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For Inertial Navigation System (INS)/Celestial Navigation System (CNS)/Global Navigation Satellite System (GNSS) integrated navigation system of the missile, the performance of data fusion algorithms based on the Cubature Kalman Filter (CKF) is seriously degraded when there are non-Gaussian noise and process-modeling errors in the system model. Therefore, a novel method is proposed, which is called Optimal Data Fusion algorithm based on the Adaptive Fading maximum Correntropy generalized high-degree CKF (AFCCKF-ODF). First, the Adaptive Fading maximum Correntropy generalized high-degree CKF (AFCCKF) is proposed and used as the local filter for the INS/GNSS and INS/CNS subsystems to improve the robustness of local state estimation. Then, the local state estimation is fused based on the minimum variance principle and high-degree cubature criterion to get the globally optimal state. Finally, the experimental results verify that the proposed algorithm can significantly improve the robustness of the missile-borne INS/CNS/GNSS integrated navigation system to non-Gaussian noise and process modeling error and obtain the global optimal navigation information.

International audience; Recently proposed adaptive platooning strategies for connected automated vehicles are able to address uncertain parameters of the vehicles in the platoon (uncertain driveline time constants), but are limited to acyclic interaction like lookahead interaction. This restrains from augmenting platooning protocols with synchronized merging maneuvers, where cyclic communication is needed and creates algebraic loops that require well posedness of the inputs. We propose an adaptive protocol for synchronized merging in the cyclic communication scenario. The protocol exploits a set of adaptive control strategies, designed to cope with uncertain driveline time constants. Well-posedness of the control inputs is proven in a distributed way (using information from neighboring vehicles) in spite of uncertainty and cyclic communication. The proposed strategy is shown in a benchmark merging scenario.

As an eco-friendly thermoelectric material, SnTe has attracted extensive attention. In this study, we use a stepwise strategy to enhance the thermoelectric performance of SnTe. Firstly, AgCl is doped into SnTe to realize band convergence and enlarge the band gap of AgCl-doped SnTe. AgCl-doping also induces dense point defects, strengthens the phonon scattering, and reduces the lattice thermal conductivity. Secondly, Sb is alloyed into AgCl-doped SnTe to further optimize the carrier concentration and simultaneously reduce the lattice thermal conductivity, leading to improved thermoelectric dimensionless figure of merit, ZT. Finally, (Sn0.81Sb0.19Te)0.93(AgCl)0.07 has approached the ZT value as high as ∼0.87 at 773 K, which is 272 % higher than that of pristine SnTe. This study indicates that stepwise AgCl-doping and Sb-alloying can significantly improve thermoelectric performance of SnTe due to synergistic band engineering, carrier concentration optimization and defect engineering.

Acute kidney injury (AKI) is a serious kidney disease without specific medications currently except for expensive dialysis treatment. Some potential drugs are limited due to their high hydrophobicity, poor in vivo stability, low bioavailability and possible adverse effects. Besides, kidney-targeted drugs are not common and small molecules are cleared too quickly to achieve effective drug concentrations in injured kidneys. These problems limit the development of pharmacological therapy for AKI. Nanotherapeutics based on nanotechnology have been proved to be an emerging and promising treatment strategy for AKI, which may solve the pharmacological therapy dilemma. More and more nanotherapeutics with different physicochemical properties are developed to efficiently deliver drugs, increase accumulation and control release of drugs in injury kidneys and also directly as effective antioxidants. Here, we discuss the recent nanotherapeutics applied in the treatment and prevention of AKI with improved effectiveness and few side effects., Graphical abstract Image, graphical abstract

High-performance sulfonated polysulfone (SPSf) mixed-matrix membranes (MMMs) were fabricated via a nonsolvent-induced phase separation (NIPS) method using zeolitic imidazolate frameworks-67 (ZIF-67) as a crosslinker. Acid-base crosslinking occurred between the sulfonic acid groups of SPSf and the tertiary amine groups of the embedded ZIF-67, which improved the dispersion of ZIF-67 and simultaneously improved the membrane strzcture and permselectivity. The dispersion of ZIF-67 in the MMMs and the acid-base crosslinking reaction were verified by energy-dispersive X-ray spectroscopy (EDX), X-ray diffractometry (XRD), Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). The pore structure analysis of MMMs indicated that filling ZIF-67 into SPSf enhanced the average surface pore sizes, surface porosities and more micropore in cross-sections. The crossflow filtrations showed the MMMs have higher pure water fluxes (57 to 111 L m−2 h−1) than the SPSf membrane (55 L m−2 h−1) but also higher bovine serum albumin (BSA) rejection rate of 93.9–95.8%, a model protein foulant. The MMMs showed a higher water contact angle than the SPSf membrane due to the addition of hydrophobic ZIF-67 and acid-base crosslinking, and also maintained high thermal stability evidenced by the thermogravimetric analysis (TGA) results. At the optimal ZIF-67 concentration of 0.3 wt%, the water flux of the SPSf-Z67-0.3 membrane was 82 L m−2 h−1 with a high BSA rejection rate of 95.3% at 0.1 MPa and better antifouling performance (FRR = 70%). Highlights An SPSf-matrix ultrafiltration (UF) membrane was developed by blending with hydrophobic ZIF-67.; ZIF-67 has good dispersibility and compatibility with SPSf of weak acid-base interaction.; Simultaneous enhancement in the permeability and selectivity of the MMMs.

Perception of the environment is the prerequisite for the realization of unmanned driving. Correctly detecting the lane line and navigating the vehicle is the key technology in unmanned driving. This paper mainly aims at the low accuracy of the traditional lane detection algorithm in the complex environment such as night and rain, and proposes a lane detection and recognition method based on dynamic region of interest (ROI) selection and firefly algorithm. First, perform distortion correction on the captured lane image, gray scale and blur image preprocessing, and then determine the height of the ROI based on the vanishing point, and dynamically adjust the width of the ROI based on the recognition of the lane line in the previous frame to achieve dynamic ROI adjust to eliminate interference factors and reduce the amount of calculation to the greatest extent. Finally, to solve the problem that the canny operator is sensitive to noise in the traditional method, an improved firefly algorithm is proposed for edge detection. The slope-limited progressive probability Hough transform is used to detect the straight line of the ROI divided into several boxes, and the least square method is used to fit several detected straight lines to extract lane lines. Experimental results show that the method we proposed can achieve lane line detection well in complex environments, with an average accuracy rate of 96.37%, and an average detection time per frame of only 118 ms.

