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Developing bioactive materials for bone implants to enhance bone healing and bone growth has for years been the focus of clinical research. Barium titanate (BT) is an electroactive material that can generate electrical signals in response to applied mechanical forces. In this study, a BT piezoelectric ceramic coating was synthesized on the surface of a TC4 titanium alloy, forming a BT/TC4 material, and low-intensity pulsed ultrasound (LIPUS) was then applied as a mechanical stimulus. The combined effects on the biological responses of MC3T3-E1 cells were investigated. Results of scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction showed that an uniform nanospheres -shaped BT coating was formed on TC4 substrate. Piezoelectric behaviors were observed using piezoelectric force microscopy with the piezoelectric coefficient d33 of 0.42 pC/N. Electrochemical measures indicated that LIPUS-stimulated BT/TC4 materials could produce a microcurrent of approximately 10 μA/cm2. In vitro, the greatest osteogenesis (cell adhesion, proliferation, and osteogenic differentiation) was found in MC3T3-E1 cells when BT/TC4 was stimulated using LIPUS. Furthermore, the intracellular calcium ion concentration increased in these cells, possibly because opening of the L-type calcium ion channels was promoted and expression of the CaV1.2 protein was increased. Therefore, the piezoelectric BT/TC4 material with LIPUS loading synergistically promoted osteogenesis, rending it a potential treatment for early stage formation of reliable bone-implant contact., Graphical abstract Image 1, Highlights • BT piezoelectric ceramic coating was hydrothermally synthesized on the surface of TC4 titanium alloy to form BT/TC4 material. • The BT/TC4 material under ultrasound loading could produce a microcurrent of approximately 10 μA/cm2. • The piezoelectric BT/TC4 material with LIPUS loading synergistically promoted biological activity of MC3T3-E1 cells. • Electroactivity of BT/TC4 material stimulated by LIPUS loading promoted the intracellular Ca2+ influx in MC3T3-E1 cells.

The U-shaped network is one of the end-to-end convolutional neural networks (CNNs). In electron microscope segmentation of ISBI challenge 2012, the concise architecture and outstanding performance of the U-shaped network are impressive. Then, a variety of segmentation models based on this architecture have been proposed for medical image segmentations. We present a comprehensive literature review of U-shaped networks applied to medical image segmentation tasks, focusing on the architectures, extended mechanisms and application areas in these studies. The aim of this survey is twofold. First, we report the different extended U-shaped networks, discuss main state-of-the-art extended mechanisms, including residual mechanism, dense mechanism, dilated mechanism, attention mechanism, multi-module mechanism, and ensemble mechanism, analyze their pros and cons. Second, this survey provides the overview of studies in main application areas of U-shaped networks, including brain tumor, stroke, white matter hyperintensities (WMHs), eye, cardiac, liver, musculoskeletal, skin cancer, and neuronal pathology. Finally, we summarize the current U-shaped networks, point out the open challenges and directions for future research.

While halide perovskite electronics are rapidly developing, they are greatly limited by the inferior charge transport and poor stability. In this work, effective surface charge transfer doping of vapor-liquid-solid (VLS)-grown single-crystalline cesium lead bromide perovskite (CsPbBr3) nanowires (NWs) via molybdenum trioxide (MoO3) surface functionalization is achieved. Once fabricated into NW devices, due to the efficient interfacial charge transfer and reduced impurity scattering, a 15× increase in the field-effect hole mobility (μh) from 1.5 to 23.3 cm2/(V s) is accomplished after depositing the 10 nm thick MoO3 shell. This enhanced mobility is already better than any mobility value reported for perovskite field-effect transistors (FETs) to date. The photodetection performance of these CsPbBr3/MoO3 core-shell NWs is also investigated to yield a superior responsivity (R) up to 2.36 × 103 A/W and an external quantum efficiency (EQE) of over 5.48 × 105% toward the 532 nm regime. Importantly, the MoO3 shell can provide excellent surface passivation to the CsPbBr3 NW core that minimizes the diffusion of detrimental water and oxygen molecules, improving the air stability of CsPbBr3/MoO3 core-shell NW devices. All these findings evidently demonstrate the surface doping as an enabling technology to realize high-mobility and air-stable low-dimensional halide perovskite devices.

Designing highly efficient and low-cost electrocatalysts for oxygen evolution reaction is important for many renewable energy applications. In particular, strain engineering has been demonstrated as a powerful strategy to enhance the electrochemical performance of catalysts; however, the required complex catalyst preparation process restricts the implementation of strain engineering. Herein, we report a simple self-template method to prepare hierarchical porous Co3O4 nanowires (PNWs) with tunable compressive strain via thermal-oxidation-transformation of easily prepared oxalic acid-cobalt nitrate (Co(NO3)2) composite nanowires. Based on the complementary theoretical and experimental studies, the selection of proper solvents in the catalyst preparation is not only vital for the hierarchical structural evolution of Co3O4 but also for regulating their compressive surface strain. Because of the rich surface active sites and optimized electronic Co d band centers, the PNWs exhibit the excellent activity and stability for oxygen evolution reaction, delivering a low overpotential of 319 mV at 10 mA·cm−2 in 1 M KOH with a mass loading 0.553 mg·cm−2, which is even better than the noble metal catalyst of RuO2. This work provides a cost-effective example of porous Co3O4 nanowire preparation as well as a promising method for modification of surface strain for the enhanced electrochemical performance.

Quasi-2D halide perovskites, especially the Ruddlesden-Popper perovskites (RPPs), have attracted great attention because of their promising properties for optoelectronics; however, there are still serious drawbacks, such as inefficient charge transport, poor stability, and unsatisfactory mechanical flexibility, restricting further utilization in advanced technologies. Herein, high-quality quasi-2D halide perovskite thin films are successfully synthesized with the introduction of the unique bication ethylenediammonium (EDA) via a one-step spin-coating method. This bication EDA, with short alkyl chain length, can not only substitute the typically bulky and weakly van der Waals-interacted organic bilayer spacer cations forming the novel Dion-Jacobson phase to enhance the mechanical flexibility of the quasi-2D perovskite (e.g., EDA(MA)n-1PbnI3n+1; MA = CH3NH3+) but also serve as a normal cation to achieve the more intact films (e.g., (iBA)2(MA)3-2x(EDA)xPb4I13). When fabricated into photodetectors, these optimized EDA-based perovskites deliver an excellent responsivity of 125 mA/W and a fast response time down to 380 μs under 532 nm irradiation. More importantly, the device with the Dion-Jacobson phase perovskite can be bent down to a radius of 2 mm and processed with 10,000 cycles of the bending test without any noticeable performance degradation because of its superior structure to RPPs. Besides, these films do not exhibit any material deterioration after ambient storage for 30 days. All these performance parameters are already comparable or even better than those of the state-of-the-art RPPs recently reported. This work provides valuable design guidelines of the quasi-2D perovskites to obtain high-performance flexible photodetectors for next-generation optoelectronics.

In this research, keratin was extracted from the waste wool fiber using sodium sulfite as dissolving agent. The reaction results showed that the optimum conditions were as follows: the dosage of so...

Flight testbeds with multiple unmanned aerial vehicles (UAVs) are especially important to support research on multi-vehicle-related algorithms. The existing platforms usually lack a generic and complete solution allowing for software and hardware design. For such a purpose, this paper presents the design and implementation of a comprehensive multi-camera-based testbed for 3-D tracking and control of UAVs. First, the testbed software consists of a multi-camera system and a ground control system, which performs image processing, camera calibration, 3-D reconstruction, pose estimation, and motion control. In the multi-camera system, the positions and orientations of UAVs are first reconstructed by using epipolar geometric constraints and triangulation methods and then filtered by an extended Kalman filter (EKF). In the ground control system, a classical proportional–derivative (PD) controller is designed to receive the navigation data from the multi-camera system and then generates control commands to the target vehicles. Then, the testbed hardware employs smart cameras with field-programmable gate array (FPGA) modules to allow for real-time computation at a frame rate of 100 Hz. Lightweight quadcopter Parrot Bebop drones are chosen as the target UAVs, which does not require any modification to the hardware. Artificial infrared reflective markers are asymmetrically mounted on target vehicles and observed by multiple infrared cameras located around the flight region. Finally, extensive experiments are performed to demonstrate that the proposed testbed is a comprehensive and complete platform with good scalability applicable for research on a variety of advanced guidance, navigation, and control algorithms.

This paper presents a model for predicting the water temperature of the reservoir incorporating with solar radiation to analyze and evaluate the water temperature of large high-altitude reservoirs in western China. Through mutual information inspection, the model shows that the dependent variable has a good correlation with water temperature, and it is added to the sample feature training model. Then, the measured water temperature data in the reservoir for many years are used to establish the support vector regression (SVR) model, and genetic algorithm (GA) is introduced to optimize the parameters, so as to construct an improved support vector machine (M-GASVR). At the same time, root-mean-square error, mean absolute error, mean absolute percentage error, and Nash–Sutcliffe efficiency coefficient are used as the criteria for evaluating the performance of SVR model, ANN model, GA-SVR model, and M-GASVR model. In addition, the M-GASVR model is used to simulate the water temperature of the reservoir under different working conditions. The results show that ANN model is the worst among the four models, while GA-SVR model is better than SVR model in terms of metric, and M-GASVR model is the best. For non-stationary sequences, the prediction model M-GASVR can well predict the vertical water temperature and water temperature structure in the reservoir area. This study provides useful insights into the prediction of vertical water temperature at different depths of reservoirs.

Designing a controller for the docking maneuver in Probe-Drogue Refueling (PDR) is an important but challenging task, due to the complex system model and the high precision requirement. In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control (ILC) is adopted in this paper. First, Additive State Decomposition (ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of NonMinimum Phase (NMP) by separating these features into two subsystems (a primary system and a secondary system). After system decomposition, an adjoint-type ILC is applied to the Linear Time-Invariant (LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback is used to stabilize the secondary system with input saturation. The two controllers designed for the two subsystems can be combined to achieve the original control goal of the PDR system. Furthermore, to compensate for the receiver-independent uncertainties, a correction action is proposed by using the terminal docking error, which can lead to a smaller docking error at the docking moment. Simulation tests have been carried out to demonstrate the performance of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type ILC in the docking control of PDR. Keywords: Additive state decomposition, Adjoint operator, Docking control, Iterative learning control, Probe-drogue refueling, Stable inversion

Object detection is an important topic for visual data processing in the visual computing area. Although a number of approaches have been studied, it still remains a challenge. There is a suitable way to promote image classifiers by blending training with blended images and corresponding blended labels. However, our experiments show that directly moving existing blending methods from classification to object detection will cause the training process become harder and eventually will lead to a bad performance. Inspired by our discovery, this paper presents a multi-phase blending method with incremental blending intensity to improve the accuracy of object detectors and achieve remarkable improvements. Firstly, to adapt blending method to detection task, we propose a smoothly scheduled and incremental blending intensity to control the degree of multi-phase blending. Based on the above dynamic coefficient, we propose an incremental blending method, in which the blending intensity is smoothly increased from zero to full. Therefore, more complex and various data can be created to achieve the goal of regularizing the network. Secondly, we also design an incremental hybrid loss function to replace the original loss function. The blending intensity in our loss function increases smoothly, which is controlled by our scheduled coefficient. Thirdly, we further discard more negative examples in our multi-phase training process than other typical training methods and processes. By doing so, we can regularize the neural network to enhance generalization capability with data diversity and eventually to improve the accuracy in object detection. Another advantage is that there is no negative effect on evaluation because our method is just applied during the training process. Typical experiments show the proposed method improves the generalization of the detection networks. On PASCAL VOC and MS COCO, our method outperforms the state-of-the-art RFBNet of one-stage detectors for real-time processing.

In this article, we propose new heuristics for minimising the amount of XOR gates required to compute a system of linear equations in GF(2). We first revisit the well known Boyar-Peralta strategy and argue that a proper randomisation process during the selection phases can lead to great improvements. We then propose new selection criteria and explain their rationale. Our new methods outperform state-of-the-art algorithms such as Paar or Boyar-Peralta (or open synthesis tools such as Yosys) when tested on random matrices with various densities. They can be applied to matrices of reasonable sizes (up to about 32 × 32). Notably, we provide a new implementation record for the matrix underlying the MixColumns function of the AES block cipher, requiring only 94 XORs., IACR Transactions on Cryptographic Hardware and Embedded Systems, Volume 2020, Issue 1

Currently, the time of endurance of electric multicopters is still too short for many mission requirements, and optimizing the efficiency of the propulsion system is considered as an effective way to overcome this problem. This paper proposes a practical method to help designers quickly select the optimal products of the propulsion system to maximize the multicopter efficiency under the desired flight condition. First, the modeling methods for the components of the propulsion system are studied respectively to describe the optimization problem mathematically. Second, methods are proposed to find optimal motor and propeller combination with the maximum thrust efficiency according to the given design requirements. Finally, factors that may affect the hovering time of multicopters are analyzed, and the optimal battery parameters are obtained for maximizing the multicopter endurance. Experiments and simulations are performed to demonstrate the effectiveness and practicability of the proposed method.

Autonomous Aerial Refueling (AAR) is vulnerable to various failures and involves cooperation among autonomous receivers, tankers and remote pilots. Dangerous flight maneuvers may be executed when unexpected failures or command conflicts happen. To solve this problem, a failsafe mechanism based on State Tree Structures (STS) is proposed. The failsafe mechanism is a control logic that guides what subsequent actions the autonomous receiver should take, by observing real-time information of internal low-level subsystems such as guidance and drogue&probe and external instructions from tankers and pilots. To generate such a controller using STS, the AAR procedure is decomposed into several modes, and safety issues related with seven low-level subsystems are summarized. Then common functional demands and safety requirements are textually described. On this basis, the AAR plants and specifications are modeled by STS, and a supervisor is synthesized to control the AAR model. To prove its feasibility and correctness, a simulation environment incorporating such a logic supervisor is built and tested. The design procedures presented in this paper can be used in decision-making strategies for similar flight tasks. Supporting materials can be downloaded in Github, [ https://github.com/KevinDong0810/Failsafe-Design-for-AAR-using-STS ] including related software, input documents and output files.

Feature selection is a critical preprocess for constructing model in computer vision and machine learning, yet it is difficult to simultaneously satisfy both reducing features’ number and maintaining classification accuracy. Toward this problem, we propose dividing-based many-objective evolutionary algorithm for large-scale feature selection (DMEA-FS). Firstly, four novel objectives are established for exploring the optimal feature’s subsets. Meanwhile, we design two structures of wrapper for high accuracy and filter for low computation cost in DMEA-FS. Secondly, two new recombination methods are presented for rapid convergence. Mapping-based variable dividing is presented for precise related variables. Thirdly, based on minimum Manhattan distance, a triangle-approximating decision-making is proposed for assisting users’ determination with/without preference information. