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The control reliability of model predictive control is largely determined by the accuracy of the process model. The Hammerstein-Wiener (HW) model is an important nonlinear process modeling technique that has obtained great success in some process industries. Disturbances result in model mismatch and steady-state deviation, but little effort has been devoted to the coupling effects and inertial information in measured disturbances. In addition, few studies try to construct a disturbance observer (DO) to alleviate unmeasured disturbances. The present work proposes prompt disturbance rejection. First, a spatial-temporal long short-term memory-based measurable disturbance encoder is devised to analyze time-series information from measured disturbances and their coupling effects. The encoder can further clarify the status of inertial interference components and the disturbance intensity. Second, a DO is designed to estimate and attenuate unmeasured disturbances. Third, to create the new HW network, which is improved by integrating the disturbance encoder and observer differential, neural networks are used as nonlinear parts. Finally, a model predictive controller based on this improved model is constructed for real-time industrial process control. Simulation comparison experiments have demonstrated the superiority of the proposed methods. Real industry application in the country's largest lead-zinc froth flotation plant in China validated the proposed model's effectiveness in controlling chemical reagents.

Spatial crowdsourcing (SC) is a location-based outsourcing service whereby SC-server allocates tasks to workers with mobile devices according to the locations outsourced by requesters and workers. Since location information contains individual privacy, the locations should be protected before being submitted to untrusted SC-server. However, the encryption schemes limit data availability, and existing differential privacy (DP) methods do not protect the tasks' location privacy. In this paper, we propose a differential privacy-based location protection (DPLP) scheme, which protects the location privacy of both workers and tasks, and achieves task allocation with high data utility. Specifically, DPLP splits the exact locations of both workers and tasks into noisy multi-level grids by using adaptive three-level grid decomposition (ATGD) algorithm and DP-based adaptive complete pyramid grid (DPACPG) algorithm, respectively, thereby considering the grid granularity and location privacy. Furthermore, DPLP adopts an optimal greedy algorithm to calculate a geocast region around the task grid, which achieves the trade-off between acceptance rate and system overhead. Detailed privacy analysis demonstrates that our DPLP scheme satisfies e-differential privacy. The extensive analysis and experiments over two real-world datasets confirm high efficiency and data utility of our scheme.

We modified the morphology and distribution of copper-bearing precipitates in an Al-Mg-Si-Cu alloy and obtained ultra-high comprehensive properties through a modified thermo-mechanical treatment composed of aging and cold deformation. The precipitate evolution and atomic structure was observed by employing atomic resolution high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM). Our results proved the existence of β′′/Q′ composite precipitates in rod-like morphology after aging treatment due to the inhibition of β′′ phase formation by copper atoms. We also revealed that the peak-aged phases transformed into in-situ reverse-transformed Guinier Preston zones (GP zones) during the deformation process. By the means of modifying the precipitates, we finally simultaneously obtained ultra-high strength (424.40 MPa) as well as favorable conductive properties (52.78%IACS), both of which surpassed three mainstream standards for high strength aluminum conductor.

Reversible addition–fragmentation chain-transfer (RAFT) polymerization has been widely exploited to produce homogeneous and living polymer networks for advanced material design. In this work, we incorporate silica nanoparticles (SNPs) into a RAFT polymerization system to prepare a series of photocurable composite resins. These resins are applied to stereolithographic 3D printing to produce composite materials with enhanced mechanical properties. Relatively high loadings of SNPs (30 wt%) within the resins are achieved, allowing an increase in the 3D printed composite material tensile strength by 165% while simultaneously increasing the fracture toughness by 168%, compared with the pristine polymer materials. In addition, the inclusion of small amounts (1 wt%) of RAFT agent into the 3D printing resins also provide efficient control over thermomechanical, tensile, and fracture properties of the resulting materials. In particular, 3D printed SNP composite materials containing 30 wt% SNPs and 1 wt% RAFT show a 37% increase in elongation at break values and a 60% increase in tensile strength compared with free radical polymerization-based counterparts. The increased ductility of composite materials imparted via RAFT agent addition has not been reported and could provide a useful tool for optimizing the performance of composite materials prepared by 3D printing. Additionally, photoinduced reactivation of thiocarbonylthio groups provides access to spatially controlled post-printing functionalization of the surface of the composite materials. As such, RAFT-mediated 3D printing of composite resins is shown to be an effective strategy for developing composite materials with arbitrary combinations of well-defined mechanical properties and diverse chemical functionalities for material post-modification.

In this paper, we study the digital implementation of derivative-dependent control for consensus of stochastic multi-agent systems. The consensus controllers that depend on the output and its derivatives are approximated as delayed sampled-data controllers. First, we consider the nth-order stochastic multi-agent systems. Second, we consider PID control of the second-order stochastic multi-agent systems. For the consensus analysis, we propose novel Lyapunov-Krasovskii functionals to derive linear matrix inequalities (LMIs) that allow to find admissible sampling period. The efficiency of the presented approach is illustrated by numerical examples.

In the present paper, we consider network-based control of linear systems with state multiplicative noise. For the sensor-controller network, Round-Robin (RR), try-once-discard (TOD) and independent and identically-distributed (i.i.d) protocols are proposed to orchestrate the measurement transmission from multiple sensor nodes. By using the time-delay approach to networked control systems, sufficient conditions for the exponential mean-square stabilization are obtained in terms of linear matrix inequalities (LMIs). Moreover, a discrete-time dynamic event-trigger is suggested to reduce the number of sent control signals from controller to actuator. An air vehicle system from the literature illustrates the efficiency of the presented approach.

