex/in situ methods have been reported to prepare selective catalysts; here, we present a distinct operational strategy. To demonstrate its power and simplicity, a high-pressure artificial photosynthetic device (HiPAD) was designed correspondingly to achieve selective and efficient solar-driven photoelectrochemical (PEC) carbon dioxide reduction (CO2R). The HiPAD features a high-pressure operation to enhance the CO2 concentration, as well as straight operations of copper (Cu) catalyst synthesis and Cu-catalyzed PEC CO2R to enhance the active site concentration. This strategy is quantitatively described by the Langmuir mechanism. By leveraging it, CO2R selectivity higher than 80% and solar-to-chemical energy conversion efficiency exceeding 0.9% were experimentally demonstrated. The material and synthesis versatility feature of this strategy was also demonstrated. These results shed new light on selective and efficient catalytic conversions.