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Recently, collaborative representation classification (CRC) has attracted extensive interest for hyperspectral images (HSIs) classification. However, for collaborative representation with Tikhonov (CRT), a testing sample is collaboratively represented by training samples from all the classes, which may result in high computational cost. In this article, we select the first $k$ class training samples that are nearest to the testing sample for representation, namely, $k$ -nearest class CRT (KNCCRT) algorithm. In order to improve the performance of KNCCRT for HSI classification, the idea of random subspace-based KNCCRT ensemble framework is proposed. KNCCRT is adopted as base classifier and random subspace (RS) contributes to diversity by selecting feature randomly. Moreover, to further increase the classification accuracy, shape-adaptive (SA) neighborhood constraint is utilized in RS ensemble framework to incorporate spatial information. Experimental results on three real hyperspectral data sets demonstrate the effectiveness of the proposed methods for HSI classification. The combination of KNCCRT and RS framework provides a reliable accuracy for HSI classification.

The combination of pervasive edge computing and blockchain technologies opens up significant possibilities for industrial Internet of Things (IIoT) applications, but there are several critical limitations regarding efficient storage and rapid response for large-scale low-delay IIoT scenarios. To address these limitations, in this article we propose a hierarchical edge-cloud blockchain called LayerChain . Specifically, to promote scalability, we design a layered structure to hierarchically store the blockchain data in multiple distributed clouds and edge nodes. Next, we propose a node classification method to accommodate differences between the edge nodes when deploying the blockchain. Moreover, to mitigate lengthy delays during block propagation, we propose a tree-based clustering algorithm where blocks are propagated through different clusters with a compressed tree depth. Simulation results show that our LayerChain efficiently reduces the system's resource requirements and block propagation time, making it well-suited for large-scale low-delay IIoT applications.

This paper proposes a new type of energy absorber with combined shrinking circular tubes for a high-speed train. An impact experiment is conducted to investigate the dynamic crushing performance of this energy absorber with combined shrinking circular tubes. The results show that the combined tubes experience steady dynamic shrinking deformation. Finite element (FE) models of the energy absorber are then developed, and the dynamic crushing forces are in good agreement with the impact test. A theoretical solution for the dynamic shrinking crushing load is derived. Based on the validated FE models, the effects of the friction coefficient, wall thickness and die radius on the dynamic crushing force and energy absorption are investigated. An increase in the wall thickness leads to a substantial growth in the maximum crushing force (Fmax) and specific energy absorption (SEA), but the growth rate of Fmax is much larger than that of the SEA as the wall thickness increases. In addition, comparing theoretical and FE results demonstrates that predictions of the dynamic steady-state forces for the combined shrinking circular tubes with different friction coefficients, wall thicknesses (t) and die radius (Rdie) are satisfactory. Finally, to improve the crashworthiness of the expanding circular tubes, Sobol’ sensitivity analysis is employed to analyze the effects of the design parameters (t and Rdie) on the objective responses (SEA and Fmax) using the Kriging model. A Pareto front of double optimization objective SEA and Fmax was obtained after being optimized by multiobjective particle swarm optimization (MOPSO). The results show that SEA and Fmax are positively correlated, and a balance between the SEA and Fmax was obtained at optimal point C (SEA = 13.09 kJ/kg, Fmax = 581.11 kN).

Corporate social responsibility (CSR) has become increasingly important. From the perspective of operations management, traditional non-CSR-compliant operations are less costly than CSR compliant operations but may be subject to the risk of being exposed to the public by third-party organizations such as Non-Governmental Organizations (NGOs) through external scrutiny. This exposure can negatively affect firms’ market share when customers are concerned about firms’ CSR compliance. This paper studies firms’ endogenous CSR compliance strategies, i.e., the incentive to adopt CSR compliant operations. We first consider a single firm's CSR compliance strategies, and then, we extend the analysis to the case of competition. We analyze how exogenous parameters, including the risk of exposure and cost premium, determine equilibrium CSR compliance strategies. We find that CSR compliant operations will be implemented either when the exposure risk is sufficiently high or when the cost premium is sufficiently low. We also discuss how competition affects firms’ CSR compliance strategies and whether firms perform better with CSR compliant operations in equilibrium. Our results show that, by adopting CSR compliant operations, firms will engage in a win-win equilibrium if the external risk is high or if the cost premium is low. Besides, we also conduct the analysis under Bertrand (price) competition. Based on these results, we provide managerial insights into when CSR compliant operations should be adopted in practice and how such adoption affects firms’ performance. Our results also imply that the practice of sustainability requires firms to consider both the external risk of exposure and cost premium.

Lanthanide (Ln) metal–organic frameworks (Ln-MOFs) have attracted attention in anti-counterfeiting and white light-emitting diodes (WLEDs) due to their high quantum efficiency, long lifetimes, and characteristic linear spectrum. However, Ln-MOFs have some disadvantages, such as harsh luminescence modulation, low transparency, and poor film formation. In this study, Ln-MOF (Ln = Eu and/or Tb)-modified TEMPO-oxidized cellulose nanofibrils were employed for preparing transparent nanopapers and luminescence modulation. These luminescent nanopapers achieved an excellent light emission color tuning from green, yellow, and red under 365 nm excitation, while 254 nm radiation on these nanopapers only emitted characteristic pink light. Thus, the luminescent nanopapers combined color-tuning and photo-switching properties through the control of Eu³⁺/Tb³⁺ molar ratio and excitation wavelength (λEₓ at 365 and 254 nm), respectively, which greatly improved them for anti-counterfeiting applications. In addition, Ln-MOFs endowed these nanopapers with excellent optical haze (>90%), such that softening of white light from WLEDs was realized in 254 nm-driven UV-LED chips, corresponding white light with good color quality [Commission International de I’Eclairage coordinates, correlated color temperature, and color-rendering index of (0.34, 0.36), 5536 K and 86, respectively].

