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Flying insects are remarkably agile and robust. As they fly through cluttered natural environments, they can demonstrate aggressive acrobatic maneuvers such as backflip, rapid escape, and in-flight collision recovery. Current state-of-the-art subgram microaerial-vehicles (MAVs) are predominately powered by rigid actuators such as piezoelectric ceramics, but they have low fracture strength (120 MPa) and failure strain (0.3%). Although these existing systems can achieve a high lift-to-weight ratio, they have not demonstrated insect-like maneuvers such as somersault or rapid collision recovery. In this article, we present a 665 mg aerial robot that is powered by novel dielectric elastomer actuators (DEA). The new DEA achieves high power density (1.2 kW/kg) and relatively high transduction efficiency (37%). We further incorporate this soft actuator into an aerial robot to demonstrate novel flight capabilities. This insect-scale aerial robot has a large lift-to-weight ratio (>2.2:1) and it achieves an ascending speed of 70 cm/s. In addition to demonstrating controlled hovering flight, it can recover from an in-flight collision and perform a somersault within 0.16 s. This work demonstrates that soft aerial robots can achieve insect-like flight capabilities absent in rigid-powered MAVs, thus showing the potential of a new class of hybrid soft-rigid robots.

Sulfurized polyacrylonitrile (SPAN) represents a unique class of cathode material for lithium sulfur (Li-S) batteries as it eradicates the polysulfides shuttling issue in carbonate-based electrolyte. However, due to the essential chemical S-linking and organic nature of SPAN, the active mass percentage and rate capability are two bottleneck issues preventing its ultimate deployment outside of laboratories. In the current work, aiming to endow both the charge conductivity and catalytic activity to SPAN for maximizing the redox kinetics of S conversion, a freestanding nanofibrous SPAN cathode embedding conductive CNTs and atomically dispersed Co centers is fabricated via multivariate electrospinning. While the CNTs enable dramatically enhancing the fiber conductivity and generating mesoscopic porosity for facilitating charge and mass transportation, the cross-linking of SPAN by Co-N4S motifs creates extra charge conduction pathways and further serves as the catalytic active sites for expediting redox S conversion. As a result, an extraordinary Li-SPAN performance is achieved with a high specific capacity up to 1856 mAh g−1@0.2 C, a superb rate capability up to 10 C, and an ultra-long battery life up to 1500 cycles@1 C. Consequently, our study here provides insights into the adoption of coordination chemistry to maximize the sulfur utilization by ensuring a more complete redox conversion from SPAN to Li2S, and vice versa.

Efficient electrocatalysts for hydrogen evolution reaction (HER) in alkaline solution are highly required for water splitting. Here we design an ultra-small PtOx nanoparticle with hybrid Pt chemical states on carbon nanotubes as highly efficient alkaline HER catalyst, which shows a low overpotential of 19.4 mV at 10 mA cm−2, a high mass activity of 5.56 A mgPt−1 at 0.1 V, and a stable durability for at least 20 h. The HER performance is better than that of the benchmark 20 wt% Pt/C while the Pt content in the catalyst is only about one tenth of that in Pt/C. It also represents one of the best catalysts ever reported for HER in alkaline solution. Synchrotron radiation X-ray absorption spectroscopy reveals that the efficient and stable alkaline HER performance can be attributed to the favorable design of hybrid chemical states of Pt with carbon nanotubes, which exhibits abundant surface Pt–O as active catalytic sites and forms stable Pt–C interfacial interaction to both anchor the NPs and improve the synergistic effect between catalyst and substrate.

Decision fusion is an effective way to resolve the conflict of diagnosis results. Aiming at the problem that Dempster-Shafer (DS) theory deals with the high conflict of evidence and produces wrong results, a decision fusion algorithm for fault diagnosis based on closeness and DS theory is proposed. Firstly, the relevant concepts of DS theory are introduced, and the normal distribution membership function is used as the evidence closeness. Secondly, the harmonic average is introduced, and the weight of each evidence is established according to the product of closeness of each evidence and its harmonic average. Thirdly, the weight of conflicting evidence is regularized, and the final decision fusion result is obtained by using the Dempster’s rule. Lastly, the simulation and application examples are designed. Simulation and application results show that the method can effectively reduce the impact of diagnostic information conflicts and improve the accuracy of decision fusion. What’s more, the method considers the overall average distribution of evidence in the identification framework, it can reduce evidence conflicts while preserving important diagnostic information.

In recent years, SiCp/Al metal matrix composites (MMCs) have attracted increasing attention from academia and industry. The size and distribution of particles in this material have an important effect on its actual performance. First, the geometric analysis of milling is performed. A two-dimensional micro finite element model consisting of hard SiC particles and a soft aluminum matrix is established to study the milling of SiCp/Al composites. In this paper, the cutting mechanism of SiCp/Al MMCs reinforced by a uniform distribution of particles of the same size and random distribution of non-equal size particles at the micron level was compared by using the finite element method and finite element model with a zero-thickness cohesive layer. The simulation results show that same-size particles are evenly distributed in the matrix, and good stress distribution and chip morphology can be obtained. In addition, the cutting force fluctuation is small, and the machining surface quality is good. The properties of Al-based SiC composites can be improved by evenly distributing same-size particles. Considering the particle size and distribution, the random model can more accurately simulate the chip, cutting force fluctuation of the workpiece, and workpiece surface damage than the uniform model. A good correlation between the research results and experimental results is shown in the literature.

CdS1−xSex nanomaterial with high optical and structural quality has been synthesized in ammonium water via a simple hydrothermal reaction. Structural and compositional analyses suggest that the CdS1−xSex synthesized in ammonium water (NH3·H2O) medium has better crystallinity and more Se content compared with that of CdS1−xSex synthesized in sodium hydroxide (NaOH) solution medium. Photoluminescence spectra show that the CdS1−xSex synthesized in NH3·H2O solution exhibits stronger red luminescence than that of the CdS1−xSex synthesized in NaOH solution. Transmission electron microscopy confirms that the size of the CdS1−xSex synthesized in NH3·H2O solution is obviously larger than that of the CdS1−xSex synthesized in NaOH solution. Basing on these results, the mechanism of CdS1−xSex formed in NH3·H2O was compared with that of the CdS1−xSex synthesized in NaOH solution media.