Supercapacitors possessing multiple functions other than storing energy, such as bearing mechanical loads, are a promising technology for a wide range of applications. However, achieving both energy storage efficiency and mechanical strength/stiffness requires structurally strong electrolytes and high-capacitance electrodes. Herein, we report a novel method of synthesizing a high-mass loading of MnO2 on carbon nanotube (CNT) mats to create nanocomposite electrodes of high capacitance and mechanical properties. With CNTs acting as structural reinforcement for the pseudocapacitive MnO2 matrix, the resulting nanocomposite electrodes exhibited an ultrahigh areal capacitance of 2579 mF/cm2 at a current density of 1 mA/cm2 and excellent mechanical properties. These electrodes were then used to fabricate flexible and mechanically strong structural supercapacitors by infusing with a PVA gel electrolyte and a PEGDGE solid electrolyte, respectively. The resulting flexible supercapacitors yielded a high areal capacitance of 947 mF/cm2 while the structural supercapacitors gave a high tensile modulus of 6.1 GPa. These results demonstrated that the hierarchical MnO2/CNT electrodes could provide both excellent energy storage capability and structural stiffness and strength, expanding their applications beyond mono-functional supercapacitors.

WO3–x (W-1) was used to achieve controllable photoisomerization of linear olefins without substituents under 625 nm light irradiation. Thermodynamic and kinetic isomers were obtained by regulating the carbon chain length of the olefins. Terminal olefins were converted into isomerized products, and the internal olefin mixtures present in petroleum derivatives were transformed into valuable pure olefin products. Oxygen vacancies (OVs) in W-1 altered the electronic structure of W-1 to improve its light-harvesting ability, which accounted for the high activity of olefin isomerization under light irradiation up to 625 nm. Additionally, OVs on the W-1 surface generated unsaturated W5+ sites that coordinated with olefins for the efficient adsorption and activation of olefins. Mechanistic studies reveal that the in situ formation of surface π-complexes and π-allylic W intermediates originating from the coordination of coordinated unsaturated W5+ sites and olefins ensure high photocatalytic activity and selectivity of W-1 for the photocatalytic isomerization of olefins via a radical mechanism.

To enable a quick and accurate access of targeted scientific and technological literature from massive stocks, here a deep content-based collaborative filtering method, namely DeepCCF, for personalized scientific and technological literature resources recommendation was proposed. By combining content-based filtering (CBF) and neural network-based collaborative filtering (NCF), the approach transforms the problem of scientific and technological literature recommendation into a binary classification task. Firstly, the word2vec is used to train the words embedding of the papers’ titles and abstracts. Secondly, an academic literature topic model is built using term frequency–inverse document frequency (TF-IDF) and word embedding. Thirdly, the search and view history and published papers of researchers are utilized to construct the model that portrays the interests of researchers. Deep neural networks (DNNs) are then used to learn the nonlinear and complicated high-order interaction features between users and papers, and the top k recommendation list is generated by predicting the outputs of the model. The experimental results show that our proposed method can quickly and accurately capture the latent relations between the interests of researchers and the topics of paper, and be able to acquire the researchers’ preferences effectively as well. The proposed method has tremendous implications in personalized academic paper recommendation, to propel technological progress.

In recent years, the developed hemostatic technologies are still difficult to be applied to the hemostasis of massive arterial and visceral hemorrhage, owing to their weak hemostatic function, inferior wet tissue adhesion, and low mechanical properties. Herein, a mussel-inspired supramolecular interaction-cross-linked hydrogel with robust mechanical property (308.47 ± 29.20 kPa) and excellent hemostatic efficiency (96.5% ± 2.1%) was constructed as a hemostatic sealant. Typically, we combined chitosan (CS) with silk fibroin (SF) by cross-linking them through tannic acid (TA) to maintain the structural stability of the hydrogel, especially for wet tissue adhesion ability (shear adhesive strength = 29.66 ± 0.36 kPa). Compared with other materials reported previously, the obtained CS/TA/SF hydrogel yielded a lower amount of blood loss and shorter time to hemostasis in various arterial and visceral bleeding models, which could be ascribed to the synergistic effect of wound closure under wet state as well as intrinsic hemostatic activity of CS. As a superior hemostatic sealant, the unique hydrogel proposed in this work can be exploited to offer significant advantages in the acute wound and massive hemorrhage with the restrictive access of therapeutic moieties., Graphical abstract Image 1, Highlights • A mussel-inspired supramolecular hydrogel is designed as a hemostatic sealant. • The biomimetic hydrogel presents robust wet tissue adhesion ability. • Wound closure and intrinsic hemostatic activity accelerate clotting synergistically. • The strategy realizes hemostasis of massive arterial and visceral hemorrhage.

In this study, the carbon quantum dots, which can emit sharp red light (R-CQDs) under optical excitation, were synthesized via simple heat treatment of wine lees. The features of the photoluminescence (PL) emission from R-CQDs in solutions with different pH values and with metal ions were examined. The intensity of the PL emission from R-CQDs depends sensitively on the pH values of the solutions with the R-CQDs. Interestingly, the pH-response of the PL intensity is reversible and can be enhanced linearly in the pH value range from 4 to 12, indicating that the R-CQDs can be applied for the pH sensor. Furthermore, the quench of the PL emission from R-CQDs can be observed when adding chromium (III) ions into the solution with R-CQDs. This effect can be utilized for selective and quick detection of chromium (III) ions in solutions simply via the standard PL measurement. The interesting and important findings from this work can be applied to explore more practical applications of the CQDs prepared cheaply and easily.