Pavement maintenance and rehabilitation (M&R) records are important as they provide documentation that M&R treatment is being performed and completed appropriately. Moreover, the development of pavement performance models relies heavily on the quality of the condition data collected and on the M&R records. However, the history of pavement M&R activities is often missing or unavailable to highway agencies for many reasons. Without accurate M&R records, it is difficult to determine if a condition change between two consecutive inspections is the result of M&R intervention, deterioration, or measurement errors. In this paper, we employed deep-learning networks of a convolutional neural network (CNN) model, a long short-term memory (LSTM) model, and a CNN-LSTM combination model to automatically detect if an M&R treatment was applied to a pavement section during a given time period. Unlike conventional analysis methods so far followed, deep-learning techniques do not require any feature extraction. The maximum accuracy obtained for test data is 87.5% using CNN-LSTM.

We use a newsvendor model to investigate equilibrium contracting strategies and their impact on the members in two competing supply chains that are subject to inventory inaccuracy. The suppliers are Stackelberg leaders and can choose either a wholesale-price contract or consignment contract. As a result, we analyze three cases: the wholesale price contract case, consignment contract case, and hybrid contract case. We first analytically derive the optimal outcomes of each case and then analyze the equilibrium behaviors of suppliers and retailers under both symmetric and asymmetric settings. Our results show that the consignment contract is no longer the dominant strategy for dominant suppliers when inventory inaccuracy is considered, even though it is considered as the channel-coordinating contract format in the traditional supply chain. Specifically, in the symmetric case, the wholesale price contract is the equilibrium contracting strategy when inventory inaccuracy is below a certain threshold. In the asymmetric case, the hybrid scenario occurs when the high-cost supplier chooses the wholesale price contract, and the low-cost supplier chooses the consignment contract when inventory inaccuracy is less than a certain value. Furthermore, our results demonstrate that the threshold point of inventory inaccuracy is interrelated with the channel cost-sharing rate and level of chain-to-chain competition between supply chains.

Separation or purification is one of the difficult problems in the petrochemical industry. To help solve the difficulty of separation or purification for C2H2/CO2 and C2Hn/CH4 in the chemical industry, we synthesized a new metal-organic framework (MOF), [Ni(dpip)]·2.5DMF·H2O (1), by a bipyridyl-substituted isophthalic acid ligand. The MOF includes two types of one-dimensional (1D) tubular channels with different sizes and porous environments. The unique tubular channels lead to not only remarkable gas sorption capacity of C2H4, C2H2, and CO2, but also good selectivity for C2H2/CH4, C2H2/CH4, CO2/CH4, and C2H2/CO2, as demonstrated by single-component sorption isotherm results, ideal adsorbed solution theory calculations, and dynamic breakthrough curves. Grand canonical Monte Carlo (GCMC) simulation reveals preferential adsorption sites in the MOF for CO2, C2H2, and C2H4. The MOF also exhibits an obvious size-selective absorption effect on vapor molecules.

The chiral deformation of a conventional helical actuator is unidirectional in each activation (stimuli-on) process and in each recovery (stimuli-off) process, which means that the twisting direction remains the same regardless of the nature of deformation (either winding or unwinding or helix-inversion). In these cases, a complete stimuli-switching cycle is necessary for the realization of a bidirectional helical deformation process that comprises a reversal of twisting direction. Here, we achieved bidirectional and even multi-directional helical shape evolution in each stimuli-on (or off) process. The strategy relies on, on one hand, asymmetrically crosslinking and/or elongating the two sides of a photocrosslinkable liquid crystal polymer (LCP) monolith, and on the other hand, tailoring the uniaxially aligned polymer strip to form an off-axis mesogen orientation. We obtained a wide variety of helical actuators capable of reversing their twisting directions by only cooling (or heating). The mechanisms of these various chiral deformation-reversal behaviours and the two observed reversals of the twisting direction during cooling have been elucidated.

Developing highly efficient and low-cost electrocatalysts for the hydrogen evolution reaction (HER) in neutral media is crucial but still a significant challenge on account of the sluggish multistep reaction kinetics involved. Theoretically, constructing a metal/metal hydroxide interface with a metal hydroxide acting as a water dissociation promotor and a metal as the subsequent hydrogen adsorption site is expected to accelerate the HER kinetics in neutral media. We found that the intercalation of tungstate into Ni(OH)2 can tune the electronic structure of the Ni/Ni(OH)2 interface, decrease the energetic barrier for the Volmer step, and thus optimize the HER performance of Ni/Ni(OH)2 hybrid. Herein, a heterostructured Ni/Ni(OH)2 nanosheet array on Cu foam is designed and constructed via a simple hydrothermal and subsequent plasma reduction strategy. The HER performance can be further enhanced by assembling tungstate-intercalated Ni/Ni(OH)2 hybrid nanosheets on Cu2O nanotube arrays, exhibiting a low overpotential (39 mV @ 10 mA cm−2) and excellent stability (48 h).

Transition metal phosphides (TMPs) have been reported as efficient pre-catalysts for the oxygen evolution reaction (OER) in alkaline media. In situ generated metal oxyhydroxides on the surface of TMPs serve as real active sites. However, the reconstruction of most of the reported TMPs is incomplete and the active components cannot be fully used. Herein, hollow nanostructured Ni5P2/FeP4 nanoboxes (NiFeP NBs) are designed and synthesized as pre-catalysts. During the OER, the NiFeP NBs deeply reconstruct into low-crystalline and ultrathin NiOOH/FeOOH nanosheet assembled nanoboxes (NiOOH/FeOOH NBs). In situ Raman spectroscopy and ex situ characterization studies provide evidence that the hollow nanostructure facilitates the deep reconstruction of NiFeP NBs. Benefiting from the hierarchical hollow structure, the abundant interface between NiOOH and FeOOH, and plentiful defects, the reconstructed NiOOH/FeOOH NBs exhibit superior OER activity and excellent stability. Density functional theory (DFT) calculations reveal that the Fe–Ni dual sites in the NiOOH/FeOOH interface may be the possible active sites.

