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In this paper, a Ni coating was deposited on the surface of the A356 aluminum alloy by high velocity oxygen fuel spraying to improve the performance of the AZ91D magnesium/A356 aluminum bimetal prepared by a compound casting. The effects of the Ni coating as well as its thickness on microstructure and mechanical properties of the AZ91D/A356 bimetal were systematically researched for the first time. Results demonstrated that the Ni coating and its thickness had a significant effect on the interfacial phase compositions and mechanical properties of the AZ91D/A356 bimetal. The 10µm's Ni coating cannot prevent the generation of the Al-Mg intermetallic compounds (IMCs) at the interface zone of the AZ91D/A356 bimetal, while the Ni coating with the thickness of 45 µm and 190 µm can avoid the formation of the Al-Mg IMCs. When the Ni coating was 45 µm, the Ni coating disappeared and transformed into Mg-Mg2Ni eutectic structures+Ni2Mg3Al particles at the interface zone. With a thickness of 190µm's Ni coating, part of the Ni coating remained and the interface layer was composed of the Mg-Mg2Ni eutectic structures+ Ni2Mg3Al particles, Mg2Ni layer, Ni solid solution (SS) layer, Al3Ni2 layer, Al3Ni layer and sporadic Al3Ni+Al-Al3Ni eutectic structures from AZ91D side to A356 side in sequence. The interface layer consisting of the Mg-Ni and Al-Ni IMCs obtained with the Ni coating had an obvious lower hardness than the Al-Mg IMCs. The shear strength of the AZ91D/A356 bimetal with a Ni coating of 45 µm thickness enhanced 41.4% in comparison with that of the bimetal without Ni coating, and the fracture of the bimetal with 45µm's Ni coating occurred between the Mg matrix and the interface layer with a mixture of brittle fracture and ductile fracture.

This research serves to advance the development of engineering computational fluid dynamics (CFD) computing efficiency for the analysis of pressurized water reactor (PWR) core using rod-type fuel assemblies with mixing vanes (one kind of typical PWR core). In this research, a CFD scheme based on the reconstruction of the initial fine flow field (RIF CFD scheme) is proposed and analyzed. The RIF scheme is based on the quantitative regulation of flow velocities in the rod-type PWR core and the principle that the CFD computing efficiency can be improved greatly by a perfect initialization. In this paper, it is discovered that the RIF scheme can significantly improve the computing efficiency of the CFD computation for the rod-type PWR core. Furthermore, the RIF scheme also can reduce the computing resources needed for effective data storage of the large fluid domain in a rod-type PWR core. Moreover, a flow-ranking RIF CFD scheme is also designed based on the ranking of the flow rate, which enhances the utilization of the flow field with a closed flow rate to reconstruct the fine flow field. The flow-ranking RIF CFD scheme also proved to be very effective in improving the CFD efficiency for the rod-type PWR core.

In this paper, the mechanical properties of polyvinyl chloride (PVC)-coated woven fabric at high-temperature and after exposure to high-temperature were studied using a uniaxial tensile test. The test temperature range is 20 °C to 170 °C. The high-temperature test shows that the maximum stress at 170 °C reduced to 57%−59% of that at 20 °C. The after exposure to high-temperature test of prestressing shows that the combined effect of high-temperature and constant external force enhanced the stiffness and reduced the material’s impact toughness. The experimental phenomena can be explained by polymer physics.

Hypereutectic Al-Si-Cu coatings were prepared by supersonic atmospheric plasma spraying to enhance the surface performance of lightweight alloys. To find out optimum process conditions and achieve desirable coatings, this work focuses on the influence of three important parameters (in-flight particle temperature, impact velocity, and substrate temperature) on the collected splats morphology, coatings microstructure and microhardness. Results show that appropriate combinations of temperature and velocity of in-flight particles cannot only completely melt hypereutectic Al-Si-Cu particles, especially the primary Si phase, but also provide the particles with sufficient kinetic energy. Thus, the optimized coating consists of 98.6 % of fully-melted region with nanosized coupled eutectic and 0.9 % of porosity. Increasing the substrate deposition temperature promotes the transition from inhomogeneous banded microstructure to homogeneous equiaxed microstructure with a lower porosity level. The observations are further interpreted by a newly developed phase-change heat transfer model on quantitatively revealing the solidification and remelting behaviors of several splats deposited on substrate. Besides, phase evolutions including the formation of supersaturated α-Al matrix solid solution, growth of Si and Al2Cu phases at different process conditions are elaborated. An ideal microstructure (low fractions of unmelted/partially-melted regions and defects) together with solid solution, grain refinement, and second phase strengthening effects contributes to the enhanced microhardness of coating. This integrated study not only provides a framework for optimizing Al-Si based coatings via thermal spraying but also gives valuable insights into the formation mechanisms of this class of coating materials.

In order to improve mechanical properties of 6082 aluminum alloy, the SiCp/Al 6082 composites were prepared by the addition of the micron-sized SiC particles combined with the squeeze casting. The effects of the SiCp content and squeeze casting on the microstructure and mechanical properties of the 6082 aluminum alloy were investigated by SEM, EDS, TEM, tensile testing and hardness testing analysis methods. Research results exhibited that the SiCp content and squeeze casting had a significant impact on the microstructure and mechanical properties of the 6082 aluminum alloy. The addition of the SiCp refined the grain size of the 6082 aluminum alloy while caused the increase of the porosity with increasing the SiCp content, especially in the permanent mold casting condition. Compared to the permanent mold casting, the squeeze casting obviously reduced pore defects, refined grain size and made the SiCp distribute evenly as well as bond tightly with the Al matrix. The tensile strength, yield strength, elongation, elastic modulus and hardness of the 6082 aluminum alloy obtained with the SiCp and squeeze casting were remarkably improved, and the optimal mechanical properties were obtained with a 2 wt.% SiCp, and they increased 10.73 %, 72.7 %, 193.9 %, 23.5 % and 25.2 %, respectively, compared to those of the 6082 aluminum alloy obtained without SiCp and squeeze casting. The fracture surface of the SiCp/Al 6082 composites obtained with the squeeze casting was dense and exhibited a ductile fracture mode.