To optimize the electronic structure of photocatalyst, a facile one-step approach is developed for the simultaneous realization of Zn-doping and surface oxygen vacancies (SOVs) formation on SnO2. The Zn-doped SnO2 with abundant SOVs exhibits efficient and stable performance for photocatalytic degradation of toluene under both low and high relative humidity. Experimental and theoretical calculations results show that the synergistic effects of Zn-doping and SOVs on SnO2 can considerably boost the charge transfer and separation efficiency. Utilizing the in situ DRIFTS and theoretical calculations methods, it is revealed that the benzene ring of toluene is opened at benzoic acid on the SnO2 surface and selectively at benzaldehyde on the Zn-doped SnO2 surface. This implies that Zn-doped SnO2 photocatalysts shorten the pathway of toluene degradation, and toxic intermediates can be significantly inhibited. This work could provide a promising and sustainable route for safe and efficient removal of aromatic VOCs with photocatalytic technology.

AgNbO3-based ceramics have been the spotlight for the lead-free dielectric capacitors due to its unique antiferroelectric feature and the ever-increasing environmental concerns. Herein, synergic modulation on the energy storage properties of AgNbO3-based ceramics was reported, in which the over-stoichiometrical introduction of only 0.10 wt% MnO2 at the atomic-scale leads to reduced leakage current, Pr and enhanced antiferroelectric stability while the SiO2 coating on the AgNbO3 particles at the micro-scale greatly inhibits the grain growth, increases the breakdown strength and the electric filed inducing the AFE-FE transitions. As a result, a large recoverable energy density up to 3.34 J/cm3 with efficiency of 60.0% was obtained in 0.10 wt% MnO2-doped AgNbO3@SiO2 ceramic, which is 2.3 times larger than that of the pristine AgNbO3. This work provides a rational approach to synergically modulate the energy storage properties.

Peritendinous adhesion, secondary to the repair surgery of tendon rupture or injury, is one of the most common causes of reoperation, owing to the proliferation of fibrous tissue and excessive collagen synthesis caused by the residing inflammatory cells. In this study, a smart oxidative stress-responsive electrospun polyester membrane (EPM) was fabricated as both physical barrier and reservoir of curcumin/celecoxib (CUR/CEL) to prevent peritendinous adhesion. The multicomponent EPM was designed to release the encapsulated drugs in response to oxidative stress of the local microenvironment induced by inflammation. Specifically, sulfides in the EPM were able to react with reactive oxygen species (ROS) and become hydrophilic sulfoxide or sulfone to accelerate the release rate of drugs and regulate oxidative stress level in the inflammatory site intelligently. The oxidation-sensitive multicomponent EPM loaded with CUR and CEL was tested for anti-adhesion capacity in vitro and in vivo. An excellent ROS-sensitive degradation behavior and good cytocompatibility with cell viability of above 85% were presented with the fabricated EPM. The CUR- or CEL-loaded EPM possessed a better anti-adhesion ability compared with EPM without the drugs. Nevertheless, they were inferior to the EPM simultaneously loaded with both drugs, where the adhesion rate and fibrous adhesion number in the EPM+CUR/CEL group were close to extremely low values of about zero, demonstrating that CUR and CEL could synergistically prevent peritendinous adhesion. More interestingly, the multicomponent EPM was able to react with the local oxidative stress, leading to a smart and sustained behavior of releasing approximately 80% of the drug within 20 days. Overall, the smart multicomponent EPM offers a promising barrier strategy to prevent peritendinous adhesion.

We study linear convergence of some first-order methods such as the proximal gradient method (PGM), the proximal alternating linearized minimization (PALM) algorithm and the randomized block coordinate proximal gradient method (R-BCPGM) for minimizing the sum of a smooth convex function and a nonsmooth convex function. We introduce a new analytic framework based on the error bound/calmness/metric subregularity/bounded metric subregularity. This variational analysis perspective enables us to provide some concrete sufficient conditions for linear convergence and applicable approaches for calculating linear convergence rates of these first-order methods for a class of structured convex problems. In particular, for the LASSO, the fused LASSO and the group LASSO, these conditions are satisfied automatically, and the modulus for the calmness/metric subregularity is computable. Consequently, the linear convergence of the first-order methods for these important applications is automatically guaranteed and the convergence rates can be calculated. The new perspective enables us to improve some existing results and obtain novel results unknown in the literature. Particularly, we improve the result on the linear convergence of the PGM and PALM for the structured convex problem with a computable error bound estimation. Also for the R-BCPGM for the structured convex problem, we prove that the linear convergence is ensured when the nonsmooth part of the objective function is the group LASSO regularizer.

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.

Joint extraction of entities and relations from unstructured text is an essential step in constructing a knowledge base. However, relational facts in these texts are often complicated, where most of them contain overlapping triplets, making the joint extraction task still challenging. This paper proposes a novel Sequence-to-Sequence (Seq2Seq) framework to handle the overlapping issue, which models the triplet extraction as a sequence generation task. Specifically, a unique cascade structure is proposed to connect transformer and pointer network to extract entities and relations jointly. By this means, sequences can be generated in triplet-level and it speeds up the decoding process. Besides, a syntax-guided encoder is applied to integrate the sentence’s syntax structure into the transformer encoder explicitly, which helps the encoder pay more accurate attention to the syntax-related words. Extensive experiments were conducted on three public datasets, named NYT24, NYT29, and WebNLG, and the results show the validity of this model by comparing with various baselines. In addition, a pre-trained BERT model is also employed as the encoder. Then it comes up to excellent performance that the F1 scores on the three datasets surpass the strongest baseline by 5.7%, 5.6%, and 4.4%.