A new strategy incorporating three sequential treatments—selective molecular pre-masking, surface modification, and unmasking—is proposed for pore-size-dependent control of the surface properties of porous carbon materials. The method was experimentally validated using wide-pore-selective hydrophilization. n-Nonane was physically adsorbed for pre-masking, followed by room-temperature ozonation for surface modification. This process was used to introduce surface functional groups into wide pores, while leaving narrow pores intact. This study facilitates the development of new porous carbon materials with different surface properties that depend on pore size. This flexible strategy can be employed in conjunction with various molecular masking agents and surface-modification methods.

The continuous development and wide application of CT in medical practice have raised public concern over the associated radiation dose to the patient. However, reducing the radiation dose may result in increasing the noise and artifacts, which may adversely interfere with the judgment and belief of radiologists. Therefore, we propose a low-dose CT denoising model based on multiscale parallel convolution neural network to improve the visual effect. Residual learning is utilized to reduce the difficulty of network learning, and batch normalization is adopted to solve the problem of performance degradation due to the increase in neural network layers. Specifically, we introduce the dilated convolution to expand the receptive field by inserting weights of zero in the standard convolution kernel, while not increasing the extra parameters. Furthermore, the multiscale parallel method is utilized to extract multiscale detail features from lung images. Compared to the traditional methods such as Wiener filter, NLM, and models based on CNN, e.g., SCNN, DnCNN, our extensive experimental results demonstrate that our proposed model (CT-ReCNN) can not only reduce the LDCT lung images noise level, but also retain more exact information as well.

This paper proposes an event-triggered reinforcement learning (RL) control strategy to stabilize the quadrotor unmanned aerial vehicle (UAV) with actuator saturation. As the quadrotor UAV equips with a complex dynamic is difficult to be model accurately, a model free reinforcement learning scheme is designed. Due to the practical limitation of actuators, the end of controller is constrained with a bounded function. In order to reduce the calculation consumption for the onboard computer, an event-triggered mechanism is developed, which only update the controller when the triggered condition is satisfied. The proposed controller is implemented with two neural networks which are called critic and actor. Some advanced RL technologies are utilized for speeding up the train process, e.g. off-policy training, experience replay, etc. The stability of closed-loop system is proved by the Lyapunov analysis. The simulation results including a stability task and a tracking task verify the theoretical analysis, in which we find the updating frequency of controller is decreased greatly.

This brief addresses the fixed-time event/self-triggered leader-follower consensus problems for networked multi-agent systems subject to nonlinear dynamics. First, we present an event-triggered control strategy to achieve the fixed-time consensus, and a new measurement error is designed to avoid Zeno behavior. Then, two new self-triggered control strategies are presented to avoid continuous triggering condition monitoring. Moreover, under the proposed self-triggered control strategies, a strictly positive minimal triggering interval of each follower is given to exclude Zeno behavior. Compared with the existing fixed-time event-triggered results, we propose two new self-triggered control strategies, and the nonlinear term is more general. Finally, the performances of the consensus tracking algorithms are illustrated by a simulation example.

By using the generating tree technique and the obstinate kernel method, Kim and Lin confirmed a conjecture due to Martinez and Savage which asserts that inversion sequences e = ( e 1 , e 2 , … , e n ) containing no three indices i j k such that e i ≥ e j , e j ≥ e k and e i > e k are counted by Baxter numbers. In this paper, we provide a bijective proof of this conjecture via an intermediate structure of open partition diagrams, in answer to a problem posed by Beaton-Bouvel-Guerrini-Rinaldi. Moreover, we show that two new classes of pattern-avoiding inversion sequences are also counted by Baxter numbers.

Two new different metal–organic frameworks (MOFs) [Ba(L1)(H2O)2]n·nH2O (MOF 1) and [Ba(L2)(H2O)2]n·0.5nDMF·0.5nH2O (MOF 2) were yielded by the assembly of oxygen-friendly Ba(II) ions and two similar linkers, namely 2-(imidazol-1-yl)terephthalic acid (H2L1) and 2-(1H-1,2,4-triazol-1-yl) terephthalic acid (H2L2). Single-crystal X-ray diffractions (XRD) indicate that MOF 1 is a new three-dimensional (3D) stacking dense network formed by the one-dimensional (1D) rod-shaped chains and L1 linkers, whereas MOF 2 presents a 3D nanotube porous framework with cylindrical tunnels based on the 1D loop chains as the secondary building units (SBUs) by replacing the imidazole group in H2L1 with the triazole group in H2L2. As a result, MOF 2 has a higher density of active sites and Lewis acid sites in the porous surface of nanotube than MOF 1. Thereby, the CO2 capture and separation capacity of MOF 2 is great higher than that of CH4 at 298 K.

In this paper, we study the practical fixed-time event-triggered time-varying formation tracking problem of leader-follower multi-agent systems with multi-dimensional dynamics. Fixed-time event-triggered control schemes with continuous communication and intermittent communication are developed, respectively. Continuous communication and measurement are avoided, and computation cost is reduced greatly in the latter scheme. And the settling time is to be specified regardless of initial states of agents. Meanwhile, tracking errors are adjustable as desired with expected settling time. It is demonstrated that time-varying formation tracking can be achieved under the two proposed control schemes and Zeno behavior can be excluded. Finally, numerical examples are provided to illustrate the effectiveness of the proposed control strategies.

Crowdsourcing is a novel distributed problem-solving mechanism that can provide the collection and share of marine data in the Internet of Things for the smart ocean. Nevertheless, the privacy leakage issue caused by multirole information interaction in crowdsourcing brings a serious challenge to the smart ocean. In this article, we propose a crowdsourcing privacy protection scheme based on multiauthority ciphertext-policy attribute-based encryption to enhance privacy protection in the data sharing environment. In this scheme, we design an independent key component distribution approach through multiple authorities, which could effectively disperse the security responsibility from the crowdsourcing platform. Then, we present the idea of partial decryption on the platform to reduce the computing cost of mobile users and prevent the platform from snooping on users’ data. Moreover, we put forward an efficient attribute revocation mechanism and a task search function in the scheme to achieve dynamic on-demand services while ensuring the forward and backward security of tasks. Theoretical analysis proves the correctness of both decryption and keywords matching, and the security of each involved entity. The simulation results show that our proposed scheme achieves a significant improvement in reducing time consumption compared with several related schemes.