Donor-acceptor co-doped rutile TiO2 ceramics with colossal permittivity (CP) have been extensively investigated in recent years due to their potential applications in modern microelectronics. In addition to CP and low dielectric loss, voltage stability is an essential property for CP materials utilized in high-power and high-energy density storage devices. Unfortunately, the voltage stability of CP materials based on co-doped TiO2 does not catch enough attention. Here, we propose a strategy to enhance the voltage stability of co-doped TiO2, where different ionic defect clusters are formed by two acceptor ions with different radii to localize free carriers and result in high performance CP materials. The (Ta + Al + La) co-doped TiO2 ceramic with suitable La/Al ratio exhibits colossal permittivity with excellent temperature stability as well as outstanding dc bias stability. The density functional theory analysis suggests that L a 3 + A l 3 + V O • • T i 3 + defect clusters and Ta5+-Al3+ pairs are responsible for the excellent dielectric properties in (Ta + Al + La) co-doped TiO2. The results and mechanisms presented in this work open up a feasible route to design high performance CP materials via defect engineering.

Catalysts of Ni supported on home-made mesoporous alumina (Ni/M-Al2O3) were prepared via facile incipient impregnation method and calcined under different temperature (500–800 °C). Compared with catalysts of Ni supported on commercial alumina, they showed much higher conversion and lower carbon deposition in methane dry reforming (DRM). Among the catalysts, Ni/M-Al2O3-700 exhibited the highest DRM activity, with 77.6% CH4 conversion and 85.4% CO2 conversion at 700 °C. TPR revealed that almost all the Ni was in the form of NiAl2O4 spinel after calcination at 700 °C. Due to the strong metal-support interaction of NiAl2O4 structure, the Ni crystal size of Ni/M-Al2O3-700 after reduction was around 5 nm. TGA and TEM results showed its carbon deposition after 20 h DRM test was only 3.8% and mainly in the form of amorphous carbon. This work indicates that the formation of NiAl2O4 spinel is beneficial to activity and stability towards DRM reaction and controlling calcination temperature is crucial.

This paper proposes a finite-time adaptive neural network (NN) position tracking control method for the permanent magnet synchronous motor (PM SM) servo system with full-state constraints. NNs are utilized to compensate the parameter uncertainties and load torque disturbances of the PMSM servo system. Multiple barrier Lyapunov functions are introduced in the backstepping based control design, such that the rotor position, the rotor angular velocity and the currents of the d-q axis are constrained in given compact sets. In addition, the finite-time control technique is adopted to the control design, which can accelerate the convergence speed and improve the robustness of the PMSM servo system. Under the proposed control algorithm, the position tracking control is realized in finite time, and the tracking error can converge to a small neighborhood of the origin. Finally, simulation results are presented to show the effectiveness of the developed control approach, and some comparisons are given to show the rapid and accurate position tracking control performance.

A novel virtual material layer model based on the fractal theory was proposed to predict the natural frequencies of carbon fiber reinforced plastic composite bolted joints. Rough contact surfaces of composite bolted joints are modeled with this new proposed approach. Numerical and experimental modal analyses were conducted to validate the effectiveness of the proposed model. A good consistence is noted between the numerical and experimental results. To demonstrate the necessity of accurately modeling the rough contact surfaces in the prediction of natural frequencies, virtual material layer model was compared with the widely used traditional model based on the Master-Slave contact algorithm and experiments, respectively. Results show that the proposed model has a better agreement with experiments than the widely used traditional model (the prediction accuracy is raised by 8.77% when the pre-tightening torque is 0.5 N∙m). Real contact area ratio A* of three different virtual material layers were calculated. Value of A* were discussed with dimensionless load P*, fractal dimension D and fractal roughness G. This work provides a new efficient way for accurately modeling the rough contact surfaces and predicting the natural frequencies of composite bolted joints, which can be used to help engineers in the dynamic design of composite materials.

Due to no requirement for direct interpretation of the guided wave signal, probability-based diagnostic imaging (PDI) algorithm is especially suitable for damage identification of complex composite structures. However, the weight distribution function of PDI algorithm is relatively inaccurate. It can reduce the damage localization accuracy. In order to improve the damage localization accuracy, an improved PDI algorithm is proposed. In the proposed algorithm, the weight distribution function is corrected by the acquired relative distances from defects to all actuator–sensor pairs and the reduction of the weight distribution areas. The validity of the proposed algorithm is assessed by identifying damages at different locations on a stiffened composite panel. The results show that the proposed algorithm can identify damage of a stiffened composite panel accurately.

In view of the problems of low precision, poor quality, and long time of gait feature recognition due to the influence of human body movement environment on the recognition process of the current gait feature recognition method of human body movement blurred image, a new method of gait feature recognition based on graph neural network (GNN) method is proposed. The gait features of human movement blurred images were extracted, and the fusion clustering recognition of the GNN algorithm was used to locate the gait features of human movement blurred images. The gait features of human body movement blurred images were located by the GNN method. According to the contour feature point info of the human body movement blurred image, the standard deviation of gait feature location of the human body movement blurred image was calculated, the gait feature of the blurred image of human body movement was reconstructed, and the gait recognition of the human body movement blurred image was achieved. The results show that the extraction of human movement is good, with high positioning confidence, good recognition quality, average recognition accuracy of 92%, and greatly shortened recognition time.

Morphing leading edge has great potential for noise abatement and aerodynamic efficiency improvement. The drooping effect is realized by bending of the flexible skin which encloses to form the leading edge. Since the flexible skin is often made of composite laminates of Glass Fiber Reinforced Plastics (GFRP), the lay-up sequences have become the determinant, which affects not only the morphing quality but also the manufacturing complexity. Two optimizing methods of lay-up sequences are comparatively studied. In the first method, the laminal quantities in 0°, ±45° and 90° vary independently, while in the second one, the concept of isotropic laminate unit [0/45/−45/90]s is employed and the unit quantity is the unique variable. Final evaluation demonstrates that for both methods there is insignificant impact to the overall morphing quality; however, specific concern is equally necessary for these two methods to the tip of the leading edge where the skin is at its minimum thickness and bears the most severe bending deformation. In terms of computational efficiency and post-processing labor, the second method has better performance.

In this paper, the coupling of fine-mesh computational fluid dynamics (CFD) thermal-hydraulics (TH) code and neutronics code is achieved using the Ansys Fluent User Defined Function (UDF) for code development, including parallel meshing mapping, data computation, and data transfer. Also, some CFD schemes are designed for mesh mapping and data transfer to guarantee physical conservation in the coupling computation. Because there is no rigorous research that gives robust guidance on the various CFD schemes that must be obtained before the fine-mesh coupling computation, this work presents a quantitative analysis of the CFD meshing and mapping schemes to improve the accuracy of the value and location of key physical prediction. Furthermore, the effect of the sub-pin scale coupling computation is also studied. It is observed that even the pin-resolved coupling computation can also create a large deviation in the maximum value and spatial locations, which also proves the significance of the research on mesh mapping and data transfer for CFD code in a coupling computation.

Catalysts/co-catalysts for cathodic oxygen reduction and anodic methanol oxidation reactions (ORR/MOR) play the major roles in promoting the commercialization of direct methanol fuel cells. Herein, bimetallic zeolite-imidazolate-frameworks (CoZn-ZIFs) is used as precursor to synthesize Co3O4@NPC/CuO composites as catalysts for ORR and Pt supports/co-catalysts for MOR. The ORR activity (E1/2 = 0.83 V) and long-term stability (activity retention of 85.5% after 30,000 s) of Co3O4@NPC/CuO-400 (400 °C) dodecahedron are better than those of commercial Pt/C (10 wt%) in alkaline electrolytes. The surface CuO with variable valence states (Cu0 and Cu2+) can be used as both the active component for ORR and the protective layer for Co3O4 to enhance catalytic stability. Partial removal of CoOx from carbon framework promotes the exposure of highly active sites (Co2+) on the Co3O4. For MOR, the mass activity of Pt-Co3O4@NPC/CuO-400 (5 wt%) (1947 mA mgPt−1) is much higher than that of Pt/C (751 mA mgPt−1), mainly attributing to that the Pt active sites are uniformly dispersed on Co3O4@NPC/CuO support. The strong interaction between Pt and CuO can reduce the bond strength of Pt-CO to enhance CO resistance. Co3O4 can activate H2O molecules to provide sufficient OH− species to promote MOR. This study provides a new idea for preparation of active ORR catalysts and MOR co-catalyst from bimetallic ZIFs.

Integrating the advantages of the Scaled Boundary Finite Element Method (SBFEM) and a temporally piecewise adaptive algorithm (TPAA), an effective numerical approach is developed for the dynamic analysis of viscoelastic problems. By virtue of a piecewise variables separation, a temporally and spatially coupled dynamic viscoelastic problem is decoupled into a series of recursive spatial problems which are solved by SBFEM. A stable and accurate temporal solution can be achieved via an adaptive process when the step size varies, and the convenience for the treatment of stress singularity and mesh generation in the viscoelastic dynamic analysis can be provided by SBFEM. Numerical tests are conducted to verify the effectiveness of the proposed approach and to highlight the advantages introducing SBFEM over the conventional methods in the dynamic analysis, and the proposed results agree well with references.

A rapid-spreading epidemic of COVID-19 hit China at the end of 2019, resulting in unignorable social and economic damage in the epicenter, Wuhan. POIs capture the microscopic behavior of citizens, providing valuable information to understand city reactions toward the epidemic. Leveraging large-scale check-in records, we analyze the POI visit trends over the epidemic period and normal times. We demonstrate that COVID-19 greatly influences the society, where most POIs demonstrate more than 60% of visit drops during the city lockdown period. Among them, Tourist Attractions received greatest impact with a 78.8% drop. Entertainment, Food, Medical and Shopping are sensible to the disease before lockdown, and we identify these "early birds" to investigate the public reaction in the early stage of the epidemic. We further analyze the revival trends, generating four different revival patterns that correlated with the necessity of POI functions. Finally, we analyze the perseverance during the COVID-19, finding no large-scale closures compared with the tremendous visit drop. The strong resilience in Wuhan supports the rapid recovery of society. These findings are important for researchers, industries, and governments to understand the city respondence under severe epidemic, proposing better regulations to respond, control, and prevent public emergencies.

In this paper, a finite-time composite adaptive fuzzy control approach is proposed for position tracking of permanent magnet synchronous motors (PMSMs) with uncertain parameters and load torque disturbances. Based on the approximate ability of fuzzy logic systems (FLSs), a novel finite-time adaptive fuzzy control is developed, where dynamic surface control with error compensation mechanism is incorporated in the control design. A serial-parallel estimation model of PMSM is established. The prediction error between PMSM and the serial-parallel estimation model is utilized to construct the adaptive law of FLSs, which can improve the approximate accuracy of FLSs. Moreover, by introducing a new fuzzy adaptive law, the algebraic loop problem in the existing backstepping based control approaches is overcome. It is proven that all signals of closed-loop systems are uniformly ultimately bounded, and the position tracking error can converge to a small neighborhood of the origin in finite time. Finally, the simulation results are presented to show the effectiveness of the proposed control approach, and some comparisons are given to show the rapid and accurate position tracking performance.

Many uncertainties exist in pile-stabilized slopes which make their design substantially complicated. In this paper, a first-order reliability method (FORM) based on a support vector machine (SVM) and uniform design (UD) is proposed to investigate the reliability of a pile-stabilized slope. A deterministic model considering soil-pile interaction and using a strength reduction method based on the displacement criterion is employed to calculate the factor of safety and the corresponding pile-bending moment. The uncertainties involved in both the soil and the stabilizing pile are taken into consideration in the proposed method. Two typical failure modes, namely the insufficient factor of safety of a pile-stabilized slope and the insufficient flexural bearing capacity of stabilizing piles, are investigated from a probabilistic viewpoint. The proposed method is illustrated with an artificial homogeneous slope and a real slope engineering case. The influence of stabilizing pile installation locations on the reliability results were studied. The sensitivity of random variability was also investigated. The results of the proposed method show high accuracy while having a smaller computational time requirement when compared with the polynomial response surface method. The case study of a real highway slope shows the feasibility of the proposed method applied to practical engineering problems.

Germanium selenide attracts emerging attention for applications in high-performance field-effect transistors and photodetector, due to its unique photoelectric performances and tunable bandgap. Herein, the germanium selenide-reduced graphene oxide (GeSe-RGO) hybrid is successfully synthesized by loading liquid-phase exfoliated GeSe nanosheets on the 2D graphene via a hydrothermal reduction process. It is demonstrated that the photoresponse performance of the photoelectrochemical (PEC) device constructed by the synthesized GeSe-RGO hybrid is significantly enhanced in comparison to the one built by the sole GeSe nanosheets. The results of the PEC tests display the photocurrent density can reach to 8.45 μA/cm2 at the bias of 0.8 V, which is approximately 4 times that of sole GeSe nanosheets. Meanwhile, with the increase of illumination intensity, the photocurrent density of the GeSe-RGO photodetector almost presents a linear increase. When the illumination intensity is 90 mW/cm2, the photoresponsivity value is up to 116 μA/W. In addition, the GeSe-RGO hybrid shows a durable photoresponse capability under the bias of 0 V, revealing that such a hybrid has the excellent self-powered ability in the PEC system. There is no evident degradation on the photoresponsive performance of GeSe-RGO photodetector after 50 light on-off cycling, indicating the good stability of the hybrids. The enhanced performance of the GeSe-RGO hybrid is attributed to the outstanding photoresponsive capability of RGO and the effective separation of photo-generated electron-hole pairs. Our work indicates that the GeSe-RGO hybrid has great prospects for future applications in PEC-type photodetector devices.

Anion redox chemistry plays a crucial role in Na-deficient or Na-rich oxide cathodes for sodium-ion batteries (SIBs). But the oxygen redox chemistry has been rarely reported in O3-type (Na-full) layered oxides for SIBs. Herein, we reveal the anion redox chemistry in O3-type NaMn1/3Fe1/3Ni1/3O2 (MFN) cathode material, and propose an integrated strategy combining ZrO2 coating and Zr4+ doping to tune the anionic redox chemistry and crystal structures which improves the activity, reversibility, and stability of O2−. Oxygen redox reactions with high reversibility and cyclic stability mainly occur between 4.0 V and 4.3 V. The structure of Na-O-TM is regulated by Zr4+ doping, and the electronic state of O-2p occupies a higher energy level by the regulation of Na-O-TM structure. The electrons can migrate from O2- more easily and O2−/O− contribute more capacity. Zr4+ doping optimizes the lattice structure, enlarges the Na layer, and decreases TMO2 layer, which reduces the diffusion resistance of Na+ and increases the structural stability. ZrO2 layer effectively mitigates the corrosion of the electrolyte and ensures the integrity of the structure, which inhibits the formation of Na2CO3 on the surface and decreases CO2 release. This study not only reveals the anion redox chemistry in O3-type layered oxide cathode material but also is available for insights into regulating the redox activity, reversibility, and stability of oxygen.

JADE is a differential evolution (DE) algorithm and has been shown to be very competitive in comparison with other evolutionary optimization algorithms. However, it suffers from the premature convergence problem and is easily trapped into local optima. This article presents a novel JADE variant by incorporating chaotic local search (CLS) mechanisms into JADE to alleviate this problem. Taking advantages of the ergodicity and nonrepetitious nature of chaos, it can diversify the population and thus has a chance to explore a huge search space. Because of the inherent local exploitation ability, its embedded CLS can exploit a small region to refine solutions obtained by JADE. Hence, it can well balance the exploration and exploitation in a search process and further improve its performance. Four kinds of its CLS incorporation schemes are studied. Multiple chaotic maps are individually, randomly, parallelly, and memory-selectively incorporated into CLS. Experimental and statistical analyses are performed on a set of 53 benchmark functions and four real-world optimization problems. Results show that it has a superior performance in comparison with JADE and some other state-of-the-art optimization algorithms.

This research presents a multi-vehicle control strategy combining the merging control and platoon control to deal with the merging scenario in the freeway ramp entrance. In the merging controller, a selection algorithm is presented firstly to determine the merging sequence based on the initial states of the vehicles in the cooperative area. Subsequently, the optimized longitudinal trajectory of the facilitating vehicle and on-ramp vehicle is addressed by solving the Hamiltonian function. As for the platoon controller, string stability is analyzed to attenuate the impact of merging on the mainline traffic, considering the influence of communication time delay. Smooth mode switching is achieved by introducing the tracking policy of platoon controller to determine the terminal states of the facilitating vehicle. The proposed strategy is simulated at two typical merging scenarios, where two on-ramp vehicles attempt to cut into a six-vehicular platoon, to validate its feasibility and advantage.

The main work of this paper focuses on identifying the heat flux in inverse problem of two-dimensional nonhomogeneous parabolic equation, which has wide application in the industrial field such as steel-making and continuous casting. Firstly, the existence of the weak solution of the inverse problem is discussed. With the help of forward solution and dual equation, this paper proves the Lipchitz continuity of the cost function and derives the Lipchitz constant. Furthermore, in order to accelerate the convergence rate and reduce the running time, this paper presents a sufficient descent Levenberg–Marquard algorithm with adaptive parameter (SD-LMAP) to solve this inverse problem. At last, compared with other methods, the results of simulation experiment show that this algorithm can obviously reduce the running time and iterative number.

Oriented towards the stiffness optimization of parallel manipulators, a stiffness modeling method based on subspace analysis was proposed. The paper revealed the “shielding effect” of passive joints, i.e. shielding the partial local stiffness of a mechanism on Cartesian space, and accordingly brought forward the concept of Effective Stiffness Tensor (EST), which was then obtained by introducing the orthogonal projector. The method allows a stiffness model of analytical form as well as high computation efficiency, which makes it suitable for applications in the stiffness optimization of parallel manipulators. Proposed method had been applied to the parallel alignment mechanism in China’s large space telescope and verified by dynamic experiments.

Carbon-molybdenum disulfide (C-MoS2) ultrathin nanosheets were prepared by a hydrothermal process, and then AgI/C-MoS2 were synthesized via an in-situ deposition method. This ternary heterojunction composite exhibited better photocatalytic activity compared with those of one-component (pristine MoS2) and bicomponent (AgI/MoS2 and C-MoS2) materials for the degradation of organic dyes under the visible-light irradiation. In particular, by comparing with AgI/MoS2, the significant role of conductive amorphous carbon in AgI/C-MoS2 in enhancing the charge transfer during the photocatalytic degradation of dyes was first confirmed by photocurrent response and electrochemical impedance spectroscopy (EIS). A possible photocatalytic mechanism was proposed based on the capture experiment results. Furthermore, a straightforward and interesting way had been applied to test the recycled/newly-prepared AgI/C-MoS2 composite for revealing its distinctive self-cleaning performance and recyclability characteristic besides its good photocatalytic activity. This work could provide a reference for the design of other new ternary heterojunction composite materials with special structures and properties.

Bone mineral consists of calcium hydroxy-carbonate apatite (HCA) that incorporates other minor cation substituents, primarily Na+ (0.5–0.8 wt %). We examine the carbonate species in various HCA spe...

Broaching is very efficient for machining complex-shaped slots in turbine disks made of high thermal resistant super-alloys. However, large cutting forces and high thermal flux lead to high tool we...

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task designed to predict the sentiment polarity of each aspect term in a text. Recent research mainly uses neural networks to model text and utilizes attention mechanisms to interact for associate aspect terms and context to obtain more effective feature representation. However, the general attention mechanism is easy to lose the original information. Besides, in the multi-aspect text, the sentiment information of other aspect terms interferes with the sentiment analysis of the current aspect term likely. In this paper, we propose two models named RA-CNN and RAO-CNN for ABSA tasks. In RA-CNN, we apply CNN to model the aspect term and utilize a specially designed residual attention mechanism to interact with the text. Based on the RA-CNN, RAO-CNN adds other aspect terms module, which can reduce interference of sentiment information related to other aspect terms in the multi-aspect text. To verify the proposed models’ effectiveness, we conduct a large number of experiments and comparisons on seven public datasets. Experimental results show that our proposed models are useful and achieve state-of-the-art results.

True Random Number Generators (TRNGs) create a hardware-based, non-deterministic noise that is used for generating keys, initialization vectors, and nonces in a variety of applications requiring cryptographic protection. A compromised TRNG may lead to a system-wide loss of security. In this brief, we show that an attack combining power analysis with bitstream modification is capable of classifying the output bits of a TRNG implemented in FPGAs from a single power measurement. We demonstrate the attack on the example of an open source AIS-20/31 compliant ring oscillator-based TRNG implemented in Xilinx Artix-7 28nm FPGAs. The combined attack opens a new attack vector which makes possible what is not achievable with pure bitstream modification or side-channel analysis.

In this letter, a novel concept for the dielectric resonator functioning as a differential antenna and as an independently controllable dual-band filter simultaneously is proposed for the first time. The multifunctional design takes full advantage of the virtual and reflection grounds of the differential dielectric resonator antenna (DDRA). The antenna and filter functions share a single module and a physical ground, making the proposed design compact and cost effective. In addition to high isolation between the two functions, the filter part shows two independently adjustable passbands. For demonstration, a prototype is designed, fabricated, and measured. The DDRA is designed at 2.5 GHz with an impedance bandwidth of 2.7% and the realized gain of 4.5 dBi. The dual-band filter operates at the center frequencies of 2.16 GHz and 3.51 GHz with 3 dB bandwidths of 13% and 7%, respectively. A good agreement is observed between the simulated and measured results.

Oxygen release and irreversible cation migration are the main causes of voltage fade in Li-rich transition metal oxide cathode. But their correlation is not very clear and voltage decay is still a bottleneck. Herein, we modulate the oxygen anionic redox chemistry by constructing Li2ZrO3 slabs into Li2MnO3 domain in Li1.21Ni0.28Mn0.51O2, which induces the lattice strain, tunes the chemical environment for redox-active oxygen and enlarges the gap between metallic and anionic bands. This modulation expands the region in which lattice oxygen contributes capacity by oxidation to oxygen holes and relieves the charge transfer from anionic band to antibonding metal–oxygen band under a deep delithiation. This restrains cation reduction, metal–oxygen bond fracture, and the formation of localized O2 molecule, which fundamentally inhibits lattice oxygen escape and cation migration. The modulated cathode demonstrates a low voltage decay rate (0.45 millivolt per cycle) and a long cyclic stability., Voltage fade is a critical issue for Li-rich transition metal oxide cathode. Here, the authors modulate the oxygen anionic redox chemistry and enlarges the gap between metallic and anionic states by constructing Li2ZrO3 slabs into Li2MnO3 domain in Li1.21Ni0.28Mn0.51O2 which fundamentally suppresses the voltage decay.

P2-type Na0.67Mn0.75Fe0.25O2 cathode materials with low cost and high specific capacity have aroused much interest for sodium-ion batteries. Nevertheless, the cyclic and structural stabilities need...

Reinforcement learning (RL) aims at searching the best policy model for decision making, and has been shown powerful for sequential recommendations. The training of the policy by RL, however, is placed in an environment. In many real-world applications, the policy training in the real environment can cause an unbearable cost due to the exploration. Environment estimation from the past data is thus an appealing way to release the power of RL in these applications. The estimation of the environment is, basically, to extract the causal effect model from the data. However, real-world applications are often too complex to offer fully observable environment information. Therefore, quite possibly there are unobserved variables lying behind the data, which can obstruct an effective estimation of the environment. In this paper, by treating the hidden variables as a hidden policy, we propose a partially-observed multi-agent environment estimation (POMEE) approach to learn the partially-observed environment. To make a better extraction of the causal relationship between actions and rewards, we design a deep uplift inference network (DUIN) model to learn the causal effects of different actions. By implementing the environment model in the DUIN structure, we propose a POMEE with uplift inference (POMEE-UI) approach to generate a partially-observed environment with a causal reward mechanism. We analyze the effect of our method in both artificial and real-world environments. We first use an artificial recommender environment, abstracted from a real-world application, to verify the effectiveness of POMEE-UI. We then test POMEE-UI in the real application of Didi Chuxing. Experiment results show that POMEE-UI can effectively estimate the hidden variables, leading to a more reliable virtual environment. The online A/B testing results show that POMEE can derive a well-performing recommender policy in the real-world application.

The paper first introduces R offset tool axis error calculation method. In order to reduce the maximum machining error, the paper presents a new geometric model of tool axis vector for flank milling non-developable ruled surfaces and machining error exact analytical solution is derived. Its main advantage is that the theoretical machining error of each point on the straight generatrix can be calculated. MATLAB software simulation and actual NC machining experiment are conducted to validate the effectiveness of the proposed algorithm. The measurement experimental results show that the actual maximum machining error is basically consistent with the theoretical calculation error, and the machining error can be reduced compared with the previous literature algorithm.

In this work, cabon nanofiber supported cobalt ferrite composites (C/CFO) were prepared by electrospinning combined with hydrothermal method. The morphology, structure and microwave absorption properties were discussed in detail. The results revealed that the C/CFO composites with 3 D network structure exhibited excellent microwave absorption properties, which were linked to the composite compositions and synergistic effect between C NFs and CFO NPs. When the content of CFO NPs was 10 wt %, the C/CFO sample showed RLmax of −137 dB at 11.36 GHz and the EAB (RL

The argon power cycle is one of the most promising technologies for high efficiency and low emission hydrogen-fueled internal combustion engines. The application of coaxial injection technology in the hydrogen-fueled argon engine can improve the mixing process and the combustion performance of the H2/O2 mixture. In this study, an innovative H2–O2 coaxial injection combustion system was designed to investigate the jet flame characteristics of oxygen coaxially wrapped by hydrogen in a controllable argon thermal atmosphere. The findings of this study could provide a new perspective for designing hydrogen-fueled argon engines in the future. The influences of co-flow temperature, jet injection pressure, and excess oxygen coefficient were all determined. Observations of the flame showed a bright blue flame with a reddish glow in the far-burner region. Experimental results show that the flame length, cross-sectional area, and area/perimeter ratio first decrease with increasing jet injection pressure and subsequently increase, reaching maximum values at 0.4–0.6 MPa. When increasing the co-flow temperature from 1023 K to 1223 K, the cross-sectional area of the flame increases significantly by 61.1% at an excess oxygen coefficient of 0.4. Furthermore, the liftoff flame height shrinks when the co-flow temperature and the excess oxygen coefficient increase, while it rises along with an increasing jet injection pressure.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies worldwide. However, the mechanisms underlying the acquisition of the metastatic potential in PDAC has not been well understood. In this study, we identified a total of 154 genes upregulated in primary tissues of PDAC with liver metastasis using the Genome Cancer Atlas (TCGA) and GSE151580 cohorts. The epithelial-mesenchymal transition and glycolysis were enriched by the liver metastasis-related genes, indicating that the liver metastasis-related genes might be functionally relevant to liver metastasis in PDAC. Moreover, we also found that the liver metastasis-related genes were primarily regulated at epigenetic level. Particularly, SFN, a cell cycle checkpoint protein, and KRT19, a marker gene for ductal cells, were predicted to be regulated by multiple methylation sites at the promoter. Clinically, we for the first time defined a liver metastasis score (LMS), which was derived from liver metastasis-related genes, and closely associated with clinical characteristics such as disease type and tumor grade, in PDAC. Furthermore, we also divided the samples into high and low LMS groups using three cohorts with long-term follow-up (TCGA, GSE71729, and E-MTAB-6134), which exhibited significantly different prognostic outcomes across three PDAC cohorts, suggesting that the LMS might be a good indicator for risk stratification in PDAC. Furthermore, we also found that the liver metastasis-related genes were primarily expressed in malignant ductal cells by integrative analysis of the bulk and single-cell gene expression data. Moreover, the malignant ductal cells and M0 macrophages were highly correlated with LMS, indicating that the two cell types might function as tumor-promoting cells in PDAC. In summary, the systematic analysis for the first time defined an LMS score to evaluate the risk of liver metastasis in PDAC and revealed that malignant ductal cells might promote PDAC liver metastasis, which greatly improves our understanding of the liver metastasis-related genes, their underlying mechanisms, and association with prognosis in PDAC.

Geosynthetic clay liners (GCLs), which have a very low permeability to water and a considerably high self-healing capacity, are widely used in liner systems of landfills. In this study, a series of experimental tests were carried out under complex conditions on typical commercial GCLs from China. In particular, the effects of pH values and lead ions (Pb2+) were tested in addition to other factors. The swelling properties of natural bentonite encapsulated between geotextile components in the GCLs were tested first. The swelling capacity was reduced rapidly at pH values 40 mM. Permeability tests on GCLs with different concentrations of lead ions were then performed by using the self-developed multi-link flexible wall permeameter, and data showed that increases in lead ion concentrations greatly improved the permeability. Finally, self-healing capacity tests were conducted on needle-punched GCLs under different levels of damage. Results showed that the GCLs have a good self-healing capacity with small diameter damage holes (2 mm, close to three times the original aperture), but with a damage aperture larger than 15% of the sample area, the self-healing capacity could not prevent leakage; hence, in certain situations it will be necessary to repair the damage to meet the anti-seepage requirement.

By increasing the charging voltage, a cell specific energy of >400 W h kg−1 is achievable with LiNi0.8Mn0.1Co0.1O2 in Li metal batteries. However, stable cycling of high-nickel cathodes at ultra-high voltages is extremely challenging. Here we report that a rationally designed sulfonamide-based electrolyte enables stable cycling of commercial LiNi0.8Co0.1Mn0.1O2 with a cut-off voltage up to 4.7 V in Li metal batteries. In contrast to commercial carbonate electrolytes, the electrolyte not only suppresses side reactions, stress-corrosion cracking, transition-metal dissolution and impedance growth on the cathode side, but also enables highly reversible Li metal stripping and plating leading to a compact morphology and low pulverization. Our lithium-metal battery delivers a specific capacity >230 mA h g−1 and an average Coulombic efficiency >99.65% over 100 cycles. Even under harsh testing conditions, the 4.7 V lithium-metal battery can retain >88% capacity for 90 cycles, advancing practical lithium-metal batteries. Charging at high voltages in principle makes batteries energy dense, but this is often achieved at the cost of the cycling stability. Here the authors design a sulfonamide-based electrolyte to enable a Li metal battery with a state-of-the-art cathode at an ultra-high voltage of 4.7 V while maintaining cyclability.

The positioning technology based on receive signal strength (RSS) fingerprints has become one of the hottest research spots with its advantages of simple deployment, low cost, and single parameter. However, in the limited space, the multipath and shadowing, result in poor separability of the fingerprint data, and low accuracy of target localization. In this paper, a novel RSS fingerprints positioning algorithm that is based on fuzzy kernel clustering SVM is proposed to combat the multipath and shadowing effects. The first step of the proposed positioning algorithm is to use kernel function to map the traditional fingerprints sample data to high-dimensional feature space to generate fuzzy classes. The second step is to generate binary-class SVM of fuzzy class based on the relationship between classes and internal discrete information of each class. After that, we can use the binary fuzzy class SVM to dichotomize the classified fingerprints in the first step, and combine these dichotomous SVMs into a handstand classification binary tree. And thus, the proposed positioning algorithm achieves quick and accurate positioning. Experimental results show that the positioning accuracy and locating stability of proposed positioning algorithm are improved by 38.73% and 59.26%, respectively, compared with the traditional RSS fingerprints algorithm.

We consider the problem of choosing design parameters to minimize the probability of an undesired rare event that is described through the average of n i.i.d. random variables. Since the probability of interest for near optimal design parameters is very small, one needs to develop suitable accelerated Monte-Carlo methods for estimating its value. One of the challenges in the study is that simulating from exponential twists of the laws of the summands may be computationally demanding since these transformed laws may be non-standard and intractable. We consider a setting where the summands are given as a nonlinear functional of random variables, the exponential twists of whose distributions take a simpler form than those for the original summands. We use techniques from Dupuis and Wang (Stochastics 76(6):481–508, 2004, Math Oper Res 32(3):723–757, 2007) to identify the appropriate Issacs equations whose subsolutions are used to construct tractable importance sampling (IS) schemes. We also study the closely related problem of estimating buffered probability of exceedance and provide the first rigorous results that relate the asymptotics of buffered probability and that of the ordinary probability under a large deviation scaling. The analogous minimization problem for buffered probability, under conditions, can be formulated as a convex optimization problem. We show that, under conditions, changes of measures that are asymptotically efficient (under the large deviation scaling) for estimating ordinary probability are also asymptotically efficient for estimating the buffered probability of exceedance. We embed the constructed IS scheme in gradient descent algorithms to solve the optimization problems, and illustrate these schemes through computational experiments.

Based on the reasonable determination of material constitutive relationship and interaction model between concrete and steel tube, a finite element model of T-shaped stiffened concrete-filled steel tube (TSCFST) stub column exposed to uniform fire was established, and model reliability was verified. Through the FE model, the working mechanism of the model in the whole process of loading was analyzed in detail. In the process of axial compression, the load distributions of steel tube and concrete, and the interaction between steel tube and concrete were discussed. This paper analyzed the effects of the main factors on residual bearing capacity of TSCFST stub columns, and put forward the calculation formula of the residual bearing capacity of TSCFST stub columns after uniform fire exposure.

Graphite anode materials are widely used in commercial lithium-ion batteries; however, the long electron/ion transportation path restricted its high energy storage. In this experiment, we designed a copper/graphite composite with a dual three-dimensional (3D) continuous porous structure combining used nonsolvent-induced phase separation and heat treatment, in which a large amount of graphite is embedded in the 3D porous copper/carbon architecture. In the novel structure, not only the electron and Li+ transmission performances are improved, but also the space of current collector is fully utilized. Meanwhile, carbonized polyacrylonitrile network stabilizes the interface between graphite and copper matrix. The obtained copper/graphite composite anode has an initial discharge capacity of 524.6 mAh·g−1, a holding capacity of 350 mAh·g−1 and excellent cycle stability (299.3 mAh·g−1 after 180 cycles at 0.1C rate), exhibiting good electrochemical performance. The experimental results show that the mass loading of the copper/graphite composite electrode material is about 4.39 mg·cm−2. We also envisage replacing graphite with other high-capacity active materials to fill the current collector, which can provide a reference for the future development of next-generation advanced electrodes.

A sub-sampling phase-locked loop (SSPLL) with loop bandwidth calibration is presented. By using a sub-sampling phase detector with gain calibration and a pulse width control circuit, the loop bandwidth deviation of the SSPLL can be reduced. This SSPLL is fabricated in a 40 nm CMOS process and its core area is 0.15mm2. The power consumption of the SSPLL is 5.81mW from a supply of 1.1V. The reference frequency is 75 MHz and the output frequency range of the SSPLL is 2.4~3.0GHz. The measured rms jitter is 2.02ps at the output frequency of 3.0GHz. With the calibration, the largest loop bandwidth deviation from 3.5MHz among five samples is reduced from −71.4% to −18.5% at 3.0GHz.

Metalorganic hydrides are a new class of hydrogen storage materials. Replacing the H of N–H or O–H functional groups using metal hydrides have been recently reported, which substantially improved the dehydrogenation properties of heteroaromatic organic hydrides by lowering their enthalpies of dehydrogenation (ΔHd), enabling dehydrogenation at much lower temperatures. Among the reported metalorganic hydrides, lithium carbazolide and sodium carbazolide appear to be the most attractive hydrogen storage/delivery material owing to its high hydrogen capacity (>6.0 wt%) and ideal ΔHd. Nevertheless, the interaction of carbazole and corresponding metal hydride to form metallo-carbazolide is a multistep process involving intensive ball milling and high temperature treatment, where the interaction was not investigated in detail. In this paper, both alkali metal hydrides and amides were employed to react with carbazole to synthesize corresponding carbazolides, aiming to broaden and optimize the synthetic method and understand the reaction mechanism. Our experimental results showed that around one equivalent of H2 or NH3 could be released from the reactions of carbazole and corresponding hydrides or amides, respectively. Instrumental spectroscopic analyses proved that metallo-carbazolides were successfully synthesized from all precursors. It is found that the alkali metal amides (i.e., LiNH2 and NaNH2) with stronger Lewis basicities as metal precursors could synthesize the metallo-carbazolides under milder conditions. Furthermore, quasi in situ nuclear magnetic resonance results revealed that alkali metal could replace H (H–N) gradually, donating more electrons to carbazole ring. Additionally, the solubilized alkali cation may unselectively interact with π-electron of aromatic systems of both carbazole molecules and carbazolide anions via electrostatic cation-π interactions.

To investigate the mutual effect of hydrogen, microstructures and stress concentration on the fatigue failure, fatigue behaviors of X65 steel welded joints in both air and saturated H2S solution were investigated at high cycle regime. The experimental result demonstrates that due to lower dislocation density observed by electron backscattered diffraction (EBSD), the fine grain heat affected zone (FGHAZ) is prone to induce cyclic strain localization and further lead to fatigue crack propagating along the FGHAZ in air. Furthermore, the quasi-cleavage with brittle-like fatigue striations and secondary crack on the fracture surface in saturated H2S solution is attributed to hydrogen embrittlement. Moreover, compared with base metal (BM) and FGHAZ, the weld metal (WM) and coarse grain heat affected zone (CGHAZ) are composed of bainite and martensite/austenite (M/A) phase, and more sensitive to hydrogen. Therefore, the fatigue crack is prone to grow along the interface between WM and CGHAZ under the normal applied stress.

Traditional open-switch fault diagnosis methods suffer from poor rapidity or robustness. To solve this issue, a new differential current observer-based fault diagnosis method is proposed in this article. With the designed differential observer, fault symptoms (residuals) can be generated and adopted for fault diagnosis easily. Considering that the residuals are sensitive to motor operating condition in the conventional model-based method due to model error, an adaptive fault detection threshold is designed. As a result, the false detection and missed detection caused by the change of working condition can be avoided, and stronger robustness against speed, load, and parameter variations can be achieved with superior rapidity compared with existing methods. Finally, the rapidity and robustness of the proposed fault diagnosis method are verified sufficiently through experimental results.

The classification tasks for numerical or categorical data have been well developed. However, the data collected in the real world are frequently the mixed type containing numerical and categorical values, and how to classify the mixed data quickly and efficiently is a critical yet challenging task. Existing classification models for mixed data usually treat the mixed data processing and subsequent classification as two independent phases, without considering their compatibility. By fusing the mixed data processing into a classification algorithm, this paper proposes an extended version of RBF-ELM (Radial Basis Function-Extreme Learning Machine), a Mixed Data RBF-ELM method (MD-RBF-ELM for short), which can achieve direct, fast, and efficient classification for mixed data. Specifically, a distance metric method for mixed data is firstly designed to calculate the distances between the input data and the RBF centers, and then these distances are used to train the network structure and weights of MD-RBF-ELM, thereby realizing the fusion of data processing with model learning. In addition, to alleviate the problem of MD-RBF-ELM’s unstable performance caused by randomly selecting the RBF centers, we propose an improved density peak clustering algorithm and use it to select the optimal RBF centers automatically and adaptively. Extensive experimental results on 34 data sets demonstrate that MD-RBF-ELM significantly enhances the classification performance (increasing 2.37% for F1-score, up to 14/34 for the number of best results, and reaching 2.4/8 for the averaged ranks), compared with seven state-of-the-art competitors.

It is worthwhile to carry out carbon emission reduction of transportation, which is an important sector of energy consumption. Thus, we used the expanded structural decomposition analysis model and input–output analysis to investigate the structural emission reduction of transportation in China. The results reveal that within the research interval, (1) the energy intensity effect (EIE) was a major factor for reducing carbon emissions from China’s transportation sector, which is caused by the improvement of energy efficiency. However, the input structure effect (ISE) on emission reduction is not apparent. Therefore, the utilization efficiency of transportation in various industries has not been significantly optimized. (2) The final demand effect (FDE) is the main determinant of carbon emission growth in transportation sector due to the steady growth of demand for transportation across all industries. Simultaneously, secondary industries play a major role in the FDE, followed by tertiary and primary industries. However, the direct consumption coefficient of primary and tertiary industries to the transportation sector demand is lower than that of the secondary industries (e.g., heavy industry). Therefore, the dynamic optimization of industrial structure is conducive to reducing carbon emissions caused by FDE. (3) The energy structure effect (ESE) is shown to restrain the growth of carbon emissions with an increasing trend, caused by the increase and decrease in the proportion of low-carbon and high-carbon energy consumption structure, respectively, in the energy supply side of transportation sector. Diesel oil, gasoline, kerosene, and liquefied natural gas were the main contributors to carbon emission reduction in ESE, while raw and cleaned coal did not play a role in reducing carbon emissions in ESE. This study can provide practical guidance for China’s transportation sector to implement emission reduction more accurately based on the energy type and industry level.

The unavoidable increase in train speed and load, as well as the aging of railway facilities, is requiring more and more attention to rail defects detection. As a promising tool for rail, in-service high-speed inspection, guided wave–based detection technologies have been developed in succession by researches in the past two decades. However, there is a lack of a systematic review on the developments and performances of these technologies. This article reviews ultrasonic rail inspection methods comprehensively with the focus on the state-of-the-art technologies based on guided wave. Different excitation options, including train wheel, electromagnetic acoustic transducer, pulsed laser, air-coupled, and contact piezoelectric transducer, are described, respectively, along with their inspection sensitivities, regions, and potential speeds. Finally, future challenges and prospects are discussed to a certain extent to provide references for researchers in this area.