In Internet of Vehicles (IoV), frequent link switching and channel interference will increase transmission delay and decrease network throughput. Unmanned Aerial Vehicles (UAVs) can avoid shadowing effect and provide line-of-sight communication to enhance IoV performance, which can help reduce communication handovers. This study introduces UAVs to assist ground vehicular network for collaborative communication and focuses on relay selection to achieve effective data dissemination. The relay selection problem is formulated as a multi-objective optimization problem related to link transmission rate, communication handovers, and node transmit power. To solve the problem, we propose an interference-aware relay selection mechanism, which is a distributed strategy to make decisions at the sender. The mechanism applies the dual decomposition method to decompose the problem into two convex optimization subproblems. The first subproblem jointly optimizes communication handovers and link transmission rate while the second subproblem optimizes node transmit power under the premise of ensuring link reliability. Simulations verify the effectiveness of the proposed relay selection mechanism in terms of data delivery ratio, average end-to-end delay and network throughput.

In the last few years, the deep learning methods, especially the residual neural network, have achieved impressive performance in remote sensing image recognition tasks. However, there are still specific problems that need to be addressed. It is well known that the first several layers of the network provide much discriminative information, and the ResNet reduces the size of the feature map so quickly that it failed to fully learn the information beneficial to classification in the early stage. Second, insufficient labeling data in remote sensing database may easily lead to overfitting and affect the final classification accuracy. Third, the optimal results cannot be achieved by relying solely on transfer learning. To overcome the problems mentioned earlier, we propose an enhanced residual neural network (ERNet) to improve the classification performance on remote sensing images. We moderately broadened the first several layers of the network, changed the size of the convolution filters, and made it learn more information of image features. Second, we add dropout layer to each residual unit of the proposed network to improve the accuracy and generalization power of ERNet. Finally, an ensemble of learning methods based on ERNet was introduced to improve the classification performance by fusing features of other baseline methods. Extensive experimental results on several benchmark data sets of remote sensing images demonstrate the superior performance of our proposed algorithm.

NiMo bimetallic catalysts were prepared by a solid reaction method. On the NiMo catalyst, the selective liquid phase hydrogenation of nitrobenzene to aniline was achieved in slurry bubble mode. And the high yields (98.9%) were obtained under the conditions of 80 °C, solvent-free and atmospheric pressure. The effect of Mo on the catalytic behavior of Ni based catalyst was investigated. The characterization displayed that the inclusion of Mo could improve the specific surface area and pore volume, and the solid reaction method made metal Mo enrichment on the surface of catalyst. These two aspects should be responsible for excellent catalytic performance of NiMo catalyst. In sum, we described a simple and efficient NiMo catalyst and provided a facile and green procedure for liquid phase hydrogenation of nitrobenzene to aniline.

Commonly, there are two different types of signal tracking methods in Global Navigation Satellite System (GNSS) receivers: Scalar Tracking (ST) and Vector Tracking (VT). In ST, each tracking channel works independently and generates the measurements for Position, Velocity and Time (PVT) estimation. On the contrary, VT accomplishes the signal tracking and PVT solutions estimation together using a Kalman filter (KF). In this manner, mutual aiding between these channels are obtained, which contributes to the superior performance of VT than ST. KF is the method in the manner of weighted average of predicted state and the measurements derived state. Two drawbacks restrict the KF performance, (1) in KF, correlation between current state and all the past states is ignored, only adjacent state is included in the current state estimation through state transformation matrix; (2) linear KF might degrade the estimation due to the nonlinearity of the VT measurement model. In this brief, a graph optimization (GO) method is employed to substitute the KF to estimate the navigation solutions in VT. In the GO-VT, the measurements and the state transformation from past epochs are all regarded as constraints to optimize the states estimation. With the iterations during the optimization, the GO-VT is expected to alleviate the influence of the measurement model nonlinearity on the state estimation. A field test was carried out for assessing the performance of the GO-VT, its superior position accuracy compared with that from the KF supported that the GO could enhance the VT.

The rheometer was used to test the rheology of cement paste with and without nano-silica, and the fluidity and free water content were tested, the isothermal calorimetry of cement and C3S was tested and hydration products were monitored using Raman spectroscopy. A modified Bingham model was used to describe the rheological properties of the paste, and the effects of nano-silica with different particle sizes on rheological parameters including yield stress, plastic viscosity and thixotropy were derived. The results show that the fresh state properties of the cement paste vary with the particle size of nano-silica at the same dosage. The decrease in the particle size of nano-silica reduces the fluidity and free water content, but increases the yield stress and plastic viscosity. The flocculation and surface water absorption of nano-silica reduce the free water content at 5 minutes, which results in a decrease in flow diameter and an increase in yield stress. During the time evolution, the heat of hydration and Raman spectroscopy show that nano-silica with smaller particle size has higher nucleation and pozzolanic effect, that is, with the same dosage, nano-silica with small particle size provides more nucleation sites to form calcium silicate hydrate (C-S-H) seeds and to promote the hydration of C3S and the formation of C-S-H.

Thin plates are widely utilized in aircraft, shipbuilding, and automotive industries to meet the requirements of lightweight components. Especially in modern shipbuilding, the thin plate structures not only meet the economic requirements of shipbuilding but also meet the strength and rigidity requirements of the ship. However, a thin plate is less stable and prone to destabilizing deformation in the welding process, which seriously affects the accuracy and performance of the thin plate welding structure. Therefore, it is beneficial to predict welding deformation and residual stress before welding. In this paper, a thin plate welding deformation and residual stress prediction model based on particle swarm optimization (PSO) and grid search(GS) improved support vector regression (PSO-GS-SVR) is established. The welding speed, welding current, welding voltage, and plate thickness are taken as input parameters of the improved support vector regression model, while longitudinal and transverse deformation and residual stress are taken as corresponding outputs. To improve the prediction accuracy of the support vector regression model, particle swarm optimization and grid search are used to optimize the parameters. The results show that the improved support regression model can accurately evaluate the deformation and residual stress of butt welding and has important engineering guiding significance.