Mobile crowdsourcing is a promising strategy for trusted data collection in Internet-of-Things (IoT) applications. In this article, we propose a new fog-blockchain distributed approach for crowdsourcing reputation management to prevent user’s privacy leakage, malicious users’ participation, and reputation tampering in wireless IoT systems. To protect the user’s privacy, we design a cross-layer privacy protection model to separate the user’s identity and tasks flexibly by means of a hierarchical structure based on fog computing. Moreover, considering the multiconstraint requirement of crowdsourcing tasks, we present a multifactor reputation evaluation method to accurately identify malicious users. Furthermore, to solve the multi-identity problem of users on multiple fog nodes, we propose an adaptive fog-blockchain reputation storage method, which efficiently reduces the system resource consumption by analyzing the adaptive classification of fog nodes. Exhaustive experimental simulation results validate the security and efficiency of our proposed reputation management system.

Simultaneous attainments of high conductivity and superior catalysis are major challenges for amorphous electrocatalysts in carbon dioxide electroreduction at high overpotential. In this study, one protocol is first demonstrated to drive the shell amorphization of nanoporous Ag-Bi (a-NPSB) catalyst with the spatially interconnected ligament during the initial stage of CO2ER. This newborn a-NPSB bestows the outstanding catalysis, evidenced by a Faradaic efficiency of 88.4% for formate production at -1.15 V vs RHE, specific current density of 21.2 mA cm-2, and mass specific current density of 321 mA mg-1. The unique catalysis is considered as the collective contribution of the conductive ligament internally and amorphous Bi2O3 shell with about 3.2 nm thickness externally. Simultaneous obtaining of the conductivity of inner metals and catalytic activity of the amorphous shell will pave a new avenue for designing a robust electrode during electrochemical reaction.

This work explores mechanical properties, structural evolution, and mechanism of creep-life enhancement for widely used P9 heat-resistant steel. The 17-year-on-site used P9 alloy exhibit a higher tensile strength and a smaller elongation than the new P9 alloy from room temperature to 700 °C. The P9 alloy also displays a typical ductile feature with a significantly necking profile. The P9 alloy shows phase transformation sequences of α-Fe (bcc) → A c 1 ∼ 858 ° C α + γ -Fe (bcc + fcc) → A c 3 ∼ 894 ° C γ-Fe (fcc) upon heating and γ-Fe (fcc) → M s ∼ 352 ° C martensite (bct) → M f ∼ 300 ° C martensite (bct) upon cooling. The new P9-alloy tube mainly contains ∼73.5% ferrite phase (α-Fe) and ∼26.5% carbide M3C. However, the used P9-alloy tube shows four crystalline phases including ∼45.9% ferrite, ∼14.5% martensite, ∼37.5% cementite (M3C) and ∼2.7% carbide M23C6. The creep test indicates that the used P9-alloy tube has a longer creep-life (or better anti-creep ability) than the new tube. Activation energies of atomic diffusion for the new and used tubes are respectively 252.45 and 345.87 kJ/mol, indicating a decreased diffusion capability in the used tube. This work suggests that martensite laths, lath boundaries, and precipitates (such as carbides) play important roles to inhibit creep-deformation in the P9-alloy steel.

With the rapid increase in service requirements driven by Internet of Things (IoT) networks, mobile crowdsourcing has become a compelling paradigm that can efficiently solve complex tasks in the physical world. Nevertheless, we found that most IoT tasks have constraints on deadline, location, and resource consumption, which limit the application of crowdsourcing platforms in the IoT networks. In this article, we innovatively propose a reliable fog-based temporal–spatial crowdsourcing for serving the above tasks. In this scenario, the key point is to achieve the best match of the attributes among tasks, fog nodes, and workers. As the bridge of the other two parts, fog nodes determine the orientation of tasks. Therefore, we present a temporal–spatial task allocation (TS-TA) scheme in the fog layer, aiming to make task results more reliable. In this scheme, we build a temporal–spatial attribute learning model based on the user behaviors. Then, we use the users’ interest attribute matching model to identify the candidate fog nodes that satisfy the requirements of temporal–spatial tasks. We choose the fog nodes with low spatial correlation that is benefit to defense the attack on the nodes in the intensive area. Meanwhile, we assign the redundancy nodes for intrusion response through replacing the attacked/negative node. Both theoretical and real-topology simulation results validate that the proposed scheme can get better performance in system resource consumption and system robustness compared with other benchmark schemes.

Electrochemical carbon dioxide reduction meditated by metallic catalysts suffers from restricted selectivity and competition from hydrogen evolution, which sensitively depends on ambiguous contributions of alloying and strain state in bimetallic catalysts. Herein, nanoporous Au-Sn (NPAS) containing trace tin solute in Au lattices is delicately designed to convince real strain effect, while eliminating other undesirable factors, such as alloying, crystal facets and surface composition. Compared with nanoporous gold (NPG), the NPAS with a solute strain of ∼2.2 % enables more efficient CO2-to−CO conversion, with an efficiency as high as 92 % at -0.85 V versus reversible hydrogen electrode (vs. RHE), and the high activity can retain for more than 8 h. The combination of HRTEM and surface valence band photoemission spectra reveals that the tensile strain on the surface of 3D nanoporous structure promotes the catalytic activity by shifting up the d-band center and strengthening the adsorption of key intermediate *COOH. A small amount of Sn solute in the nanoporous alloy can prevent ligament coarsening effectively and improve the electrochemical stability.