Millimeter-sized electrostatic film actuators, inspired by the efficient spatial arrangement of insect muscles, achieve a muscle-like power density (61 W kg−1) and enable robotic applications in which agility is needed in confined spaces. Like biological muscles, these actuators incorporate a hierarchical structure, in this case building from electrodes to arrays to laminates, and are composed primarily of flexible materials. So comprised, these actuators can be designed for a wide range of manipulation and locomotion tasks, similar to natural muscle, while being robust and compact. A typical actuator can achieve 85 mN of force with a 15 mm stroke, with a size of [Formula: see text] mm3 and mass of 92 mg. Two millimeter-sized robots, an ultra-thin earthworm-inspired robot and an intestinal-muscle-inspired endoscopic tool for tissue resection, demonstrate the utility of these actuators. The earthworm robot undertakes inspection tasks: the navigation of a 5 mm channel and a 19 mm square tube while carrying an on-board camera. The surgical tool, which conforms to the surface of the distal end of an endoscope, similar to the thin, smooth muscle that covers the intestine, completes tissue cutting and penetrating tasks. Beyond these devices, we anticipate widespread use of these actuators in soft robots, medical robots, wearable robots, and miniature autonomous systems.

In an integrated radar and communication system, the filter output of each pulse of the integrated radar and communication waveform usually has high sidelobe level (SLL) and signal-to-noise ratio (SNR) loss as well as serious range sidelobe modulation (RSM). To solve these problems, a joint weighted optimization (JWO) method is proposed to design the filters. In this method, the filters for different integrated radar and communication waveforms are designed by jointly minimizing the RSM, SNR loss, and SLL according to different practical demands. In terms of the RSM, SNR loss, and SLL, the performance of the designed filters is tunable by reasonably changing the weight factors. Finally, several numerical results are presented to verify the effectiveness of the proposed method.

The goal of sentence matching is to determine the semantic relation between two sentences, which is the basis of many downstream tasks in natural language processing, such as question answering and information retrieval. Recent studies using attention mechanism to align the elements of two sentences have shown promising results in capturing semantic similarity/relevance. Most existing methods mainly focus on the design of multi-layer attention network, however, some critical issues have not been dealt with well: 1) the higher attention layer is easily affected by error propagation because it relies on the alignment results of preceding attentions; 2) models have the risk of losing low-layer semantic features with the increase of network depth; and 3) the approach of capturing global matching information brings about large computing complexity for model training. To this end, we propose a Deep Bi-Directional Interaction Network (DBDIN) to solve these issues, which captures semantic relatedness from two directions and each direction employs multiple attention-based interaction units. To be specific, the attention of each interaction unit will repeatedly focus on the original sentence representation of another one for semantic alignment, which alleviates the error propagation problem by attending to a fixed semantic representation. Then we design deep fusion to aggregate and propagate attention information from low layers to high layers, which effectively retains low-layer semantic features for subsequential interactions. Moreover, we introduce a self-attention mechanism at last to enhance global matching information with smaller model complexity. We conduct experiments on natural language inference and paraphrase identification tasks with three benchmark datasets SNLI, SciTail and Quora. Experimental results demonstrate that our proposed method can achieve significant improvements over baseline systems without using any external knowledge. Additionally, we conduct interpretable study to disclose how our deep interaction network with attention can benefit sentence matching, which provides a reference for future model design. Ablation studies and visualization analyses further verify that our model can better capture interactive information between two sentences, and the proposed components are indeed able to help modeling semantic relation more precisely.

Nowadays, cluster tools tend to concurrently process multiple types of wafers with similar recipes in order to improve their utilization and flexibility in semiconductor manufacturing. Different wafer types may have different wafer flow patterns, resulting in that cluster tools are deadlock-prone. It is challenging to develop a general method to solve the deadlock problem of cluster tools without restriction on the wafer types. This work aims at solving such a challenging problem for single-arm cluster tools. To do so, a general Petri net model is developed for single-arm cluster tools. Given the wafer flow patterns of all wafer types to be processed in a single-arm cluster tool, such a Petri net model can be easily obtained by defining the relationship between places and transitions. Then, a control method by using self-loops is presented to prevent the model from deadlocks during the evolutions from the initial state to the final state. Furthermore, such a control method is proved to be optimal. Illustrative examples are given to verify the proposed method at last.

An artificial protocell model mimicking stimuli-triggered extracellular matrix formation is demonstrated based on the self-immobilization of coacervate microdroplets. Endogenous enzyme activity within the microdroplets results in the release of Ca2+ ions that trigger hydrogelation throughout the external environment, which in turn mechanically supports and chemically stabilizes the protocells.

State space techniques are one of the main approaches deployed for the analysis of concurrent systems. However, state space construction is stalled by a common phenomenon called the state explosion problem which makes it a tough task or even impossible when the state space computation demands prohibitive cost (time and memory). We limit general resource allocation systems (RASs) to a certain class whose state space can be hierarchically constructed, yet it comprises various enough types of real-world discrete event systems, such as automated manufacturing systems. This paper focuses on a class of RASs modeled with Petri nets (PNs), where, through pure algebraic operations, a novel method to compute the state spaces is proposed, which is motivated by the superposition property. Given a PN model of a system and a target resource configuration, we first propose a special initial marking called the initial basis marking and compute the corresponding reachability graph. Then, we increase the capacity of the resource places in an incremental way and generate the reachability graphs by taking advantage of the PN structure and the previously computed reachability graph until the capacity function of resources reaches the target resource configuration. A complete enumeration of reachable states can be obtained by a recursive scheme. Experimental studies also demonstrate the efficiency of the proposed approach in terms of computational cost and its high-potential to cope with the state-explosion problem.

In the intelligent transportation system, it is an efficient way for the intelligent vehicle to use the integrated radar and communications system (IRCS) to obtain the range and velocity estimations of other vehicles and simultaneously communicate with other facilities, such as vehicles and base stations. In the IRCS, the transmitted waveform is the orthogonal frequency division multiplexing (OFDM) integrated radar and communications waveform that contains communications information. Due to the existence of communications information, the traditional range and velocity estimation methods in radar can not be directly utilized in the IRCS. Moreover, to improve the resolution of range and velocity estimations, the signal bandwidth and coherent processing interval (CPI) are usually required to be increased, which will result in the increase of system cost and the reduction of update rate. To solve these problems, a auto-paired super-resolution range and velocity estimation method is proposed by using the OFDM integrated radar and communications waveform. First, the communications information in the received signals is compensated. Then, the frequency smoothing is performed to reduce the correlation between the echoes reflected by different targets. Finally, the auto-paired super-resolution range and velocity estimations are obtained by exploiting the translational invariance structure of the underlying signal model in both frequency and pulse domains. Furthermore, Cramer-Rao bounds (CRBs) on range and velocity estimations with the OFDM integrated radar and communications waveform are derived. Several numerical examples are presented to verify the effectiveness of the proposed method.

A series of brucite structure-doped samples with different amounts of Er(III) have been synthesized by hydrothermal method. Various characterizations have been employed to study the composition, structure, and photoluminescence. Results suggested that the Er3+-doped samples with Mg(II)/Er(III) molar ratio of 0.99/0.01~0.8/0.2 exhibited typical brucite structure, and the sample with Mg(II)/Er(III) molar ratio of 0.7/0.3 represented layered double hydroxide (LDH) structure. Moreover, the crystallinity of the Er3+-doped samples tended to be poorer with the increasing amount of Er(III). Various strong emissions ranging from red to infrared emissions appear in the down-conversion photoluminescent spectra of the Er3+-doped samples excited by different wavelengths, and the energy of emission peaks has linear dependence on the energy of the excitation wavelength at 450~565 nm. Photoluminescent decay spectra show that the decaying behavior of the red and infrared emissions for the Er3+-doped samples are different from that of the previous Er3+-doped materials, and the lifetimes of the red and infrared emissions are less than that of the other Er3+-doped materials. Based on the present results as well as relevant references, the energy transition diagrams of the Er(III) incorporated in the samples were described, and the transition mechanisms of the red and infrared emissions have been proposed. The new Er3+-doped materials may be a latent fluorescent material applied in NIR detector or biomedical imaging in view of its strong red and infrared emissions as well as less toxicity for organisms.

Many insects demonstrate remarkable locomotive capabilities on inclined or even inverted surfaces. Achieving inverted locomotion is a challenge for legged insect-scale robots because repeated attachment and detachment to a surface usually requires the design of special climbing gaits, adhesion mechanisms, sensing, and feedback control. In this study, we propose a novel adhesion method that leverages capillary and lubrication effects to achieve simultaneous adhesion and sliding. We design a 47 mg adhesion pad and install it on a 1.4 g insect-scale quadrupedal robot to demonstrate locomotion on inverted and inclined surfaces. On an inverted acrylic surface, the robot's climbing and turning speeds are 0.3 cm/s and 23.6 °/s, respectively. Further, the robot can climb a 30° inclined acrylic surface at 0.04 cm/s. This light-weight, passively stable, and versatile adhesion design is suitable for insect-scale robots with limited sensing, actuation, and control capabilities.

A series of Er3+ -doped magnesium aluminium layered double hydroxides (Er3+ -doped, MgAl-LDHs) with different Mg2+ /(Al3+ +Er3+ ) molar ratios were synthesized using the hydrothermal method. Compositional and structural analyses suggest that the Er3+ -doped MgAl-LDHs kept a hexagonal structure while the Mg2+ /(Al3+ +Er3+ ) molar ratio was at 1.0-4.1. The downconverted emission spectra of the Er3+ -doped MgAl-LDHs showed a red emission at 650 nm and strong infrared emissions at 720, 780, and 850 nm. These infrared emissions were hardly observed in previous downconverted emission spectra of Er3+ -doped materials. In the analysis of the Er3+ energy levels and in relevant published literature, the energy transfer diagram for Er3+ -doped in MgAl-LDHs is described, and infrared emissions at 720, 780, and 850 nm may be attributed to 4 F7/2 →4 I13/2 , 2 H11/2 →4 I13/2 , and 4 S3/2 →4 I13/2 transitions of Er3+ , respectively. Er3+ -doped MgAl-LDHs could have potential application as marking and targeting agents in the processes for drug delivery in consideration of the strong near-infrared Er3+ emissions, as well as the special layered structure of MgAl-LDH.

In recent years, with the explosion of information on the Internet, there has been a large amount of data produced, and analyzing these data is useful and has been widely employed in real world applications. Since data labeling is costly, lots of research has focused on how to efficiently label data through semi-supervised learning. Among the methods, graph and hypergraph based label propagation algorithms have been a widely used method. However, traditional hypergraph learning methods may suffer from their high computational cost. In this paper, we propose a Hypergraph Label Propagation Network (HLPN) which combines hypergraph-based label propagation and deep neural networks in order to optimize the feature embedding for optimal hypergraph learning through an end-to-end architecture. The proposed method is more effective and also efficient for data labeling compared with traditional hypergraph learning methods. We verify the effectiveness of our proposed HLPN method on a real-world microblog dataset gathered from Sina Weibo. Experiments demonstrate that the proposed method can significantly outperform the state-of-the-art methods and alternative approaches.

7,8-dihydroxyflavone (7,8-DHF), a tyrosine kinase B (TrkB) receptor agonist, can mimick physiological actions of brain-derived neurotrophic factor (BDNF) to attenuate neurogenic disease. However, its use as a functional food, is limited by its low-water solubility, chemical instability, and poor bioavailability. The purpose of this work is to fabricate stable 7,8-DHF loaded zein/lactoferrin (LF) composite nanoparticles (zein/LF-DHF) to overcome these challenges. Results showed that mean particle size of zein/LF nanoparticles was about 74 nm with low polydispersity index (0.200) and turbidity (0.300) values. Zein/LF nanoparticles had good stability against pH (3.0-9.0), ionic strengths (0-500 mM NaCl at neutral pH) and long-term storage. Zein/LF nanoparticles showed spherical structures formed by hydrogen bonding and hydrophobic interactions, however, LF changed surface morphology of zein nanoparticles as observed by scanning electron microscope. X-ray diffraction indicated 7,8-DHF was presented in an amorphous state inside zein/LF nanoparticles. Most importantly, zein/LF-DHF had good redispersibility, and increased the encapsulation efficiency, chemical stability, water solubility and bioaccessibility of 7,8-DHF. Collectively, zein/LF nanoparticles are promising delivery systems for 7,8-DHF in functional foods.

The reaction of 2-hydroxyquinoxaline ligand with silver nitrate in anhydrous methanol leaded to the formation of complex [Ag·(L)·(NO3)] (1) (L = 2-hydroxyquinoxaline). This complex is characterized...

Petri nets are an important and popular tool to model and analyze deadlocks in flexible manufacturing systems. The state space of a Petri net model can be divided into two disjoint parts: a live-zone and a dead-zone. Reachability graph analysis plays an important role in the modeling and control of Petri nets. Most existing studies have to fully enumerate the reachable markings of a Petri net to obtain the first-met bad markings (FBMs), which exacerbates the computational overheads. In this paper, a computationally efficient method to find dead markings in Petri nets is presented. We first introduce an algorithm to find dead markings by solving an integer linear programming problem. Then, the set of markings in the dead-zone is calculated, including the set of dead markings and the set of bad markings. Then we can find all the FBMs. By using a vector covering approach, the minimal covered set of FBMs is computed. The proposed approach can obtain the dead markings and FBMs by searching only a part of a reachability graph. Finally, examples are provided to demonstrate the proposed method.

Distributed Raman amplifier (DRA) has been widely studied in recent decades because of its low noise figure and flexible gain. In this paper, we present a novel scheme of DRA with broadband amplified spontaneous emission(ASE) source as pump instead of discrete pump lasers. The broadband pump is optimized by machine learning based inverse design and shaped by programmable waveshaper, so as to realize the ultrafine, dynamic and arbitrary gain spectrum shaping of Raman amplification. For the target of flat gain spectrum, the maximum gain flatness of 0.1086 dB is realized based on the simulation results. For the target of arbitrary gain spectrum, we demonstrate four gain profiles with maximum root mean square error (RMSE) of 0.074 dB. To further measure the performance of arbitrary gain spectrum optimization, the probability density functions (PDF) of RMSE and Errormax are presented. Meanwhile, the numeral relationship between the bands of broadband pump and signal is also explored. Furthermore, this work has great application potential to compensate the gain distortion or dynamic change caused by other devices in communication systems.

In order to create an irrigation scheduling plan for use in large-area citrus orchards, an environmental information collection system of citrus orchards was established based on the Internet of Things (IoT). With the environmental information data, deep bidirectional long short-term memory (Bid-LSTM) networks are proposed to improve soil moisture (SM) and soil electrical conductivity (SEC) predictions, providing a meaningful reference for the irrigation and fertilization of citrus orchards. The IoT system contains SM, SEC, air temperature and humidity, wind speed, and precipitation sensors, while the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were calculated to evaluate the performance of the models. The performance of the deep Bid-LSTM model was compared with a multi-layer neural network (MLNN). The results for the performance criteria reveal that the proposed deep Bid-LSTM networks perform better than the MLNN model, according to many of the evaluation indicators of this study.

This article addresses the forbidden state problem in discrete-event systems (DESs) modeled with Petri nets. Given a control specification, we first decide the sets of forbidden and admissible markings. Then, the minimal mask set of first-met forbidden markings (FFMs) and the minimal root set of admissible markings are computed by marking mask that is implemented using a class of special places in a plant, called competitive places. Marking mask can effectively filtrate the markings to be processed such that the two obtained sets are in general much smaller than the sets of originally specified forbidden and admissible markings, respectively. Monitors computed by place invariants are used to forbid the forbidden markings. It is shown that a maximally permissive (optimal) supervisor can be computed if it exists. Integer linear programming is used to optimize the structure of a supervisor. The minimal mask set of FFMs and root set of admissible markings efficiently reduce the computational overhead because of much fewer constraints and variables in the formulated programming problem. The developed methodology is illustrated by parameterized examples.

Flying insects capable of navigating in highly cluttered natural environments can withstand in-flight collisions because of the combination of their low inertia1 and the resilience of their wings2, exoskeletons1 and muscles. Current insect-scale (less than ten centimetres long and weighing less than five grams) aerial robots3–6 use rigid microscale actuators, which are typically fragile under external impact. Biomimetic artificial muscles7–10 that are capable of large deformation offer a promising alternative for actuation because they can endure the stresses caused by such impacts. However, existing soft actuators11–13 have not yet demonstrated sufficient power density to achieve lift-off, and their actuation nonlinearity and limited bandwidth create further challenges for achieving closed-loop (driven by an input control signal that is adjusted based on sensory feedback) flight control. Here we develop heavier-than-air aerial robots powered by soft artificial muscles that demonstrate open-loop (driven by a predetermined signal without feedback), passively stable (upright during flight) ascending flight as well as closed-loop, hovering flight. The robots are driven by multi-layered dielectric elastomer actuators that weigh 100 milligrams each and have a resonance frequency of 500 hertz and power density of 600 watts per kilogram. To increase the mechanical power output of the actuator and to demonstrate flight control, we present ways to overcome challenges unique to soft actuators, such as nonlinear transduction and dynamic buckling. These robots can sense and withstand collisions with surrounding obstacles and can recover from in-flight collisions by exploiting material robustness and vehicle passive stability. We also fly two micro-aerial vehicles simultaneously in a cluttered environment. They collide with the wall and each other without suffering damage. These robots rely on offboard amplifiers and an external motion-capture system to provide power to the dielectric elastomer actuators and to control their flight. Our work demonstrates how soft actuators can achieve sufficient power density and bandwidth to enable controlled flight, illustrating the potential of developing next-generation agile soft robots. Heavier-than-air insect-scale aerial robots powered by soft artificial muscles can hover and also recover from in-flight collisions, illustrating the potential for developing next-generation agile soft robots.

In this work, we proposed a long-term tracking strategy to deal with the occlusion, out-of-plane rotation, and the confusing non-target object. Our tracking system is composed of two parts, the CA-CF tracker, an efficient correlation method for short-term tracking, and the SVM-based re-detector, which prevents the CA tracker from degradation. When the tracker works with confidence, the CA-CF module ensures an accurate tracking result and the SVM updates accordingly. When the response maps fluctuate heavily, the SVM switches to work as a re-detector and the tracker will be initialized. We also introduced to adopt both the maximum response criterion and the APCE criterion to judge the performance of the tracker in time. By evaluating our algorithm on the OTB benchmark datasets, we proposed to analyze the result affected by the parameters of our CA-CF-SVM strategy. The experimental results show that our method has a significant improvement than the state-of-the-art methods for the long-term tracking both in accuracy and robustness.

In this study, a flower-like Cu2FeSnS4 (CFTS) nanomaterial was applied to the activation of persulfate (PS) for the degradation of bisphenol A (BPA) in model industrial wastewater. The efficiency of PS decomposition by CFTS, and the mechanism of contaminant degradation by SO4 − were investigated. The results revealed that CFTS catalyzes PS decomposition more efficiently than the monometallic Cu/Fe/Sn sulfides and exhibits activity over a wide pH range. Electron spin resonance spectroscopy, X-ray photoelectron spectroscopy, and free radical quenching experiments revealed a tandem synergistic effect between Cu, Fe, and Sn in their quaternary chalcogenide systems upon PS activation. The intrinsic electron transfer between Cu, Fe and Sn, especially the Fe(II)* species formed upon the complexation of Fe(II) by S on the surface of the CFTS, overcomes the inhibition of the M(n+1)+/Mn+ redox cycle. CFTS shows promise as a catalyst for wastewater treatment.

By considering 92 renewable energy listed enterprises in China between 2007 and 2016 as sample, this paper constructs a panel threshold effect model to investigate the threshold effect of government subsidies on renewable energy investment and further explores the effects and differences regarding government subsidy types and enterprise size on the threshold effect. The results show that, first, government subsidies have a positive threshold effect on renewable energy investment of China. When energy consumption intensity and bank credit are greater and the level of economic development is less than the threshold value, the contribution of government subsidies to renewable energy investment is significantly enhanced. Second, monetary subsidies and tax incentive policies can promote renewable energy investment, but the effect of tax incentives is more significant. Third, government subsidies are the main force supporting the development of medium-, small-, and micro-sized renewable energy enterprises. On the basis of these results, the Chinese government should strengthen policy support in those areas with high economic costs of energy transition, encourage the investment in renewable energy in large-sized enterprises, play a policy-guiding role, and focus on strengthening subsidies for medium-, small-, and micro-sized enterprises.

ZnS/CdS1-xSex nancomposite has been obtained by a two-step process. Namely CdS1-xSex nanoparticles are prepared by one-pot hydrothermal method; then the CdS1-xSex is dispersed in Zn(NH3)42+ solution, and dropping with Na2S solution, resulting in the ZnS/CdS1-xSex shell/core nanocomposite. Various techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), thermogravimetry and differential scanning calorimetry (TG-DSC), and photoluminescence (PL) are used to characterize the structure, composition, sizes, and luminescent property of the ZnS/CdS1-xSex nancomposite. Results reveal that the ZnS/CdS1-xSex nanocomposite consists of cubic ZnS shell and hexagonal CdS1-xSex core, and exhibits thermally decomposing behavior of shell/core structure. Moreover, the sizes of the ZnS/CdS1-xSex nanocomposite are about 10–15 nm. Further a strong broad emission ranging from red to blue light presents in the PL spectrum of the ZnS/CdS1-xSex nanocomposite. This broad emission makes the ZnS/CdS1-xSex nanocomposite potential application in full color or white light fields.

Heterogeneous photocatalytic oxidation technology is currently a technology with the potential to solve environmental pollution and energy shortages. The key to this technology is to find and design efficient photocatalysts. Here, a series of inorganic coordination polymer quantum sheets (ICPQS)@graphene oxide (GO) composite photocatalysts are synthesized by adding GO to the synthesis process of ICPQS: {[CuII(H2O)4][CuI4(CN)6]}n. These composite photocatalysts were characterized by X-ray diffraction, X-ray photoelectron spectroscopy, cyclic voltammetry, scanning electron microscopy, transmission electron microscopy, Zeta potential, and N2 adsorption/desorption isotherms. The photocatalytic degradation of methylene blue showed that the activity of ICPQS@GO composite photocatalysts is better than that of ICPQS. Among ICPQS@GO composite photocatalysts, the 10.6% ICPQS@GO composite photocatalyst has the best activity, which can reach 3.3 mg/(L min) at pH 3. This method of loading low–specific surface area photocatalysts onto GO to improve photocatalytic performance indicates the direction for the synthesis of highly efficient photocatalysts.

Hyperspectral imaging has great achievements in agriculture, astronomy, surveillance, and so on. However, the inherent low spatial resolution of hyperspectral imaging, unfortunately, limits its more widespread applications. Recently, hyperspectral image (HSI) super resolution addresses this problem by fusing a low spatial resolution HSI (LR-HSI) with a high spatial resolution multispectral image (HR-MSI), but most of these methods did not consider real-time restoration of high spatial resolution HSI. In this study, the authors propose a fast HSI super-resolution method which fills this blank. Specifically, they model the hyperspectral super resolution as a linear regression problem according to the fact that the imaging process is a linear transform and the inverse of this transform can be approximately estimated, as the spectra of a typical scene lie in a very low-dimensional space. To further exploit the low-dimensional nature of the spectra, they divide the HR-MSI and LR-HSI into several patches and learn the inverse transform patch-by-patch. Experiments on several public datasets show that their method approximates state-of-the-art methods in accuracy, but is several orders of magnitude faster than all of them. Furthermore, they provide an efficient C language implementation of their methods, which can meet the real-time request.

Kyphoplasty is a novel approach treating osteoporotic vertebral compression fractures and poly(methyl methacrylate) (PMMA) is the most common bone cement used for kyphoplasty. Cement leaked into an intervertebral disc (IVD) usually causes problems. Herein, we explored the effects of PMMA particles on the biological behavior of nucleus pulposus cells and proved the inhibiting effect of PMMA particles in a dose-dependent manner. Besides, PMMA particles led to cell cycle arrest and the abnormal expression of several apoptosis associated gene. Futhermore, we found PMMA particles upregulated the expression of p-Mst1/2, p-Lats1, and p-YAP, whereas they downregulated the expression of YAP and CTGF, suggesting the activing effect on the Hippo/YAP/CTGF signaling pathway. By overexpression of YAP, PMMA-induced nucleus pulposus cell degeneration was reversed. In vivo study also showed that recombinant CTGF protein could alleviate PMMA-induced IVD degeneration in rats. To sum up, our study revealed the role of PMMA particles in inducing IVD degeneration by downregulating CTGF through Hippo/YAP pathway.

CdS1−xSex/CdS nanocomposite was prepared via a two-step approach. Namely the CdS1−xSex nanoparticles were prepared using Cd(NH3)42+, Na2S, and Se as reagents by hydrothermal method, and then the CdS1−xSex nanoparticles were dispersed in Cd(NH3)42+ solution and Na2S solution dropped in, leading to the CdS1−xSex/CdS nanocomposite. TEM measurement reveals that the sizes of the CdS1−xSex nanoparticles and CdS1−xSex/CdS nanocomposite are about 10 and 15 nm, respectively. The CdS1−xSex/CdS nanocomposite has similar structure as that of the CdS1−xSex, but exhibits different optical property compared with that of the single CdS and CdS1−xSex. The present work would provide a mild, convenient, and inexpensive process for preparing II/VI group semiconductor nanocomposite.

Based on the hypothesis that all vehicles on roads are floating vehicles equipped with vehicular terminals and upload their traffic information to the traffic control center in real time, many traffic flow guidance algorithms are proposed in order to improve the road capacity in Intelligent Transportation System. Based on this complete information scenario, the traffic control center can obtain all traffic information on roads and compute the guidance metrics according to the traffic flow guidance algorithm. The vehicle therefore can select the route with the guidance. However, due to the cost and technology limits, in short time, some vehicles cannot be equipped with vehicular terminals, which are non-floating vehicles and cannot upload their traffic information to the traffic control center. In this incomplete information scenario, the guidance effect will be affected by non-floating vehicles. In this paper, the method of estimating non-floating vehicles’ driving information according to floating vehicles’ information is introduced. With the estimation method, a new traffic flow guidance algorithm, Estimated Weighted Vehicle Density Feedback Strategy based on Weighted Vehicle Density Feedback Strategy (WVDFS) is proposed. In the simulation, the guidance effect of the new algorithm is performed based on the two-route scenario, which is the simplified model of traffic network. NaSch model is also used as the vehicle mobility model, which can simulate the vehicle motion. The simulation results show that non-floating vehicles have great negative influence on the performance of WVDFS, and the applicability of our algorithm is validated to have better performance in incomplete information scenarios. Our algorithm can provide solutions for current traffic flow guidance with incomplete information scenarios.

Based on the brake light (BL) model, Knospe et al. proposed a symmetric two-lane cellular automaton (BL-STCA) model, which could reproduce various empirically observed two-lane phenomena. In real traffic, the effect of brake light on the lane-changing behavior cannot be ignored. Therefore, BL-STCA model is interesting. However, there are two problems with BL-STCA model, too strong exchange of vehicles between lanes and unreasonable phenomenon in some special scenarios, such as a broken-down vehicle parked on one lane due to traffic accident. In order to solve the problems, we introduce the dynamic lane-changing probability and proposed an improved BL-STCA model with modification of lane-changing rules. The simulation results show as below. (1) Our new model effectively solves the above two problems of BL-STCA model. In addition, the lane-changing frequency is consistent with the real traffic data, which means the validity of our new model. (2) Compared with single-lane scenario, the lane-changing behaviors in two-lane scenario can effectively suppress the emergence of wide moving jam. (3) From the microcosmic level, the lane-changing behaviors can well explain the moving blank phenomenon within wide moving jam.

Petri nets are an important and popular tool to model and analyze deadlocks in automated manufacturing systems. The state space of a Petri net model can be divided into two disjoint parts: a live-zone and a dead-zone. A first-met bad marking (FBM) is a marking in the dead-zone, representing the very first entry from the live-zone to the dead-zone, and the calculation of FBMs to a large extent contributes to the complexity of designing optimal liveness-enforcing supervisors. Most existing studies have to fully enumerate the reachable markings of a Petri net model to obtain the FBMs, which exacerbates the computational overheads. This paper first explores a variation mechanism of calculating FBMs with respect to the resource capacity in a class of S3PR (Systems of Simple Sequential Processes with Resources) from the structural analysis perspective, which contains a ξ-resource. More generally, for the class of S3PR with an η-resource as defined in this paper, the FBMs can be calculated in an algebraic way by a customized structural analysis technique without enumerating all the reachable markings. Finally, the variation mechanism of calculating FBMs is revealed for these considered classes of Petri net models. Examples are given to demonstrate the proposed method.

Eu3+-doped hydrocalumites (Eu3+-doped CaAl-LDH) were prepared in an ethanol/water media. Various techniques, including inductively coupled plasma atomic emission spectroscopy (ICP-AES), X-ray diffraction (XRD), and fluorescent spectra (FL), etc., were employed to study the structure, composition, and fluorescence of samples. Compositional analyses reveal that the contents of all the elements calculated from the chemical formula are close to that of the actual found from measurement. XRD results suggest that the samples with different molar ratios of Ca2+/(Al3++Eu3+) present typical layered structure. Moreover, all the samples exhibit monoclinic structure. Fluorescent spectra show strong red emissions ascribed to 5D0→7FJ (J = 1, 2) transitions of Eu3+ ions for all the Eu3+-doped hydrocalumites, revealing that the hydrocalumite is favorable for the emission of Eu3+. The Eu3+-doped hydrocalumite may be a latent fluorescent material applied in biological or medical fields in consideration of the biocompatibility of Ca2+ ions and fluorescent property of Eu3+ ions.

The energy consumption and CO2 emissions of the transportation sector in China have increased greatly in recent years, accompanied by the growing regional disparities. Considering undesirable output and environmental impact factors, a four-stage DEA (data envelope analysis) combined with NDDF model (non-radical directional distance function) is adopted in this paper to calculate the energy efficiency and eliminate the environmental impacts of Chinese transportation sector. In this paper, five environmental factors are considered, including GDP per capita, consumption level, urbanization level, economic openness level, and transport infrastructure. The empirical results on the panel data for 30 provinces of China from 2005 to 2016 show that the energy efficiency of the transportation sector in China decreases from Eastern to Western region. After the adjustment of environmental factors, energy efficiency still shows a decreasing trend from Eastern to Western region, while energy efficiency increases more in Eastern region and less in Central region and Western region. The potential of energy efficiency improvement for some Central and Western provinces is relatively high. Some policy suggestions are proposed to improve the energy efficiency of Chinese transportation sector.

Providing an intelligent campus guiding application is a good example of smart campus. Our smartphone APP focused on monitor the drivers' illegal behaviors, such as illegal parking for safety and management issues. For illegal parking detection, the motion mode recognition algorithm is focused on to detect the walking mode after drivers park their cars. When distinguishing low-speed driving mode and walking mode, the accuracy rate of Decision Tree algorithm is 67.03%, and that of our algorithm is 89.91%. When the walking mode is detected, the car location will be computed and if the location is not within the parking lot, the illegal parking occurs and the illegal parking alert will be sent to the APP. The application will play an important role in smart campus to improve the traffic safety and traffic management in campus.

Motors have a wide range of applications in various aspects such as automotive, medical, industrial production, etc. Commonly used motors can generally be divided into DC motors and AC motors.Permanent Magnet Synchronous Motor (PMSM) is a type of AC motors with strong starting ability, high peak efficiency and high reliability, and with greater application value. This paper mainly studies the control technology of PMSM, and designs a set of motor controller and its protection system based on FPGA. First, based on the development and simulation platform of FPGA, the important algorithms of the motor controller and protection system are simulated and analysed. Then the system is built in the actual environment. Through actual testing, the speed control of the motor can be accurately achieved and various fault protection and instructions of the motor controller can be realized. The system is based on the development platform of FPGA, which with fast running speed, high flexibility, short development cycle, high resource utilization rate, and strong portability.

Layered double hydroxides (LDHs) have been recognized as potent electrocatalysts for oxygen evolution reaction (OER), but are lacking in hydrogen evolution reaction (HER) activities due to the sluggish kinetics of water dissociation in alkaline medium. Herein, aiming to simultaneously bolster the HER and OER kinetics, a metal-organic framework (MOF) mediated topotactic transformation tactic is deployed to fabricate holey ternary CoFeNi LDHs on nickel foam, exposing polygonal mesopores with atomistic edge steps and lattice defects. The optimized catalyst requires only an external voltage of 1.49 V to afford the water splitting current density of 10 mA cm-2 apart from the superb electrolytic stability, far surpassing the benchmark Pt/C||RuO2 couple. More importantly, mechanistic investigations utilizing advanced spectroscopies in conjunction with density function theory (DFT) understandings unravel while the synergetic effect among under-coordinated metal centers lowers the energy barrier of water dissociation, Fe-doping enables further modulating the d-band density of states (DOS) of Co and Ni in favor of intermediates binding, thereby promoting the intrinsic HER activity. Operando Raman studies reveal negligible structural change of the LDHs during the HER process, whereas for OER the active sites can quickly turn into oxyhydroxides in the presence of lattice defects and under-coordinated metal centers.

V2X (Vehicle-to-X) network is an important support to automated vehicle. However, for automated vehicle safety, if the security of V2X network cannot be guaranteed, automated vehicles may not obtain required correct information, which is a fatal problem. In V2X network, black hole and gray hole attacks are critical threats of network availability and may prevent legitimate users from receiving packets. In this review, the definition, classification and impact of the attacks on V2X network are presented, and the detection and prevention technologies are classified and analyzed, respectively. Finally, three open issues and future trends are proposed, which are considering specific characters of V2X network, paying attention to artificial intelligent technologies in detection and utilizing edge computing architecture as the local trusted environment in detection and prevention.

In the field of traffic-light signal identification, most technologies are based on image recognition, which have high requirements for image quality. Such technologies will be invalid when encountering the Line-of-Sight interference or bad weather problems. Based on road vehicle cooperative technology, a traffic-light signal recognition algorithm is proposed, which can identify the corresponding traffic-light information through the multiple traffic-lights at the intersection. In the algorithm, the auxiliary point is used to improve the fault tolerance of positioning drift. The traffic-light signal recognition system is designed and implemented based on the algorithm. The system is tested in our campus, and results show that the recognition time is less than 2 seconds, which is consistent with the sliding window configuration and can guarantee the security because the maximum speed of urban road vehicles is less than 70km/h.

In this paper, a beampattern design method is proposed for the multi-input-multi-output (MIMO) dual-function radar-communication (DFRC) system with an interleaved array. To make full use of the whole aperture, first, the transmit array is partitioned into two interleaved subarrays, one is for radar and the other is for downlink communications. Then, both the radar waveform and communication beamformers are optimized by combining the null-space projection (NSP) method and cyclic approach (CA) to perform the coexistence of MIMO radar and downlink communications. Besides, the communication beampattern can be utilized to improve the detection performance. Finally, several numerical results are given to show the effectiveness of the proposed method.

For the coexistent radar and communications system, the radar can receive the communications-path signal, i.e., the target return due to the transmission from the communications. The communications-path signal can be exploited to improve the radar detection performance when it has high signal to noise ratio (SNR). To show this, the statistical signal model at the radar receiver is developed, and then the generalized likelihood ratio test (GLRT) for the coexistent radar and communications system is derived in this paper. The derived GLRT has constant false alarm rate (CFAR). Finally, several numerical examples are presented to verify the performance gain by exploiting the communications-path signal with high SNR.

To reduce the consumption of spectrum and hardware resource, two methods are proposed to design the integrated waveform that can satisfy the requirements for both radar and communication. First, a linear superposition (LS) method is proposed to design the transmit waveforms of a uniform linear array (ULA). In the far field of the ULA, the designed transmit waveforms can simultaneously synthesize the desired radar and communication waveforms in different directions. However, the designed waveforms usually have high peak-to-average power ratio (PAPR). Then, an alternative projection (AP) method is proposed to reduce the PAPR of the transmit waveforms. The AP method can devise the waveforms with constant modulus. Both the proposed methods can perform radar and communication function in different directions. Finally, several simulation results are given to verify the effectiveness of the proposed methods.

We propose a machine learning based approach to design few-mode DRAs by using neural networks to optimize the pump wavelengths, powers and mode content in order to obtain flat gain spectrum with low mode-dependent gain (MDG). Based on the proposed intelligent inverse design method, amplification optimization for the random fiber laser based two-mode DRA can be achieved with gain flatness of 1.0 dB and MDG of 0.6 dB at 14.5 dB on-off gain level. For backward pumping four-mode DRA, gain flatness of 0.46 dB and MDG of 0.3 dB can be achieved at 12.5 dB on-off gain.

The design and construction of mutual interaction models between artificial microsystems and living cells have the potential to open a wide range of novel applications in biomedical and biomimetic technologies. In this study, an artificial form of invasion-defense mutual interactions is established in a community of glucose oxidase (GOx)-containing liquid coacervate microdroplets and living cells, which interact via enzyme-mediated reactive oxygen species (ROS) damage. The enzyme-containing coacervate microdroplets, formed via liquid-liquid phase separation, act as invader protocells to electrostatically bind with the host HepG2 cell, resulting in assimilation. Subsequently, the glucose oxidation in the liquid coacervates initiates the generation of H2 O2 , which serves as an ROS resource to block cell proliferation. As a defense strategy, introduction of catalase (CAT) into the host cells is exploited to resist the ROS damage. CAT-mediated decomposition of H2 O2 leads to the ROS scavenging and results in the recovery of cell viability. The results obtained in the current study highlight the remarkable opportunities for the development of mutual interacting communities on the interface of artificial protocells/living cells. They also provide a new approach for engineering cellular behaviors through exploiting artificial nonliving microsystems.

It is very important to understand the mechanism of surface flashover characteristics of the insulator materials in vacuum in order to avoid the surface discharge of sensitive components. There are two models with dominant roles and highly reorganization in academic circle. One is secondary electron emission avalanche model (SEEA) and the other is electron triggered polarity relaxation model (EPTR). However these models are still not understood clearly and there is no model which can completely explain all the phenomenon of surface flashover in vacuum, especially the reverse surface flashover phenomenon. In this paper, DC surface flashover experiments of insulator are studied in vacuum to reveal the reverse surface flashover mechanism. Results showed that there is a phenomenon in which a reverse flashover current sometimes occurs in the surface flashover process. The reverse flashover current pulse may occur in both the initial pre-flashover phase and the flashover breakdown phase, that is, when the flashover current pulse is positive, the surface flashover voltage is negative. The experiments results can be explained as follows. The electrons injection phenomenon near the cathode and holes injection phenomenon simultaneously near the anode are formed in the high electric field intensity at the triple junction (cathode end, vacuum and dielectric surface, CTJ). The electron-hole pairs migrate to the anode and cathode in the applied electric field intensity, respectively. Some of the carriers in the migration are captured by the trap center, forming the positive and negative charge trapping center. The region between the trap center and the electrodes will form an additive electric field. The negative charge trapping center weaken the local region electric field between cathode and negative charge, forming a reverse electric field to limit further injection of charge. At the same time, the trapping center enhanced the additive electric field between the anode and the trapping center. The region of the space charge electric field formed by the center of the negative charge is completely opposite to the applied electric field. When the negative charge trapping center value is sufficiently deep, the reverse additive electric field intensity will reach a critical of flashover breakdown value in the region. The partial discharge usually cannot occur due to the weakening of the high external electric field intensity. However, the applied electric field intensity of the insulator is instantaneously reduced during the surface flashover breakdown. At the same time, the electrons in the trapping center are not detrapping, so the electric field intensity of the reverse in the cathode and trapping center will rise rapidly. As a consequence of the electric field intensity reaching the critical breakdown value, a reverse partial discharge is formed immediately. Unlike the usual case, the electrons will escape from trapping center and transfer towards cathode under the action of reverse electric field intensity in the process, forming a reverse surface flashover current pulse. The reverse flashover current pulse typically occurs at a time when the applied electric field intensity value is low in the flashover process. It is suggest that the traps will play a critical role in affecting the reverse surface flashover performance under DC voltage in vacuum.

The aspect-based sentiment analysis (ABSA) task remains to be a long-standing challenge, which aims to extract the aspect term and then identify its sentiment orientation.In previous approaches, the explicit syntactic structure of a sentence, which reflects the syntax properties of natural language and hence is intuitively crucial for aspect term extraction and sentiment recognition, is typically neglected or insufficiently modeled. In this paper, we thus propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn). Additionally, we design a simple yet effective message-passing mechanism to ensure that our model learns from multiple related tasks in a multi-task learning framework. Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach, which significantly outperforms existing state-of-the-art methods. Besides, we achieve further improvements by using BERT as an additional feature extractor., Comment: Code: https://github.com/XL2248/DREGCN

Machine translation quality estimation (Quality Estimation, QE) aims to evaluate the quality of machine translation automatically without golden reference. QE can be implemented on different granularities, thus to give an estimation for different aspects of machines translation output. In this paper, we propose an effective method to utilize pretrained language models to improve the performance of QE. Our model combines two popular pretrained models, which are Bert and XLM, to create a very strong baseline for both sentence-level and word-level QE. We also propose a simple yet effective strategy, ensemble distillation, to further improve the accuracy of QE system. Ensemble distillation can integrate different knowledge from multiple models into one model, and strengthen each single model by a large margin. We evaluate our system on CCMT2019 Chinese-English and English-Chinese QE dataset, which contains word-level and sentence-level subtasks. Experiment results show our model surpasses previous models to a large extend, demonstrating the effectiveness of our proposed method.