The accuracy of the prediction and reliability of the deflection basin area index method for rigid pavement heavily depend on the sensor layout scheme and the calculation theory. A total of 154 groups of deflection data were generated by the finite element software in different conditions, and the simulated results were in good agreement with the analytical solutions of thin plates on elastic foundation. The accuracy of several types of deflection basin area index methods was assessed with the database of pavement deflection results. It is found that, apart from densifying the distal sensor layouts locally, replacing the deflection value of a specific point with the average of all measured points in the deflection basin area index was also an effective measure to improve the back-calculation accuracy of pavement structural parameters up to 40%. Finally, a new deflection basin area index method was proposed based on the deflection database with the regression analysis. Comparisons between the theoretical calculations of several models and the practical deflection data of an airport in northern China revealed that the newly proposed method performs better on the back-calculation accuracy and efficiency, which can provide a valuable guideline for the practical engineering in the design.

The formation mechanism of rockburst is complex, and its prediction has always been a difficult problem in engineering. According to the tunnel engineering data, a three-dimensional discrete element numerical model is established to analyze the initial stress characteristics of the tunnel. A neural network model for rockburst prediction is established. Uniaxial compressive strength, uniaxial tensile strength, maximum principal stress, and rock elastic energy are selected as input parameters for rockburst prediction. Training through existing data. The neural network model shows that the rockburst risk is closely related to the maximum principal stress. Based on the division of rockburst risk areas, according to different rockburst levels, the corresponding treatment methods are put forward to avoid the occurrence of rockburst disaster. Based on the field measured data and test data, combined with the existing rockburst situation, numerical simulation and neural network method are used to predict the rock burst classification, which is of great significance for the early and late construction safety of the tunnel.

Critical to the applications of pseudocapacitors is the cycling performance of electrodes. Here, we present a new carbon fibre electrode enhanced by multilayer structure of vertical graphene (VG) and manganese dioxide (MnO2) to achieve a cycling performance at a very high areal capacitance (546.3 mF/cm2) that outperforms most of the reported pseudocapacitors in the literature. Particularly, this multilayer electrode is able to retain 99.3% of its initial capacitance after 10,000 cycles at scan rate of 200 mV/s. The retention ratio is significantly higher than the state-of-art value. In addition, this electrode shows excellent rate performance (75.4% capacitance retention for current density from 1 to 10 mA/cm2). This exceptionally high stability at high capacitance and rate performance originates from the multilayer electrode’s effectiveness in self-replenishing the degraded MnO2 in the outer layer. The microcracks and micro holes induced in the VG layer by electrical charge/discharge cycling enable the inner layer MnO2 to progressively participate in the redox reaction. Results from energy dispersive spectroscopy, X-ray photoelectron, and Raman spectroscopy confirm the self-replenishing mechanism responsible for the exceptionally stable performance. The new multilayer electrode designed herein provides a new route for improving the cycling performance of pseudocapacitors.

To obtain an A356 aluminum alloy casting with a uniform structure and no internal shrinkage defects, ProCAST software is used to set different filling and solidification process parameters for an A356 aluminum alloy casting with large wall thickness differences, And multiple simulations are conducted to obtain optimized casting process; then, based on the process, the microstructure of the thickest and thinnest part of the casting are simulated. The size, morphology, and distribution of the simulated microstructure of the thinnest part and the thickest part of the casting are very similar. The simulated microstructure is similar to that of the actual casting. This shows that castings with uniform structure and no internal shrinkage defects can be obtained through the optimized casting process .

Transceiving ultra-weak sound typically relies on signal pre-amplification at the transmitting end via active electro-acoustic devices, which inherently perturbs the environment in the form of noise that inevitably leads to information leakage. Here we demonstrate a passive remote-whispering metamaterial (RWM) enabling weak airborne sound at audible frequencies to reach unprecedented signal enhancement without altering the detected ambient soundscape, which is based on the extraordinary scattering properties of a metamaterial formed by a pair of self-resonating subwavelength Mie meta-cavities, constituting the acoustic analogy of Förster resonance energy transfer. We demonstrate efficient non-radiative sound transfer over distances hundreds times longer than the radius of the meta-cavities, which enables the RWM to recover weak sound signals completely overwhelmed by strong noise with enhanced signal-to-noise ratio from −3 dB below the detection limit of 0 dB in free space to 17.7 dB., Typically, sending sound from transmitter to receiver requires pre-amplification and disturbs the surrounding sound environment. Here, the authors present a metamaterial designed to enable transmission of weak sound that can be recovered even in the presence of strong noise

30SiMn2MoVA is a high-strength alloy steel used for barrels. Cold radial forging can greatly improve the performance of axles, barrels and other shaft parts. However, residual stresses will greatly affect the dimensional stability and service life of the products. This work focused on the effects of different annealing temperatures on the residual stress, microhardness, dislocation density, grain boundary and grain size of radial forged tubes. The results show that the residual stresses are gradually eliminated with the deformed grains transform into equiaxed and the reduction in dislocation density. The residual stress relaxation caused by the creep process conforms to the Zener-Wert-Avrami equation. As the dislocation density and low-angle grain boundary decrease, the microhardness also decreases gradually. The best annealing temperature for radial forged 30SiMn2MoVA high-strength steel tube is 500 °C. After annealing at 500 °C, the microhardness decreases by 5%, the axial and hoop residual stress on the outer surface are close to 0 MPa, and the residual stress near the inner surface are less than 200 MPa. When the annealing temperature is higher than 500 °C, there is no significant change in residual stress relaxation rate. Nevertheless, the grain size and the low angle boundary decreased significantly. Partial recrystallization occurred, and the microhardness decreased by more than 10%.

Carbonaceous slate is heterogeneous and anisotropic, which has a great influence on the stability of tunnel. In this paper, by means of laboratory test, field measurement, and numerical simulation, the surrounding rock stability and plastic zone distribution characteristics of the carbonaceous slate tunnel at different intersection angles are analyzed. First, combined with the Haibaluo tunnel project, Brazilian splitting and uniaxial compression tests of jointed carbonaceous slate are performed. The test results show that the tensile strength of carbonaceous slate is related to joint dip angle. When the joint angle is 0°, the tensile strength is the largest and decreases with the increase of the joint angle. The uniaxial strength of rock decreases first and then increases. Based on the discrete fracture network (DFN) technology, a calculation model is established. The calculation results show that the maximum displacement is 0.45 m, when the dip angle of the surrounding rock joint is 45°. The field measurement also shows that the dip angle of the surrounding rock joint has an important influence on the distribution of the plastic zone. When the joint dip angle is 45°, the plastic zone develops most strongly.

In this paper, we conduct a comprehensive investigation on the thermal transport in one-dimensional (1D) van der Waals (vdW) heterostructures by using non-equilibrium molecular dynamics simulations. As for the considered 1D vdW heterostructures having a base of (40,40) carbon nanotube (CNT), we find that the boron nitride nanotube (BNNT) coating can increase the thermal conductance of inner CNT base by 36%, while the molybdenum disulfide nanotube (MSNT) coating can reduce the thermal conductance by 47%. The different effects of nanotube coatings on the thermal transport behaviors of 1D vdW heterostructures are explained by the competition mechanism between the improved heat flux and the increased temperature gradient in 1D vdW heterostructures. The thermal transport in 1D vdW heterostructures containing three layers is also investigated. It is found that the coaxial BNNT-MSNT coating can significantly reduce the thermal conductance of inner CNT base by 61%, which is even larger than the influence of an individual MSNT coating. This work not only provides molecular insights into the heat transport behaviors of 1D vdW heterostructures but also provides guidance for the design of 1D vdW heterostructures with desired thermal properties.

Summary Two-dimensional (2D) graphdiyne (GDY) is a promising floating-gate material for flexible optoelectronic flash memory owing to its fascinating electrical and optical properties. However, research in GDY-based flash memory is still in its infancy owing to the huge challenge in the synthesis of large-area and ultrathin GDY films with high quality and uniformity. Here, an electric double-layer-confined strategy is proposed to synthesize a wafer-scale GDY film with thickness of 1 nm. Then, a two-terminal top-floating-gated optoelectronic memory with multibit storage capability is investigated using GDY as a photoresponsive top-floating gate. Benefiting from the excellent charge storage capability and high photoresponse of GDY, this device exhibits over 256 distinct storage levels (8 bits) with signal-to-noise ratios larger than 100. Moreover, the fully 2D material and two-terminal architecture endows the device with robust bending stability for over 1,000 bending circles, paving the way to develop wearable electronics.

Microwave absorbing metasurfaces with optical transparency are essentially useful in electromagnetic compatibility, stealth, or camouflage while keeping continuous optical observation or communication. Although significant efforts have been devoted on expanding the bandwidths of optically transparent absorbing metasurfaces, dynamic control of microwave absorption in such metasurfaces remains a challenge. In this paper, we experimentally propose a metasurface made of patterned graphene sandwich structures, showing unprecedented wave control, excellent optical transparency and outstanding flexibility. Both the simulated and measured results demonstrate that the tunable absorption with dual, single and broad band are realized by applying bias voltage to graphene layers. In particular, the as-designed metasurface shows a strong microwave absorption of 90 % covering a wide frequency band of 7–18 GHz due to the electric and magnetic resonances caused by the patterned graphene layer, as well as the multiple reflection mechanism. We speculate that such a metasurface with optical transparency and tunable microwave absorption has potential applications in stealth technology and photovoltaic solar cells.

Funding Information: This work was supported by the National Natural Science Foundation of China (52025023, 51991342, 52021006, 51722204, 51972041, 51972042, 51672007, 11974023, 12025407 and 11934003), the National Key R&D Program of China (2016YFA0300903 and 2016YFA0300804), Beijing Natural Science Foundation (JQ19004), Beijing Excellent Talents Training Support (2017000026833ZK11), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB33000000), Beijing Municipal Science & Technology Commission (Z191100007219005), Beijing Graphene Innovation Program (Z181100004818003), Key-Area Research and Development Program of GuangDong Province (2020B010189001, 2019B010931001 and 2018B030327001), the Science, Technology and Innovation Commission of Shenzhen Municipality (KYTDPT20181011104202253), Bureau of Industry and Information Technology of Shenzhen (Graphene platform 201901161512), The Pearl River Talent Recruitment Program of Guangdong Province (2019ZT08C321), National Equipment Program of China (ZDYZ2015-1) and the China Postdoctoral Science Foundation (2020M680177). Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer Nature Limited. Copyright: Copyright 2021 Elsevier B.V., All rights reserved. Colloidal quantum dots are promising photoactive materials that enable plentiful photonic and optoelectronic applications ranging from lasers, displays and photodetectors to solar cells1–9. However, these applications mainly utilize the linear optical properties of quantum dots, and their great potential in the broad nonlinear optical regime is still waiting for full exploration10–12. Here, we demonstrate that a simple coating of a sub-200-nm-thick quantum dot film on two-dimensional materials can significantly enhance their nonlinear optical responses (second, third and fourth harmonic generation) by more than three orders of magnitude. Systematic experimental results indicate that this enhancement is driven by a non-trivial mechanism of multiphoton-excitation resonance energy transfer, where the quantum dots directly deliver their strongly absorbed multiphoton energy to the adjacent two-dimensional materials by a remote dipole–dipole coupling. Our findings could expand the applications of quantum dots in many exciting areas beyond linear optics, such as nonlinear optical signal processing, multiphoton imaging and ultracompact nonlinear optical elements.

The development of rechargeable lithium-ion batteries (LIBs) is being driven by the ever-increasing demand for high energy density and excellent rate performance. Charge transfer kinetics and polarization theory, considered as basic principles for charge regulation in the LIBs, indicate that the rapid transfer of both electrons and ions is vital to the electrochemical reaction process. Graphene, a promising candidate for charge regulation in high-performance LIBs, has received extensive investigations due to its excellent carrier mobility, large specific surface area and structure tunability, etc. Recent progresses on the structural design and interfacial modification of graphene to regulate the charge transport in LIBs have been summarized. Besides, the structure-performance relationships between the structure of the graphene and its dedicated applications for LIBs have also been clarified in detail. Taking graphene as a typical example to explore the mechanism of charge regulation will outline ways to further understand and improve carbon-based nanomaterials towards the next generation of electrochemical energy storage devices.

The microscopic structure of a macroscopic material assembled from a nanoscale determines its performance in practical functional applications. The rapid, effective, and in situ characterization of...

The addition of carbon additives to the catalytic layers (CLs) with low-Pt loadings significantly improves the cell performance of proton exchange membrane fuel cells. However, the structure-activity relationship between the different dimensional carbon materials in CLs and the cell performance is still unknown. In the present work, three different dimensional carbon materials have been added into the cathode CLs with low Pt loading. The addition of one-dimensional carbon nanotubes and zero-dimensional XC-72R significantly enhanced the power density of the fuel cell because of the improved Pt dispersion and porous structures of the CL. This resulted in enhanced gas transfer and water removal accessibility. Nevertheless, the tortuous transfer path of gas and water after the presence of graphene nanosheet in the CL results in increased mass transfer resistance, although it reduces the charge transfer of the CL due to the improved catalyst utilization at low current density regions of polarization curves.

Electrical discharge machining (EDM) is a rapidly developing technique amongst nontraditional techniques. However, low processing efficiency significantly hinders its application in industry. This study proposes a new high-speed EDM milling method using rotating short arcs under a composite energy field. By the combined effect of the Lorentz force, electric field force, and high-speed rotation of the tool electrode, rotating short arcs are generated between the tool electrode and the workpiece, which in turn provide constant erosion of materials. The proposed method can increase the discharge energy density by converging the electric arcs through the composite energy field, and increase the energy obtained by the workpiece. By this means, it improves the material removal rate. During processing, a portion of debris is attracted by the magnetic field and adheres to the end surface of the electrode, which can protect the electrode and reduce the electrode wear rate. Compared with traditional EDM, the material removal rate of the rotating short arc milling was increased by 46 %, while the tool electrode wear rate decreased by 62 %. Furthermore, an increase in the strength of the magnetic field increases the material removal rate of the rotating short arc milling and decreases the tool electrode wear rate and surface roughness. Therefore, high-efficiency and high-quality EDM milling can be achieved by using the proposed method.

The fluorescent carbon quantum dots (FCQDs) are promising for wide applications especially in the field of optoelectronic materials and devices. For these applications, it is essential to examine the optoelectronic properties of FCQDs not only in a solution but also in the form of dry particles. Here we study the optoelectronic properties of dry FCQDs by using terahertz (THz) time-domain spectroscopy (TDS). The THz response of dry FCQDs, which can emit blue (B-CQDs) and red (R-CQDs) lights in solutions under optical excitation, has been measured in temperature range from 80 to 280 K. We find that R-CQDs behaves like an optical insulator in 0.2–1.2 THz range, while B-CQDs experiences insulator-to-semiconductor transition with increasing THz frequency and temperature. The optical conductance and the key sample parameters of B-CQDs are determined. This is the first research work in applying the THz TDS to study the dry CQDs. The findings from this study can be helpful in gaining an in-depth understanding of the physical properties of dry FCQDs.

Froth flotation is an important mineral concentration technique. Faulty conditions in flotation processes may cause the huge waste of mineral resources and reagents, and consequently, may lead to deterioration in terms of benefits of flotation plants. In this paper, we propose a computer vision-aided fault detection and diagnosis approach for froth flotation. Specifically, a joint Gabor texture feature based on the Copula model is designed to describe froth images; a rejection sampling technique is developed to generate training sets from the quality distribution of real flotation products, and then an isolation forest-based fault detector is learned; and a fault diagnosis model based on spline regression is developed for root cause identification. Simulation experiments conducted on the historical industry data show that the proposed strategy has better performance than the alternative methods. Thereafter, the entire framework has been tested on a lead-zinc flotation plant in China. Experimental results have demonstrated the effectiveness of the proposed method.

Complex geological conditions such as fault fracture zone will have a significant adverse impact on tunnel engineering, and collapse, large deformation, and other problems are prominent. The research on the large deformation mechanism and control of tunnel crossing fault fracture zone can provide guidance for tunnel safety construction. Based on the Jingzhai tunnel, combined with geological analysis, theoretical research, numerical simulation, and other means, this paper studies and analyzes the large deformation mechanism of the tunnel. The control effect of different advanced reinforcement measures is studied.

Mixed ion–electron conductors have a wide range of important applications in devices relying on mixed ion–electron transport, mainly including conducting polymer composites and ceramics. Herein, we...

In this paper, we use the analytic methods, the properties of the sixth-order characters, and the classical Gauss sums to study the computational problems of a certain special sixth residues’ modulo p and give two exact calculating formulas for them.

Trending topic recommendation (TTR) has become a popular social service for social users to obtain interesting topics based on public social content. Due to sensitive privacy, the traditional differential privacy (DP) methods are used to protect social contents. However, these DP methods rely on a fully trusted third party (TTP) without considering protecting the correlations of social keywords, and do not support the privacy-preserving online social content publication. In this article, we propose a novel local DP-based TTR (LDPTR) scheme to perform high-quality online TTR services while achieving local privacy-preserving social contents. Specifically, LDPTR first clusters the social keywords with high correlations into noisy graph classes based on a graph-based LDP (GLDP) algorithm for ensuring the keyword correlation privacy. Second, LDPTR adopts a novel mechanism called $\epsilon _{2}$ -compressive sensing indistinguishability (CSI) to generate noisy social topics, thereby preventing the user-linkage attack and breaking the curse of high-dimensional local differential privacy (LDP). Then, a dynamic graph-based CSI (DGCSI) algorithm is proposed to protect the online social content privacy while ensuring data usability. Furthermore, LDPTR calculates the trending topics of interest with high burstiness by using a topic distribution similarity model-based topic burstiness (TMTB) algorithm, which achieves effective TTR services. Our LDPTR scheme satisfies $\epsilon $ -LDP by detailed security analysis. Extensive experiments over two real-world data sets show that the proposed LDPTR gets high data utility while ensuring high-level privacy.

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere with wireless signal propagation, that manifested as unique patterns for activity recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carries substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual’s activities. Since only recording activities of limited subjects in a certain speed and scale, recent works commonly have a moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi-based human activity recognition system that synthesizes variant activities data through eight channel state information (CSI) transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep-learning model that caters to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data.

Elucidating the reaction mechanism of hydrazine oxidation reaction (HzOR) over carbon-based catalysts is highly propitious for the rational design of novel electrocatalysts for HzOR. In present work, isolated first-row transition metal atoms have been coordinated with N atoms on the graphite layers of carbon nanotubes via a M-N4-C configuration (MSA/CNT, M=Fe, Co and Ni). The HzOR over the three single atom catalysts follows a predominant 4-electron reaction pathway to emit N2 and a negligible 1-electron pathway to emit trace of NH3, while their electrocatalytic activity for HzOR is dominated by the absorption energy of N2H4 on them. Furthermore, FeSA/CNT reverses the passivation effect on Fe/C and shows superior performance than CoSA/CNT and NiSA/CNT with a recorded high mass activity for HzOR due to the higher electronic charge of Fe over Co and Ni in the M-N4-C configuration and the lowest absorption energy of N2H4 on FeSA/CNT among the three MSA/CNT catalysts.

This study investigated the optimal design of a capillary heat exchanger device for the heat pump system and its innovative engineering application in a building. The overall aim was to use a capillary heat exchanger to obtain energy in coastal areas for promoting renewable energy in low-carbon building design. Initially, the main factors affecting the efficiency of the capillary heat exchanger were identified, a mathematical model was then established to analyse the heat transfer process. The analysis showed the flow rate and the capillary length are the key factors affecting the efficiency of the capillary heat exchanger. Secondly, to optimize the structural design of the capillary heat exchanger, the heat energy transfer is calculated with different lengths of the capillary under various flow rates in summer and winter conditions, respectively. Thirdly, a typical building is selected to analyse the application of the capillary heat exchanger for extracting energy in the coastal area. The results show the performance of the selected capillary heat exchanger heat pump system, in winter, the heat energy transfer rate is 60 W/m2 when the seawater temperature is 3.7 °C; in summer, the heat energy transfer rate is 150 W/m2 when the seawater temperature is 24.6 °C. Finally, the above field test results were examined using a numerical simulation model, the test and simulation results agree with each other quite well. This paper is conducive in promoting the development of the capillary heat exchanger heat pump as an innovative sustainable technology for net-zero energy and low carbon buildings using renewable energy in coastal areas. Practical application: A recently proposed capillary heat exchanger is used as an energy extraction and utilisation device to obtain energy in coastal areas for promoting renewable energy in low-carbon building design. This paper explores the application of a capillary heat exchanger as both cold and heat sources for application in typical low-rise buildings. The analysis of the heat energy transfer rate of a typical low-rise building located in a coastal area in summer and winter provides guidance for the application of capillary heat exchangers.

In order to study the influence of travelers’ self-adaptive adjustment behavior on transportation network under the assumption of bounded rationality, using cellular automaton to discretize the selection model under the analytic paradigm in the existing research, abstract each cell into a traveler, and describe the traveler characteristics with finite rationality characteristics through the travel risk attitude and travel generalized cost budget. Cellular automata and cumulative prospect theory is used to establish the travel route choice model, giving the dynamic evolution process of different reference points for travelers and taking the actual regional transportation network of Sichuan Tibet region in China as the study object, analyzes the impact of bounded rational travel behavior on route choice. The model and algorithm proposed in our study can not only guide the transportation organization of Sichuan Tibet region, but also provide theoretical support for the implementation of regional transportation planning and traffic control scheme in the future.

With the development of virtualization technology, it is practical and necessary to integrate virtual machine software into embedded systems. I/O scheduling is important for embedded systems, because embedded systems always face different situations and their requests have more diversity on the requirement of real-time and importance. However, the semantic information associated with the I/O data is completely lost when crossing the virtualized I/O software stack. Here, we present an I/O scheduling framework to connect the semantic gap between the application threads in virtual machines and hardware schedulers in the host machine. Therefore, the details for the I/O request can be passed through the layers of the software stack and each layer can get the specific information about the device environment. Also, various scheduling points have been provided to implement different I/O strategies. Our framework was implemented based on Linux operating system, KVM, QEMU and virtio protocol. A prototype scheduler, Orthrus , was implemented to evaluate the effectiveness of the framework. Comprehensive experiments were conducted and the results show that our framework can guarantee the real-time requirements, and reserve more system resources for critical tasks, with negligible memory consumption and throughput overhead.

Heteropoly acids have been employed to increase the proton conductivity of phosphoric acid (PA) doped polymer membranes for high temperature polymer electrolyte membrane fuel cells (HT-PEMFCs). In this work, we develop a new composite membrane based on phosphotungstic acid (PWA) doped polyethersulfone-polyvinylpyrrolidone (PES-PVP) matrix, forming PWA/PES-PVP composite membrane for HT-PEMFCs. The homogeneous distribution of PWA on the PES-PVP membrane enhances its mechanical strength. In addition, there is a strong interaction between PWA and PA that is confirmed experimentally by the attenuated total reflectance Fourier Transform Infrared spectroscopy and semi-empirical quantum mechanics calculation. This enhances not only the PA uptake but also the proton conductivity of the PWA/PES-PVP composite membrane. 1H nuclear magnetic resonance spectroscopy results elucidate that the high proton conductivity of the PA doped PWA/PES-PVP membranes is due to their higher proton content and mobility compared to the pristine PA doped PES-PVP membrane. The best results are observed on the PES-PVP composite membrane with addition of 5 wt% PWA, reaching proton conductivity of 1.44 × 10−1 S cm−1 and a peak power density of 416 mW cm−2 at 160 °C and anhydrous conditions. PWA additives increase the proton conductivity and cell performance, demonstrating significantly positive effects on the acid-base composite membranes for high temperature polymer electrolyte membrane fuel cell applications.

To investigate the rock particle breakage behavior subject to impact load, a series of split Hopkinson pressure bar (SHPB) tests were conducted on selected Nishan rock and Shoushan rock particles within gravel size range at different strain rates. Both rock particles exhibit a typical strain softening macrobehavior and their dynamic peak strengths increase with strain rate. A dynamic increasing factor (DIF) is used to describe this strain rate effect. Based on the SHPB test, a constitutive model is proposed for the two types of rock particles to describe their dynamic stress–strain behavior considering strain rate effect and damaging effect. Grain size distribution of fractured rock particles was analyzed, which reflected the fragmentation characteristics quantitatively. A fractal theory was used to describe the fragmentation characteristics and dynamic strength of both types of rock particles. Dynamic strength was found to vary differently with fractal dimension for Nishan rock and Shoushan rock.

Graphene has extensive application in various prospects due to its good stability, high conductivity, and large specific surface area. However, the dispersion of graphene in solvents can significan...

Recent works have shown that acoustic signals can be leveraged to perform respiration monitoring with high accuracy and low energy consumption. Since smartphones, smart speakers, and many other IoT devices are already equipped with microphones and speakers, it is convenient to implement the acoustic sensing solutions on those devices. However, the existing technologies require the speaker to transmit certain ultrasonic signals to detect respiration. Although these signals are inaudible to adults, they are audible to children and pets and they may even have negative impacts on plants. In this article, instead of using ultrasonic signals, we are trying to leverage audible signals in daily lives, e.g., music or broadcasting audios, to detect human respiration. We design a respiration detection system which derives the respiration rate by continuously estimates the channel impulse response (CIR) using music and broadcast signals. We study the intersymbol interference (ISI) brought by the randomness of music and broadcast signal and give our strategy to minimize the interference. We also propose several techniques to resolve some practical issues, such as the multipath effect and sampling frequency offset between the speaker and the microphone. Extensive experiments are conducted to demonstrate the feasibility of our system. The result shows that our system can achieve high respiration detection accuracy with the mean error of less than 0.5 BPM when different audio signals are used.

The application of the Assembly–Disassembly–Organization–Reassembly (ADOR) protocol to the synthesis of germanosilicate zeolites has become a major milestone in material design by enabling the prep...

The effect of cucurbit[7]uril (CB[7]) on fluorescence properties and biocompatibility of the bis-viologen biphenyl molecule (BPV22+) was investigated by using 1H NMR spectroscopy, fluorescence emission titration, and in vitro cytotoxicity experiments. CB[7] can be combined with BPV22+ in a stoichiometric ratio of 1:1 and 2:1. After the formation of host-guest complex, the fluorescence emission intensity of BPV22+ increased significantly, and the emission spectrum blue shifted. Meanwhile, the host-guest complexes showed better biocompatibility than BPV22+ in cell cytotoxicity studies. Results of this paper lay a foundation for the development of host-guest type of fluorescent probes, biological imaging and so forth.

Achieving moldable energy storage devices will make it possible to power wearable electronics by adapting to body contours for wear comfort. Herein, we present a new approach to create hierarchically structured electrodes that enable supercapacitors to retain their capacity under mechanical deformation. The electrodes are made by first growing vertical graphene nanosheets (VGNs) and then depositing manganese oxide (MnO2) on ductile nickel wires. Two such electrodes are made into a moldable supercapacitor using a solid-state electrolyte containing carboxymethylcellulose and sodium sulfate. This foldable supercapacitor achieves a high areal capacitance up to 56 mF cm−2, areal energy density of 7.7 μWh cm−2, and areal power density of 5 mWh cm−2. These exceptional properties originate from the synergy between VGNs and MnO2, where the highly porous VGNs serve two critical functions: a mechanically robust platform of large surface area allowing high mass loading deposition of pseudocapacitive MnO2 and an interconnected conductive network for efficient electron/ion transport. Fiber-shaped supercapacitors made from these electrodes can be molded into different shapes by bending and twisting with little performance loss. The promising results presented in this study provide a new route for fabricating high-performance moldable energy storage devices for wearable electronics and wireless electronic skins.