The oxidation behavior of a newly designed Co–Ni-based alloy with varied addition of Ti (1.4 and 2.1 wt.%, hereafter referred as 1.4Ti and 2.1Ti alloys) was explored in air at 900 °C. It is found that an outer oxide layer composed of CoO, an intermediate oxide layer composed of intermittent TiO2 plus continuous Cr, W-rich oxides, and an inner oxidized region composed of Al2O3 were formed on the surface of 1.4Ti alloy, whereas the oxide structure of 2.1Ti alloy was composed of CoO as the outer oxide layer, continuous TiO2, Cr2O3, and WO3 as intermediate oxide layer, and Al2O3 as the inner oxidized region. With increasing Ti content, continuous TiO2 films formed in the intermediate oxide layer, which would lead to the formation of compact Cr2O3 and improved oxidation resistance after 100 h oxidation at 900 °C. These observations indicated that Ti addition can improve the high-temperature oxidation resistance of Co–Ni-based alloys.

Novel main-chain liquid crystalline Diels-Alder dynamic networks (LCDANs) were prepared that exhibit unprecedented ease for actuator programming and reprocessing compared to existing liquid crystalline network (LCN) systems. Following cooling from 125 °C, LCDANs are deformed with aligned mesogens self-locked at room temperature by slowly formed Diels-Alder (DA) bonds, which allows for the formation of solid 3D actuators capable of reversible shape change, and strip walker and wheel-capable light-driven locomotion upon either thermally or optically induced order-disorder phase transition. Any actuator can readily be erased at 125 °C and reprogrammed into a new one under ambient conditions. Moreover, LCDANs can be processed directly from melt (for example, fiber drawing) and from solution (for example, casting tubular actuators), which cannot be achieved with LCNs using exchangeable covalent bonds. The combined attributes of LCDANs offer significant progress toward developing easily programmable/processable LCN actuators.

This paper investigates the synchronization problem of the Kuramoto-oscillator network with non-identical oscillators. The fixed-time event-triggered synchronization control strategies are developed for phase agreement and frequency synchronization under both continuous and intermittent communication. With the developed fixed-time controller, the synchronization can be achieved within a pre-defined time for any initial phase of each oscillator. The event-triggered mechanism avoids continuous controller update and data transmission, which significantly saves the computation and communication resources. Furthermore, theoretical analysis shows that the fixed-time convergence can be guaranteed and the Zeno behavior is avoided by the proposed methods. The numerical simulations of each situation also verify the effectiveness of the proposed synchronization control strategies.

The explosion pressure and minimum explosible concentration (MEC) properties of metal sulfide ore dust clouds are valuable for the prevention and control of metal sulfide ore dust explosions. In this study, a 20 L explosion sphere vessel was used to investigate the effect of sulfur content, particle size, and concentration on the explosion pressure and minimum explosible concentration of metal sulfide ore dust clouds. Four samples with different sulfur contents were selected (30%–40%, 20%–30%, 10%–20%, and 0%–10%). Before and after the explosion, samples were tested by X-ray diffraction. The results indicate that the metal sulfide ore dust is explosive dust with St1 grade explosion pressure. With an increase in concentration, the maximum explosion pressure increased at first and then decreased. With an increase in sulfide content, the explosion pressure of metal sulfide ore dust increased, while the minimum explosible concentration decreased. As particle size decreased, the MEC also decreased. The sulfur content, particle size, and concentration of metal sulfide ore dust were the main factors affecting the explosion hazard.

The development of appropriate catalysts with relatively low cost, good selectivity and excellent stability is one of the major issues in electrochemical reduction of CO2. In this work, an efficient electrocatalyst was fabricated via ultra-small Co3O4 nanoparticles encapsulated within the tip of carbon nanotubes, denoted as Co/CNTs. Benefiting from the synergistic effects of highly active Co3O4 nanoparticles and well-graphitized carbon nanotubes, Co/CNTs exhibited remarkable performance in CO2 electroreduction. In a conventional H-type cell, CO with a 90% faradaic efficiency and 20.6 mA cm−2 partial current density was obtained at only −0.7 VRHE cathode potential with 40 hour stability. Upon switching to a gas-diffusion device, the CO partial current density could reach as high as 232.6 mA cm−2 with >80% faradaic efficiency, which might be even comparable to that of state-of-the-art CO2 electrocatalysts. Our work could also provide a new strategy for developing non-noble metal catalysts for CO2 electroreduction.

Extrusion temperature can significantly influence grain size, the shape of second phase particles and the dislocation distribution of Mg alloys. In this study, the effects of extrusion temperatures ranging from 250 to 400 °C on the microstructure, mechanical properties and corrosion resistance of a Mg-Al-Ca alloy with 1.61 wt.% Al and 1.76 wt.% Ca (Mg-1.61Al-1.76Ca) were investigated. The results showed that the size of grains and phases increased with an increase in extrusion temperature; the yield strength and ultimate tensile strength values decreased with an increase in temperature, while the fracture elongation increased initially (up to 300 °C) and decreased thereafter with an increase in temperature. Under test, the extruded alloy exhibited some evidence of corrosion at 250 °C, while extrusion at 300 °C demonstrated better corrosion resistance. Thus, the 300 °C extruded alloy possessed the optimum comprehensive performance because of the relatively small grains, fine second phase particles and fewer dislocations.

Carbon dioxide electroreduction provides a useful source of carbon monoxide, but comparatively few catalysts could be sustained at current densities of industry level. Herein, we construct a high-yield, flexible and self-supported single-atom nickel-decorated porous carbon membrane catalyst. This membrane possesses interconnected nanofibers and hierarchical pores, affording abundant effective nickel single atoms that participate in carbon dioxide reduction. Moreover, the excellent mechanical strength and well-distributed nickel atoms of this membrane combines gas-diffusion and catalyst layers into one architecture. This integrated membrane could be directly used as a gas diffusion electrode to establish an extremely stable three-phase interface for high-performance carbon dioxide electroreduction, producing carbon monoxide with a 308.4 mA cm−2 partial current density and 88% Faradaic efficiency for up to 120 h. We hope this work will provide guidance for the design and application of carbon dioxide electro-catalysts at the potential industrial scale., Here the authors deploy Ni single atom-decorated carbon membranes as integrated gas diffusion electrodes to construct an extremely stable three-phase interface for CO2 electroreduction, producing CO with a partial current density of 308.4 mA cm–2 and a Faradaic efficiency of 88% for up to 120 h.

Utilizing the self-assembly strategy between the paddle-wheel Cu2(COO)4 cluster and a terephthalic acid ligand modified by a triazolyl group, namely H2tztp = 2-(1H-1,2,4-triazol-1-yl)terephthalic acid, a porous MOF, [Cu0.5(tztp)0.5]·0.5DMA (1), has been successfully designed and built. 1 is a three-dimensional self-interpenerated framework with a (3,6)-connected rtl topology, and contains hydrophilic and hydrophobic channels decorated with rich uncoordinated triazolyl N atoms and benzene rings from tztp ligands, respectively. The unique porous environment leads not only to high gas adsorption amounts of CO2, C2H6, C2H4 and C2H2 but also to a good separation ability of C2 hydrocarbons over CH4, CO2 over CH4, and C2H2 over CO2 in the MOF. The specific adsorption sites of CO2 and C2 hydrocarbons were explored by a molecular simulation. The breakthrough experiments demonstrated excellent dynamic separations for CO2 and CH4, C2 hydrocarbons and CH4 as well as C2H2 over CO2. The MOF also revealed excellent adsorption and separation ability for CH3OH over other bulkier alcohol molecules.

Herein, a new porous Zn(II)-based metal-organic framework (MOF 1) has been prepared, the structure of which featured a twofold entangled motif based on two typical secondary building units (SBUs). The gas sorption studies indicated that MOF 1 may be explored as a useful platform to encapsulate metallic nanoparticles. Then the Au@1 composite has been prepared via a facile incorporation method without extra reducing agents. The Au@1 composite has been fully characterized by HRTEM, SEM-EDX, PXRD, gas sorption, XPS, ICP, etc. Catalytic experiments showed that the Au@1 composite had a perfect catalytic performance in CO2 fixation for epoxides with different substituents under mild conditions.

In this article, a new generation of magnet-free circulators with high performance is proposed. Circulators are crucial devices in modern communication systems due to their ability to enable full-duplexing and double the spectral efficiency directly in the physical layer of the radio-frequency (RF) front end. Traditionally, the Lorentz reciprocity is broken by applying the magnetic bias to ferrite materials; therefore, conventional circulators are bulky and expensive. In this article, this problem is addressed by replacing the magnetic bias with periodic spatiotemporal modulation. Compared to previous works, the proposed circulator is constructed using surface acoustic wave (SAW) filters instead of transmission lines (TLs), which reduces the modulation frequency by at least a factor of 20 and ensures ultra-low power consumption and high linearity. The miniaturized high quality ( ${Q}$ ) factor SAW filters also lead to a low-loss nonreciprocal band with strong isolation (IX) and broad bandwidth (BW) on a chip scale, therefore addressing such limitations in previous magnet-free demonstrations. Furthermore, compared to the conventional differential circuit configuration, a novel quad configuration is developed, which doubles the intermodulation-free BW.

In this paper, we study the fixed-time event-triggered consensus problem of the uncertain nonlinear multiagent systems. Two fixed-time event-triggered consensus controllers are proposed. In contrast to finite-time results, the convergence time of fixed-time results is independent of initial conditions. Furthermore, continuous communications can be avoided both in the update of controllers and in the triggering condition monitoring. It is proved that there is no Zeno behavior under the fixed-time event-triggered consensus control strategies. The availability of the control algorithms is verified by numerical simulations.

Previous evidence has shown that the increased expression of aurora kinase is closely related to melanoma progression and is an important therapeutic target in melanoma. Danusertib is an inhibitor of aurora kinase, and recent studies have shown that danusertib treatment induces autophagy in several types of cancer. Interestingly, autophagy plays a dual function in cancer as a pro-survival and anti-survival factor. In this study, we investigated the role of danusertib on the induction of autophagy in melanoma and determined the impact of autophagy induction on its anticancer activity against melanoma. Our results showed that danusertib can significantly inhibit melanoma growth by inducing cell cycle arrest and apoptosis. In addition, we demonstrated that danusertib treatment significantly inhibits the oncogenic Akt/mTOR signaling pathway and induces autophagy in melanoma cells. Furthermore, we identified that the inhibition of autophagy can enhance the inhibitory effects of danusertib on melanoma growth. Thus, the induction of autophagy by danusertib appears to be a survival mechanism in melanoma cells that may counteract its anticancer effects. These findings suggest a novel strategy to enhance the anticancer efficacy of danusertib in melanoma by blocking autophagy.

All-inorganic CsPbX3 (X = Cl, Br, and I) nanocrystals (NCs) are emerging as attractive semiconductor materials because of their outstanding optical properties. The low resistance of CsPbX3 NCs to light, heat, oxygen, and water has been recognized as a major obstacle to their practical applications. Here, we demonstrate that the stability of CsPbX3 NC films can be dramatically enhanced by Ar plasma treatment. It is revealed that plasma irradiation can induce ligand polymerization in the NC films if the ligands contain unsaturated carbon bonds. The ligand polymerization leads to encapsulation of the NCs in the ligand polymers. Because of the precise localization of the in situ ligand polymerization under plasma irradiation and the high NC content in the films without extra additives, the polymerized area can be precisely defined down to several micrometers. This enables easy fabrication of high-resolution NC pixels for next generation displays.

A series of new alkaline earth-metal organic frameworks (AE-MOFs), i.e. {[Mg2(μ6-L)(μ2-OH2)(H2O)4]·DMF}n (1), [Ca2(μ10-L)(EtOH)]n (2), [Sr(μ6-H2L)(MeOH)]n (3) and {[Ba1.5(μ5-H2L)(μ4-H3L)(H2O)3]·2(H2O)}n(4) were synthesized under solvothermal reaction by employing a symmetrical V-shaped multicarboxylic acid ligand H4L (H4L = 5,5′-oxydiisophthalic acid), resulting in versatile coordination modes as well as various topologies. 1 forms a uninodal 4-connected (66)-dia 3D topology based on dinuclear Mg2(COO)2(μ2-OH2) clusters. 2 displays an unprecedented 3-nodal (5,5,10)-connected 3D net with (424·621)(48·62)2 topology based on 1D [Ca(COO)]n chains and dinuclear Ca2(COO)2 clusters. 3 indicates a uninodal 6-connected 3D net with (410·65)-sma topology based on 1D [Sr(COO)]n chains. 4 features a new 4-nodal (4,5,6,6)-connected net with (43·63)2(46·64)2(46·68·8)(46·69)2 topology based on two types of 1D chains. 1-4 show solid-state fluorescent properties at ambient temperature. Especially, 1 and 2 have multiresponsive recognition of Fe3+ and Pb2+ ions, Fe3+ and Cu2+ ions, respectively, while 3 and 4 show single selective detection for Fe3+ ion, signifying that they could be potential fluorescent sensors for specific heavy metal ions in water system. The sensing mechanism has also been discussed in detail.

Summary The buccal mucosa (BM) is a critical first line of defense in terrestrial animals. To gain further insights into the evolutionary origins and primordial roles of BM in teleosts here we show that rainbow trout, a teleost fish, contains a diffuse mucosal associated lymphoid tissue (MALT) within its buccal cavity. Upon parasite infection, a fish immunoglobulin specialized in mucosal immunity (sIgT) was induced to a high degree, and parasite-specific sIgT responses were mainly detected in the buccal mucus. Moreover, we show that the trout buccal microbiota is prevalently coated with sIgT. Overall our findings revealed that the MALT is present in the BM of a non-tetrapod species. As fish IgT and mucus-producing cells are evolutionarily unrelated to mammalian IgA and salivary glands, respectively, our findings indicate that mucosal immune responses in the BM of teleost fish and tetrapods evolved through a process of convergent evolution., Graphical Abstract, Highlights • A mucosal associated lymphoid tissue is identified in teleost buccal cavity • Local immune responses can be induced in teleost buccal mucosa • Teleost IgT plays important roles in defending against buccal pathogen • Microbiota in teleost buccal mucosa is prevalently coated with secreted IgT, Biological Sciences; Immunology; Omics; Transcriptomics

In recommender systems, modeling user-item behaviors is essential for user representation learning. Existing sequential recommenders consider the sequential correlations between historically interacted items for capturing users' historical preferences. However, since users' preferences are by nature time-evolving and diversified, solely modeling the historical preference (without being aware of the time-evolving trends of preferences) can be inferior for recommending complementary or fresh items and thus hurt the effectiveness of recommender systems. In this paper, we bridge the gap between the past preference and potential future preference by proposing the future-aware diverse trends (FAT) framework. By future-aware, for each inspected user, we construct the future sequences from other similar users, which comprise of behaviors that happen after the last behavior of the inspected user, based on a proposed neighbor behavior extractor. By diverse trends, supposing the future preferences can be diversified, we propose the diverse trends extractor and the time-aware mechanism to represent the possible trends of preferences for a given user with multiple vectors. We leverage both the representations of historical preference and possible future trends to obtain the final recommendation. The quantitative and qualitative results from relatively extensive experiments on real-world datasets demonstrate the proposed framework not only outperforms the state-of-the-art sequential recommendation methods across various metrics, but also makes complementary and fresh recommendations.

Matching items for a user from a travel item pool of large cardinality have been the most important technology for increasing the business at Fliggy, one of the most popular online travel platforms (OTPs) in China. There are three major challenges facing OTPs: sparsity, diversity, and implicitness. In this paper, we present a novel Fliggy ITinerary-aware deep matching NETwork (FitNET) to address these three challenges. FitNET is designed based on the popular deep matching network, which has been successfully employed in many industrial recommendation systems, due to its effectiveness. The concept itinerary is firstly proposed under the context of recommendation systems for OTPs, which is defined as the list of unconsumed orders of a user. All orders in a user itinerary are learned as a whole, based on which the implicit travel intention of each user can be more accurately inferred. To alleviate the sparsity problem, users' profiles are incorporated into FitNET. Meanwhile, a series of itinerary-aware attention mechanisms that capture the vital interactions between user's itinerary and other input categories are carefully designed. These mechanisms are very helpful in inferring a user's travel intention or preference, and handling the diversity in a user's need. Further, two training objectives, i.e., prediction accuracy of user's travel intention and prediction accuracy of user's click behavior, are utilized by FitNET, so that these two objectives can be optimized simultaneously. An offline experiment on Fliggy production dataset with over 0.27 million users and 1.55 million travel items, and an online A/B test both show that FitNET effectively learns users' travel intentions, preferences, and diverse needs, based on their itineraries and gains superior performance compared with state-of-the-art methods. FitNET now has been successfully deployed at Fliggy, serving major online traffic., 12 pages

With the vigorous development of big data and the 5G era, in the process of communication, the number of information that needs to be forwarded is increasing. The traditional end-to-end communication mode has long been unable to meet the communication needs of modern people. Therefore, it is particularly important to improve the success rate of information forwarding under limited network resources. One method to improve the success rate of information forwarding in opportunistic social networks is to select appropriate relay nodes so as to reduce the number of hops and save network resources. However, the existing routing algorithms only consider how to select a more suitable relay node, but do not exclude untrusted nodes before choosing a suitable relay node. To select a more suitable relay node under the premise of saving network resources, a routing algorithm based on intuitionistic fuzzy decision-making model is proposed. By analyzing the real social scene, the algorithm innovatively proposes two universal measurement indexes of node attributes and quantifies the support degree and opposition degree of node social attributes to help node forward by constructing intuitionistic fuzzy decision-making matrix. The relay nodes are determined more accurately by using the multi-attribute decision-making method. Simulation results show that, in the best case, the forwarding success rate of IFMD algorithm is 0.93, and the average end-to-end delay, network load, and energy consumption are the lowest compared with Epidemic algorithm, Spray and Wait algorithm, NSFRE algorithm, and FCNS algorithm.

A colocated multiple-input multiple-output (MIMO) radar system may be interested in sending different data stream to multiple communication sinks widely separated in different directions with a physical layer security feature, i.e., a time-division dual function radar communications (DFRC) mode. Conventional channel precoding methods do not depend on the contents of the data streams. In this paper, given communication channels, we directly optimize constant-modulus transmit waveforms for a colocated MIMO radar, such that those communication sinks can decode the data stream in their own manner. Given the constellations of communication sinks, we design codes for different transmit antennas. Different from the precoding method, we transmit the optimized codes instead of precoded codes into space if a code combination needs to be transmitted into space. Meanwhile, the code optimization method enables to distribute different powers into space and can avoid eavesdroppers in other spatial directions from decoding the information transmitted. Numerical results with phase shift keying (PSK) constellations indicate that this method can form several intended code constellations in different spatial directions and the accuracy is higher if more transmit antennas are available. Meanwhile, the eavesdroppers in other space directions have a messed code constellations and it is hard to decode useful information from received signal.

Blockchain has unique advantages in data privacy protection and data integrity. We can solve many security problems, such as the single point of failure and data sharing in the current centralized system through blockchain approach. Existing studies have demonstrated that the application of blockchain in the medical system could improve the patient's medical experience. However, we found that the blockchain-based medical systems didn't consider the problems of long insurance claim cycle and complicated procedures. There are also very few healthcare systems that provide targeted sharing protocols for medical data and personal health data. In this paper, we propose a multi-role healthcare data sharing system framework based on blockchain. In this system, we reduce the storage cost through the collaborative storage of blockchain and IPFS. Then we design a smart contract on insurance to help patients achieve automatic insurance claim. In addition, we design two different sharing protocols to realize the fine management of personal data. The system analysis shows that our proposed blockchain-based multi-role healthcare data sharing system can effectively address the actual needs of users and has perfect performance in data storage, privacy protection, insurance claims and personal data management.

International audience; Alloy melts can solidify into metallic glasses if cooled fast enough to avoid crystallization. Glass-forming ability (GFA), a measure of the ease of vitrification, is vital for the fundamental understanding of glass formation, and is also crucial for the application of metallic glasses. Previous studies of GFA mainly focused on the undercooled liquid phase, while the influence of the evolution of the stable melts on GFA is rarely addressed. Here we show that the Pd42.5Ni42.5P15 glass-forming liquid, in which a first-order liquid-liquid transition (LLT) takes place at TLL = 1063 K high above its liquidus temperature, shows significantly different GFA when quenched from the temperature above or below TLL. Moreover, the pathway and kinetics of crystallization of the melt are strongly related to the kinetics of the LLT. Our work provides new insights into the vitrification process and the kinetics of crystallization, and contributes to designing more stable metallic glasses.

V-Cr-Al-O nanospheres were successfully synthesized using V2O5, Al(OH)3, CrO3, and H2C2O42O as the starting materials by a facile one-pot hydrothermal approach. Several techniques containing X-ray powder diffraction, hydrogen temperature programmed reduction, scanning electron microscopy were used to characterize the composition, morphology and redox property of V-Cr-Al-O nanospheres. The catalytic behavior of prepared nanospheres on the thermal decomposition of AP was investigated by the thermogravimetric analysis and differential thermal analysis (TG/DTA). The experimental results show that the thermal decomposition temperature of AP in the presence of V-Cr-Al-O nanospheres is to 395 °C (decreased by 35 °C), which proves better catalyst for the thermal decomposition of AP.

In this paper, the output feedback consensus problem of high-order multi-agent systems with nonlinear uncertainties is researched by the robust control method. By the dimensional extension of the observation matrix, the consensus control problem is transformed into a stability problem. Then the robust controller is designed by combining the nominal controller and the robust compensator. The nominal controller can obtain desired nominal performance based on the output informations. The robust compensator, which relys on robust signal compensation technology, is order to suppress the nonlinear uncertainties. According to the proposed method, output consensus error can be guaranteed as small as desired. Finally, simulation results are given to illustrate the effectiveness of this control method.

A new porous metal-organic framework, namely [Co3(L)(OH)2(H2O)4]·NMP·3H2O (1), has been yielded by using Co(II) and rigid V-shaped 2,6-di(2′,5′-dicarboxylphenyl)pyridine (H4L). The structure analysis revealed that MOF 1 is a three-dimensional (3D) porous framework with 1D [Co3(OH)2]n ferromagnetic chains. The gas sorption measurements and the IAST analysis indicated MOF 1 has a relatively high selectivity for C2H6 and CO2 over CH4. The magnetic properties indicated that MOF 1 has the ferrimagnetic and slow magnetic relaxation behavior, giving an interesting single-chain magnet behavior with an effective energy barrier of 15.82 K.

In this brief, the robust trajectory tracking control problem is addressed for uncertain quadrotor systems with time delays. The quadrotor dynamical model is an under-actuated system subject to non-linearity, unmodelled uncertainties, parametric perturbations, external disturbances, and state and input time delays. A robust cascade trajectory tracking controller is developed by the backstepping method and the robust compensation theory. Theoretical analysis is given to prove that the tracking errors can converge into the given neighbourhood of the origin ultimately. An on-line unidirectional tuning method is given to determine the controller parameters. Experimental results are provided to verify the effectiveness of the proposed control approach and the parameter tuning method.