Formamidinium lead iodide (FAPbI3) is a category of perovskite material with an ideal band gap and high thermal stability, which can be efficiently prepared by two-step spin-coating. Spin-coating organic salts and transforming intermediate phase at the second step involves a components' reaction and state transition, thus playing a crucial role in the film quality formed afterward and optoelectronic properties of the fabricated perovskite solar cells (PSCs). In this paper, a cooling stage (CO) is used to post-treat the as-prepared precursor after the second spin-coating. The procedure of intermediate phase transferring to other state is found to be retarded; hence, the appearing velocity of perovskite nucleation is decreased. As a result, components react more adequately and larger perovskite grains with fewer defects are obtained; charge transport as well as carrier recommbination behaviors are therefore optimized. The PSCs based on the CO process achieved a champion power conversion efficiency (PCE) of 21.51% with enhanced stability. Moreover, CO treatment is observed to be beneficial for improving the film quality of perovskite in large-area preparation, which we anticipate can be further extended to the commercialized application of PSCs.

The conversion efficiencies reported for Tin (Sn) halide-based perovskite solar cells (PSCs) fall a large gap behind those of lead halide-based PSCs, mainly because of poor film quality of the former. Here we report an efficient strategy based on a simple secondary crystallization growth (SCG) technique to improve film quality for tin halide-based PSCs by applying a series of functional amine chlorides on the perovskite surface. They were discovered to enhance the film crystallinity and suppress the oxidation of Sn2+ remarkably, hence reduce trap state density and non-irradiative recombination in the absorber films. Furthermore, the SCG film holds the band levels matching better with carrier transport layers and herein favoring charge extraction at the device interfaces. Consequently, a champion device efficiency of 8.07% was achieved along with significant enhancements in Voc and Jsc, in contrast to 5.35% of the control device value. Moreover, the SCG film-based devices also exhibit superior stability comparing with the control one. This work explicitly paves a novel and general strategy for developing high performance lead-free PSCs.

The relationship between the structural connectivity (SC) of human brain networks and their functional connectivity (FC) is of immense importance in understanding brain cognition and disorders and has gained significant attention in the neuroscience over the past decade. However, the underlying mechanism of how SC gives rise to the whole pattern of FC especially the negative correlations still remains poorly understood. In this paper, we propose a model that can accurately simulate the resting-state human brain functional correlations based on hypergraph Laplacian, including the negative correlations. We firstly assume that, for each brain region, there are some links showing positive correlations and some other links showing negative correlations. Then we derive a hypergraph model with the two matrices indicating positive and negative correlations respectively using the SC of human brain network. We apply the model to two empirical datasets and the results show that the simulated FC can achieve higher Pearson correlations with the empirical FC, outperforming the state-of-art graph diffusion (GD) model.

With the excellent structural design, rotary triboelectric nanogenerator (R-TENG) is suitable for harvesting mechanical energy such as wind energy and water energy to build a self-powered electrochemical system for environmental science. The electrochemical performance has been greatly improved by using the pulsed direct-current (PDC) output of a TENG; however, a full-wave PDC (FW-PDC) is hardly realized in R-TENG devices due to existence of phase superposition. Here, a R-TENG with FW-PDC output is reported to perform a self-powered electro-Fenton system for enhancing the removal efficiency of levofloxacin (OFL). By adjusting the rotation center angle ratio between each rotator and stator unit, the phase superposition of R-TENG caused by multiple parallel electrodes can be effectively eliminated, thus achieving the desired FW-PDC output. Because of the reduced electrode passivation effect, the removal efficiency of OFL is improved by 30% under equal electric charges through using the designed R-TENG with FW-PDC output compared to traditional R-TENG. This study provides a promising methodology to improve the performance of self-powered electrochemical process for treating environment pollutions.

Gray-black kaolinitic claystones of industrial value are abundant in Upper Carboniferous–Lower Permian coal-bearing strata of the Datong Coalfield of northern China. The main types are tonsteins and cryptocrystalline kaolinitic claystones, distinguished by the thinness and greater crystallinity of kaolinite in the former and by the presence of detrital illite and authigenic pyrite in the latter. In order to determine the formation history of these two types of kaolinitic claystone, the petrological, mineralogical, and geochemical characteristics of borehole samples from the Upper Carboniferous Taiyuan Formation which comprises siliciclastics and coal seams deposited in a coastal environment, were analyzed. In addition to kaolinite, the claystones contain subordinate illite, quartz, pyrite, anatase, feldspar, siderite, and calcite. The tonsteins and cryptocrystalline kaolinitic claystones have different sources, as shown by petrographic data, elemental ratios, and chondrite-normalized rare earth element (REE) patterns. The volcanic origin of the tonsteins is revealed by an abundance of volcanic quartz and vitric fragments as well as Al2O3/TiO2, Zr/Hf, and Nb/Ta ratios consistent with a felsic igneous source. Their REE fraction was derived from feldspars or micas of the parent rocks, and the fraction decreased with alteration of these minerals to kaolinite. The sedimentary origin of the cryptocrystalline kaolinitic claystones is revealed by an abundance of detrital quartz and illite grains derived from either granite or sedimentary upper crust, and by the total REE contents (ΣREE) and (La/Yb)N values which are consistent with granitic material. Their depositional environment was in a transitional (coastal) setting (as shown by intermediate Sr/Ba ratios) hosting an open acidic hydrologic system (as shown by high chemical index of alteration (CIA) values indicative of intensive chemical weathering) that was suboxic to anoxic (as shown by high U/Th ratios and trace-metal enrichment factors). The present chemistry of these claystones was thus controlled by a combination of parent rock type and diagenetic alteration.

In the conventional GNSS receiver, pseudo-range and pseudo-range rates measurements are generated through carrier signal tracking and code tracking respectively. Then, Least Square method (LSM) or Kalman filter (KF) is utilized to estimate the position and velocity (PV) regarding the pseudo-range and pseudo-range rates as the measurements. However, the LSM ignores the fact that PV information is time-correlated. Smoother positioning results can be obtained considering time-correlated characteristic of the PV information. In KF, the PV information is estimated in a weighting manner between the prediction and measurements updated states, smoother positioning results are obtained since state transformation constraints are included. However, in KF, abundant historical information is dropped out and excluded while estimating the state at current epoch. In this letter, a Graph Optimization (GO) method based GNSS position estimation was proposed and implemented. State transformation and measurements were all regarded as the constraints to optimize the states estimation in the GO method. Historical states and measurements were utilized to estimate state at current epoch in the GO framework. Superior position results were expected compared with that from the LSM and KF. In this study, a field was carried out, position results from LSM, KF and GO method were presented, compared and analyzed. With the iterative process and historical information included in the GO, the field test results demonstrated that GO method could generate better position results compared with that from the LSM and KF methods.

In this paper, an attentive cross-modal fusion (ACMF) network is proposed for RGB-D salient object detection. The proposed method selectively fuses features in a cross-modal manner and uses a fusion refinement module to fuse output features from different resolutions. Our attentive cross-modal fusion network is built based on residual attention. In each level of ResNet output, both the RGB and depth features are turned into an identity map and a weighted attention map. The identity map is reweighted by the attention map of the paired modality. Moreover, the lower level features with higher resolution are adopted to refine the boundary of detected targets. The entire architecture can be trained end-to-end. The proposed ACMF is compared with state-of-the-art methods on eight recent datasets. The results demonstrate that our model can achieve advanced performance on RGB-D salient object detection.

Synthetic aperture radar (SAR), with all-day and all-weather observation capabilities, can capture the phenology of crops with short growth cycles to improve land cover classification results. The present study carried out a land cover classification using multitemporal RADARSAT-2 fully polarimetric SAR (PolSAR) images that are 24 days apart. The objectives of this study were 1) examining the land cover classification capacity of multitemporal fully PolSAR data processed with multiple polarimetric decomposition methods, 2) investigating the contribution of multi-type polarimetric decomposition methods to multitemporal PolSAR image classification, 3) and determining optimal image acquisition dates and polarimetric parameters for land cover classification. Overall accuracy and kappa coefficient attained using the multitemporal PolSAR data were 96.55% and 0.96, respectively, which were improved by as much as 16.77% and 0.20, respectively, compared with those obtained with a single scene. Compared with the multitemporal PolSAR image classification using coherency matrices alone, the use of polarimetric decomposition methods improved overall accuracy and kappa value by 2.22% and 0.03, respectively. Using decision tree algorithms for feature selection, we found that April 14, May 8, June 1, and June 25 and Pauli, Cloude, Neumann3, An&Yang4, Freeman3, Barnes2, and MCSM5 decomposition methods were optimal for the land cover classification.

The demand for large energy storage systems is consecutively increasing, which requires low-cost and renewable batteries technologies with sustainable performance. Potassium, as the nearest element to sodium and lithium in the IA group of the periodic table, possesses excellent superiorities in electrochemical energy storage devices. Correspondingly, numerous electrode materials with excellent stability and capability have been developed for rechargeable potassium-ion batteries (KIBs). In this review, recent results of electrode materials concerning different types of electrolytes (organic electrolyte, aqueous electrolyte, and all-solid-state electrolyte) for rechargeable KIBs are summarized. An overall content of ongoing cathode/anode electrode materials utilized in rechargeable KIBs with organic electrolyte is reviewed. The topic focuses on employing organic compounds, Prussian blue analogues, layered oxides, and polyanionic compounds as cathode. In addition, different anode materials such as intercalation compounds, alloy compounds, conversion compounds, and organic compounds were discussed. Furthermore, the current research progress of other potassium-based electrochemical energy storage devices (KEES) with low costs and high specific energy densities, such as potassium-ion hybrid capacitors (KIHCs) and potassium dual-ion batteries (KDIBs), are also summarized. Finally, we pointed out the current challenges and future directions of KEES.

Two novel heteroleptic iridium complexes containing 4-phenylquinazoline (pqz) as a cyclometalating ligand, namely Ir1 and Ir2, are designed and synthesized. Methoxy groups are incorporated into the 6- and 7-sites of pqz rings to tune the physical properties and the emission performance of the complexes. The introduction of methoxy groups in Ir1 and Ir2 causes a slight blue shift by 3–5 nm relative to the reference complex Ir(pqz)2(acac) (630 nm), and moderate photoluminescence quantum yields of 0.35 and 0.38 and short lifetimes of 0.79 and 0.64 μs, respectively, are obtained. Ir1 and Ir2 are used as doped emitters to fabricate phosphorescence organic light-emitting diodes and pure red electroluminescence (EL) is achieved. In comparison with the reference complex Ir(pqz)2(acac), dramatically improved EL performance with color purity and efficiencies are realized for both Ir1 and Ir2. In particular, the Ir2 device exhibits a maximum external quantum efficiency of 26.22% with Commission Internationale de l’Eclairage (CIE) coordinates of (0.65, 0.34), which is close to the best efficiencies reported for other pqz based red iridium phosphors but advantageous in terms of a simple device structure with a single emitting layer.

Very recently, the centimeter-scale MoSi2N4 monolayer was synthesized experimentally and exhibited a semiconducting nature with high mobility (Hong et al., Science, 2020, 369, 670-674). Here, we show that MoSi2N4 and its analogues, MoSi2P4 and MoSi2As4, are potential two-dimensional (2D) materials for valleytronics based on first-principles calculations. We demonstrate that the intrinsic inversion symmetry breaking and strong spin-orbital coupling lead to the remarkable spin-valley coupling in the inequivalent valleys at K and K' points, which result in not only the valley-contrasting transport properties, but also the spin and valley coupled optical selection rules. Moreover, the in-plane strain can tune the bandgaps and spin splitting or even induce an indirect-to-direct bandgap transition for promising application in the strain-tunable valleytronics. We find that the valley polarization can be generated by doping magnetic element. Our findings offer theoretical insight into the exotic physical properties of novel MoSi2N4-family materials beyond transition metal dichalcogenides.

An excellent catalyst for hydrodesulfurization, aromatization and olefin hydrogenation of FCC gasoline, is designed in this paper.

Telomere single-stranded DNA-binding proteins bind to the terminal single-stranded DNA of telomeres, maintaining and protecting the chromosomal end in eukaryotes. This paper focuses on the protective mechanism of single-stranded DNA-binding proteins in plant telomeres. This review summarizes the roles of plant single-stranded DNA-binding proteins and their influence on telomere length and telomerase. This review provides insights into the mechanism and development of single-stranded DNA-binding proteins in plants.

Owing to the intrinsically good near-room-temperature thermoelectric performance, β-Ag2Se has been considered as a promising alternative to n-type Bi2Te3 thermoelectric materials. Herein, we develop an energy- and time-efficient wet mechanical alloying and spark plasma sintering method to prepare porous β-Ag2Se with hierarchical structures including high-density pores, a metastable phase, nanosized grains, semi-coherent grain boundaries, high-density dislocations, and localized strains, leading to an ultralow lattice thermal conductivity of ∼0.35 W m-1 K-1 at 300 K. A relatively high carrier mobility is obtained by adjusting the sintering temperature to obtain pores with an average size of ∼260 nm, therefore resulting in a figure of merit, zT, of ∼0.7 at 300 K and ∼0.9 at 390 K. The single parabolic band model predicts that zT of such porous β-Ag2Se can reach ∼1.1 at 300 K if the carrier concentration can be tuned to ∼1 × 1018 cm-3, suggesting that β-Ag2Se can be a competitive candidate for room-temperature thermoelectric applications.

The work presented in this paper concerns a switching-based control formulation for multi-intersection and multiphase traffic light systems. A macroscopic traffic flow modeling approach is first presented, which is instrumental to the development of a model-based and switching-based optimization method for traffic signal operation, in the framework of adaptive dynamic programming (ADP). The main advantage of the switching-based formulation is its capability to determine both 'when' to switch and 'which' mode to switch on without the need to use the cycle-based average flow approximation typical of state-of-the-art formulations. In addition, the framework can handle different cycle times across intersections without the need for synchronization constraints and, moreover, minimum dwell-time constraints can be directly enforced to comply with minimum green/red times in each phase. The simulation experiments on a multi-intersection and multiphase traffic light systems are presented to show the effectiveness of the method.

Two-Phase Closed Thermosyphons (TPCTs) is a high-efficiency heat transfer technology being widely utilized in the fields of built and solar energy exploitations. In present work, a 2-D steady-state model is innovatively proposed to investigate thermal performance and flow dynamics of a single TPCT containing nanofluids. Conservation equations for mass, momentum, and energy were fully solved by the dichotomy algorithm. The effects of input powers, nanoparticles materials (Al2O3, Fe2O3 and Cu), and concentration levels (φ = 0–12 wt%) on the thermodynamics and entropy generation of the thermosyphon were numerically investigated and the results were evaluated through those from the pure water. A substantial change in the liquid film thickness, flow velocity profile, interfacial shear force, local heat transfer coefficient, temperature distribution, entropy generation rate and thermal resistance were subsequently obtained when using a nanofluid. Our numerical results revealed that nanofluids in thermosyphon could effectively enhance thermal performance by decreasing the evaporator temperature, thereby reducing overall entropy generation by about 41%, 32% and 29% for Cu, Fe2O3 and Al2O3 in comparison with pure water, respectively. Additionally, entropy generation significantly decays when nanoparticle concentration levels promote. Numerical results further indicate that the flow resistance of working fluid increases, maximum friction entropy generation increases by approximately 36.37%, 15.15%, and 9.09% for Cu, Fe2O3, Al2O3 of φ = 9 wt%, respectively. Moreover, the existence of an optimum concentration level for nanoparticles in maximizing the heat transfer limit was theoretically achieved for all nanofluids. Current results agreed well with experimental data within average deviation being no more than 10%. In conclusions, results suggest that TPCT-solar collect using nanofluid has a low simple payback period to absorb solar radiation to convert thermal energy compared to the conventional one charged with pure water.

With the booming demand of indoor position information, Wireless signals (Wi-Fi, Bluetooth, Ultra-Wide-Band etc.) are investigated to construct Indoor Positioning System (IPS). Among these wireless signals, UWB (Ultra-Wide-Band) is recognized as the most promising technology to construct IPS with decimeter-level positioning accuracy. However, there are various objects in indoor environments, UWB signals might be reflected by these surrounding objects. These None-Line-Of-Sight (NLOS) signals will induce additional errors to the distance measurements between the anchor and agent. Therefore, NLOS/LOS (Line-Of-Sight) signals classification should be carried out for identifying the NLOS reception. In UWB based IPS, the distance information is extracted through the Channel Impulse Response (CIR) waveforms. NLOS reception will lead to different CIR waveforms compared with that from the LOS signal, this difference could be characterized for NLOS/LOS signals classification. However, the hardware circuits, signal transmitting path and the NLOS reception will bring about noises to the CIR waveforms, which might influence the signal type identification. Motivated by this problem, in this letter, a reversible transformation method was proposed for de-nosing the CIR data, and a Conventional Neural Network (CNN) was employed to identify the NLOS signal. Raw and de-noised CIR dataset were both input to the CNN for assessing the effect of the de-noising method. With dataset collected from seven different sites, the results showed that the reversible transformation method was able to increase the Signal Noise Ratio (SNR) values of these CIR datasets. After the de-noising, the CNN performed higher classification accuracy with less convolutional layers. Moreover, the de-noising operation brought about higher NLOS signal identification accuracy increase than that of LOS signals.

It is a great challenge to prepare non-noble metal electrocatalysts toward hydrogen evolution reaction (HER) with large current density. Synergistic electronic and morphological structures of the catalyst have been considered as an effective method to improve the catalytic performance, due to the enhanced intrinsic activity and enlarged accessible active sites. Herein, we present novel ternary Co1−xVxP nano-needle arrays with modulated electronic and morphological structures as an electrocatalyst for highly efficient HER in alkaline solution. The NF@Co1−xVxP catalyst shows a remarkable catalytic ability with low overpotentials of 46 and 226 mV at current densities of 10 and 400 mA cm−2, respectively, as well as a small Tafel slope and superior stability. Combining the experimental and computational study, the excellent catalytic performance was attributed to the improved physical and chemical properties (conductivity and surface activity), large active surface area, and fast reaction kinetics. Furthermore, the assembled Co-V based electrolyzer (NF@Co1−xVx-HNNs(+)||NF@Co1−xVxP(−)) delivers small full-cell voltages of 1.58, 1.75, and 1.92 V at 10, 100, and 300 mA cm−2, respectively. Our findings provide a systematic understanding on the V-incorporation strategy to promote highly efficient ternary electrocatalysts via synergistic control of morphology and electronic structures.

Ultra-Wide-Band (UWB) was recognized as its great potential in constructing accurate indoor position system (IPS). However, indoor environments were full of complex objects, the signals might be reflected by the obstacles. Compared with the Line-Of-Sight (LOS) signal, the signal transmitting path delay contained in None-Line-Of-Sight (NLOS) signal would induce positive distance errors and position errors. Before employing ranging information from the channels to calculate the position, LOS/NLOS classification or identification was necessary for selecting the “clean” channels. In conventional method, features extracted from the UWB channel impulse response (CIR) or some other signal properties were employed as the input vector of the machine learning methods, e.g. Support Vector Machine (SVM), Multi-layer Perception (MLP). Deep learning methods represented by Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) had performed superior performance in dealing with time series data classification. In this pap er, deep learning method CNN-LSTM was employed in the UWB NLOS/LOS signal classification. UWB CIR data was directly input to the CNN-LSTM. CNN was employed for exploring and extracting the features automatically, and then, the CNN outputs were fed into the LSTM for classification. Open source datasets collected from seven different sites were employed in the experiments. Classification accuracy of CNN-LSTM with different settings was compared for analyzing the performance. The results showed that CNN-LSTM obtained stat e-of-art classification performance.

High temperature of the radiator group is harmful to the power system and hydraulic system. In order to improve the heat dissipation performance of the loader, the flow field characteristics of the cooling chamber are analyzed by simulation and heat balance test. Firstly, the mathematical model of heat flow is established. Secondly, the flow field in the cooling chamber under different speeds is simulated based on CFX. And then the influence of fan position and internal flow field distribution on radiator performance is studied. Through the simulation of four different distances, it is concluded that the optimal distance between cooling fan and radiator is 76mm. Finally, the heat balance test system based on wireless sensor network is established, and the simulation results are verified. The test results show that the heat dissipation performance of the whole machine meets the requirements after optimization. In this paper, the proposed scheme can dynamically adjust the heat dissipation performance and make the loader excavator in an efficient and energy-saving operation state.

This study aimed to determine the potential of sodium starch octenylsuccinate (SSOS)-based Pickering emulsions incorporated with cinnamon essential oil (CEO) as a vehicle for delivering active biodegradable films to improve their antimicrobial and antioxidant activites. This study explored the combined effects of mixed oils (CEO and corn oils). Particle size distribution, mean drop size, ζ-potential, polymer dispersity index (PDI), and microstructure were determined to confirm emulsion stability. Well-distributed emulsion droplets maintained under a good condition and encapsulated by SSOS resulted in homogeneous and stable emulsions. The obtained mixed oil-loaded Pickering emulsions were incorporated into the SSOS film preparation, and the morphological, physical, and functional characteristics of the active film were studied. An optimal CEO/corn oil ratio decreased tensile strength but improved elongation at break, water vapor permeability, and oxygen permeability. Fourier transform infrared spectroscopy(FTIR) and scanning electron microscopy(SEM) showed that the SSOS membrane matrix is compatible with Pickering emulsions incorporated with mixed oil. Moreover, a significant decrease was observed in loss of CEO by the Pickering emulsion method. This method exhibited antioxidant and antimicrobial activity against Escherichia coli, Staphylococcus aureus, and Bacillus subtilis, indicating that the encapsulation strategy was efficient to protect against loss of CEO during drying and storage.

Several methods have been proposed to handle uncertainty issues, including process uncertainty and parameter uncertainty, in stochastic-process based degradation modeling and statistical analysis. However, these methods oftentimes do not address the uncertainty issues well under small sample conditions. Hence, due to the powerful ability of evidence theory to describe uncertainty, especially under small sample conditions, an evidence theory based model fusion method is proposed. The candidate models are considered as different evidence sources and give evidences about the evaluated product based on likelihood. Considering the heterogeneous characteristics of the evidences predicted from different candidate models, the reliability degree is introduced to convert the evidences and estimated based on goodness-of-fit. By fusing converted evidences, inferences and estimations of reliability, degradation mean, degradation variance, and mean time to failure are obtained. The effectiveness of the proposed method is verified by previously published degradation datasets and comparing to Bayesian model averaging method and model selecting method. The degradation mean and variance estimations are more precise and more stable compared to the other two methods under small sample conditions. Furthermore, the proposed method can be used to consider the uncertainty issues by belief, plausibility and uncertainty measure, even though under extremely small sample conditions.

To test the hydrodesulfurization (HDS) performance of nickel phosphide (Ni2P), a periodic Ni2P model consisting of multiple sites was established. A density functional theory (DFT) accounting for dispersion corrections was used to calculate the structures, energy changes and energy barriers of hydrogen dissociation and key elementary step of 4,6-dimethyldibenzothiophene (4,6-DMDBT) HDS via direct hydrodesulfurization (DDS) route and hydrogenation (HYD) route. The results show the hydrogen dissociation on one hcp site and one Ni-P bridge site is exothermic and with a moderate activation energy, which means good reductivity of Ni2P surface. The adsorption of 4,6-DMDBT is strongly exothermic than Co(Ni)MoS, thanks to the extra interaction between the molecule and Ni2P surface. The process of C–S bond cleavage of 4,6-DMDBT through DDS route is also exothermic and activated efficiently when the activated hydrogen is in the appropriate position. While for HYD route, it’s a barrierless process with a tiny energy change when forming TH-4,6-DMDBT. Partial cycles of DDS and HYD were also calculated to understand the HDS mechanism. In addition, the HDS process over Ni2P has lots of advantages like adsorption strength, metal cations, accessibility of hydrogen, hydrogen transfer and sufficient sites for adsorption and reaction than that of Co(Ni)MoS. Hence, the Ni2P (001) surface is of great performance to catalyze HDS process, which makes Ni2P a promising next generation catalyst.

Small-particle interlayers (lenticles) show some characteristic hydraulic properties and can affect the movement of unsaturated water. In this study, we developed a novel online capillary-water-absorption monitoring device and conducted three groups of comparison tests to simulate lenticle positions and thicknesses with respect to the capillary rise. The results show that the characteristic wetting front exhibits a fast rise in the early stage, a slow rise in the middle stage, and stability in the later stage. The motion of the capillary water in the lenticle is mainly transversal, with the upward curve being “flat,” and the longer is “flat,” the longer is the time needed for the water to move. The interlayer can form a capillary stagnation zone with moisture content close to saturation. The high interlayer may form a discontinuous corrugated capillary zone. Thus, when the wetting front reaches the “coarse-grain (lower)-fine-grain (upper)” interface, the “anti-capillary barrier effect” results in more moisture in the upper layer. Thus, when the wetting front of the capillary water reaches the “fine-grain (upper)-coarse-grain (lower)” interface, the “capillary barrier effect” causes the moisture content of the upper tailings to decreases sharply because of the horizontal movement of the water in the fine medium. It is clear that the presence of lenticles can retard the rise of capillary water by storing water.

OBJECTIVE To investigate the therapeutic and synergistic effects of QHC (combination of quercetin (Q), hirudin (H) and cinnamaldehyd (C)) on Schwann cell differentiation and myelination against high glucose (HG) induced injury. METHODS Primary-culture Schwann cells exposed to HG (50 mmol/L) for 72 h and Schwann cell-dorsal root ganglion (DRG) neuron cocultures exposed to HG (50 mmol/L) for 7 days were employed as in vitro model of diabetic neuropathy. The cells were randomly divided into 10 groups: control (CON, 25 mmol/L glucose), HG (50 mmol/L glucose), HG plus 10 μmol/L quercetin (Q), HG plus 0.04 IU/mL hirudin (H), HG plus 100 nmol/L cinnamaldehyd (C), HG plus 10 μmol/L quercetin and 0.04 IU/mL hirudin (QH), HG plus 10 μmol/L quercetin and 50 nmol/L cinnamaldehyd (QC), HG plus 0.04 IU/mL hirudin and 50 nmol/L cinnamaldehyd (HC), HG plus 10 μmol/L quercetin, 0.04 IU/mL hirudin and 50 nmol/L cinnamaldehyd (QHC) or 10 μmol/L U0126. Cell differentiation was evaluated by periaxin immunofluorescence staining. The protein expression levels of myelin protein zero (P0), myelin basic protein (MBP), myelin-associated glycoprotein (MAG), extracellular signal-regulated kinase (ERK), p-ERK, p-c-Jun, c-Jun, notch intracellular domain (NICD) and the mRNA expression levels of P0, MBP, MAG, Krox-20, Notch1 and Jagged1 were detected by Western blotting and real-time quantitative PCR analysis. The secretion of ciliary neurotrophic factor (CNTF) was determined by enzyme-linked immunosorbent assay (ELISA). The number and length of the myelin segments were evaluated by MBP immunofluorescence staining. The expression and the location of p-ERK in cocultures were detected by MAG and p-ERK immunofluorescence double staining. RESULTS Co-treatment with Q, C, H and their combination promoted Schwann cell differentiation, increased CNTF secretion, up-regulated the protein and mRNA expressions of myelin, and increased the number and length of the myelin segments (P

Taking corn straw as the main raw material, diatomite-modified wood ceramics were prepared by carbonizing a phenolic resin-corn straw powder composite under vacuum. The structure and properties of as-prepared materials were characterized by a scanning electron microscope, X-ray diffraction(XRD), Fourier transform infrared spectroscopy(FT-IR), Scanning electron microscope(SEM) and Mercury injection. Using tetracycline as the target pollutant, the adsorption isotherms and adsorption kinetics of the samples on tetracycline were investigated. Results indicated that the diatomite-modified wood ceramics were mainly amorphous, containing a small amount of quartz phase and crystalline graphite. The pore size range, the porosity, and the specific surface area of the wood ceramics were 1000 nm to3800 nm, 48.6 %, and 7.83 m2/g respectively. According to the correlation coefficient R2, a Langmuir isothermal adsorption model could better describe the adsorption process of tetracycline on wood ceramics than the Freundlich model (0.999 versus 0.975). Adsorption kinetics of tetracycline (initial concentration 5 mg/L) on wood ceramics were analyzed at pH values of 3, 5, 7, and 9. Results of the kinetic model fitting showed that the quasi-second-order kinetic model parameter (R2 > 0.98) could better fit the adsorption process of tetracycline. After repetitive use of the wood ceramics, the removal efficiency decreased due to blockage of pores. After regeneration, the removal efficiency for tetracycline exceeded 90%.

Raman spectroscopy demonstrates that the rotational spectrum of solid hydrogen, and its isotope deuterium, undergo profound transformations upon compression while still remaining in phase I. We show that these changes are associated with a loss of quantum character in the rotational modes, ie. with increasing pressure, the angular momentum J gradually ceases to be a good quantum rotational number. Through isotopic comparisons of the rotational Raman contributions, we reveal that hydrogen and deuterium evolves from a quantum rotor to a harmonic oscillator. We find that the mechanics behind this transformation can be well described by a quantum mechanical single inhibited rotor, accurately reproducing the striking spectroscopic changes observed in phase I.

In order to address the uneven flow velocity of catalytic reactors and to solve problems such as wear and soaring temperatures in catalysts, a three-dimensional mathematical model of a catalytic reactor was established. A comparison of the physical model and the simulation results revealed that the experimental results were a good fit to the mathematic model. The optimization results revealed the following: advantageous placement of the catalyst was 12.5–17.5 mm from the optimization site; the cross plate + porous plate was preferential; and the area-weighted uniformity index reached 0.93. When the catalyst was placed 25–30 mm from the optimization site, the cross plate + strip plate + hole or cross + grid + plate was preferential and the area-weighted uniformity index reached 0.93.

Significance When hydrogen and deuterium are mixed, they form H 2 +HD + D 2 mixtures at very low pressures and room temperature. We show that at high pressures and low temperatures these mixtures behave as an isotopic molecular alloy (ideal solution); exhibiting symmetry-breaking phase transitions between phases I, II, and III; and shifting the transformations to phases II and III to higher pressures as compared to pure isotopes. This runs counter to any quantum effects based on isotope mass but can be explained by quantum trapping of high-kinetic energy states by the exchange interaction.

How to quantitatively evaluate the groundwater environment is an important question. In this paper, the groundwater environment was defined by considering both groundwater quantity and quality, and...

As the pace of global sustainable economic development accelerates, increasing attention has been brought to the reutilization of waste electronic resources (E-waste). As the leading manufacturer a...

Plasmonic nanocavities comprised of metal film-coupled nanoparticles have emerged as a versatile nanophotonic platform benefiting from their ultrasmall mode volume and large Purcell factors. In the weak-coupling regime, the particle-film gap thickness affects the photoluminescence (PL) of quantum emitters sandwiched therein. Here, we investigated the Purcell effect-enhanced PL of monolayer MoS2 inserted in the gap of a gold nanoparticle (AuNP)–alumina (Al2O3)–gold film (Au Film) structure. Under confocal illumination by a 532 nm CW laser, we observed a 7-fold PL peak intensity enhancement for the cavity-sandwiched MoS2 at an optimal Al2O3 thickness of 5 nm, corresponding to a local PL enhancement of ∼350 by normalizing the actual illumination area to the cavity’s effective near-field enhancement area. Full-wave simulations reveal a counterintuitive fact that radiation enhancement comes from the non-central area of the cavity rather than the cavity center. By scanning an electric dipole across the nanocavity, we obtained an average radiation enhancement factor of about 65 for an Al2O3 spacer thickness of 4 nm, agreeing well with the experimental thickness and indicating further PL enhancement optimization. Our results indicate the importance of configuration optimization, emitter location and excitation condition when using such plasmonic nanocavities to modulate the radiation properties of quantum emitters.

Traditional teaching programing for the welding of large-scale complex structures is extremely difficult, with low efficiency and poor consistency, and it is very difficult to obtain the random change of joint geometry and achieve excellent welding quality along the entire welding path. In this paper, an integrated method of automatic generation of welding paths and adaptive filling of lap welding based on laser-structured light scanning is proposed. First, the point-cloud data of the welding joint on the entire welding path were acquired by laser scanning a workpiece, and the welding-seam model of the lap joint was accurately reconstructed using a cubic smoothing spline algorithm. The key feature points of the welding path were then extracted by Douglas-Peucker algorithm and the corresponding postures calculated and optimized. Furthermore, a neural network model that correlated the optimized process parameters with the gaps in the lap joints was established. During the welding process, the welding parameters were adaptively adjusted according to the variation of the obtained joint geometry based on the model. The results of the experiments showed that the proposed method has excellent adaptability to complex-curve welding seams with gaps randomly distributed between 0 and 2 mm, achieving satisfactory results in both path accuracy and weld appearance.

A low-cost beam-steerable antenna operating at 28 GHz is presented for 5G mobile terminal applications. The proposed array has a sandwich-like stack structure, including ten patch elements, a ground plane with two rows of slots, and a long microstrip transmission feed line. The radiators and the transmission line are placed at the two sides of the ground plane, which provides great flexibility in designing the radiating pattern and the feed network. Switches are added on the transmission line to construct periodic distributed phase shifters for beam steering. The 2 bit phase shifter with phases of 0°, 90°, 180°, and 270° is formed without using phase delay lines. Continuous beam steering is achieved, and the steerable angle is 121° in the upper hemisphere. Two location configurations of the proposed array in 5G mobile terminals are analyzed and the angular coverage range is evaluated. The array can cover 38.8% of the hemispherical space with a gain threshold of 10 dBi.

Ferroelectrics is expected to be an alternative to traditional solar cells, because its bulk photovoltaic effect (BPVE) may overcome the Shockley–Queisser limit. Here, we propose that a family of p...

“O2O” (online to offline) classification recycling is a new recycling mode. It is premised on the double lines of online service platform and offline recycling service system, thus forming a pattern of online disposal and offline trading. This mode is capable of integrating upstream and downstream resources on the network platform, creating a recycling and processing mode for the entire industry chain of waste sorting, and developing a circular development mode featuring “resources-products-waste-renewable resources” to realize the recycling of resources. In this paper, the abovementioned recycling mode is constructed using the dynamic method. Taking the recycling of electronic waste (e-waste) as an example, on the basis of the original recycling, an online recycling mode is added and the e-waste is reprocessed by means of classification. Besides, the final process of manufacturing is put into the market for new products. By comparing the efficiency of different classification processing channels and the efficiency of O2O recycling, it was found out that the higher the product quality, the higher the recycling efficiency, and the efficiency of online recycling was concluded due to offline recycling. The system effect is evaluated according to the environmental index. In addition, this paper demonstrates the applicability of the mode by introducing Gree Corp. The results of this research are believed as contributory to improving the efficiency of recycling and remanufacturing at corporate level. It also provides a valuable reference for the improvement of e-waste management work for governments both in China and other countries.

Purpose This paper aims to investigate the situation and evolution of sustainability performance in China. Design/methodology/approach This paper adopts the global Malmquist-Luenberger (GML) productivity index based on data envelopment analysis and Tobit regression for analysis. Findings The results indicate the following: China’s sustainability performance has been improving since 2005 and is closely related to the national development strategy and supportive policy; regional gaps in sustainability are a prominent problem represented by the fact that South Central China is becoming a sustainability collapse zone; interprovincial heterogeneity is evident with the varying development speed and conditions; and the level of sustainability performance has a significantly positive correlation with the urbanization rate, investment completed in the treatment of industrial pollution, government appropriation for education and per capita area of paved roads, but it has a negative correlation with the possession of private vehicles. Originality/value As an application, this study assessing the GML productivity index of 30 provinces in China from 2005 to 2015 and analyse the sustainability performance on three regional levels (i.e. country level, regional level and provincial level). Tobit regression is also applied to recognize the factors related to the GML index with the results taken as references for policy suggestions. The results have implications for a comprehensive understanding of China's sustainability performance and policymaking in this field.

Based on the conjunction of contact electrification and electrostatic induction, triboelectric nanogenerators (TENGs) can harvest mechanical energy dispersed in our environment. With the characteristics of simple structure, light weight, broad material availability, low cost, and high efficiency even at low operation frequency, TENG can serve as a promising alternative strategy for meeting the needs of distributed energy for the internet of things and network. The major potential applications of TENG can be summarized as four fields containing micro/nano power sources, self-powered sensors, large-scale blue energy, and direct high-voltage power sources. In this paper, the fundamental physics, output performance enhancement, and applications of TENGs are reviewed to timely summarize the development of TENGs and provide a guideline for future research.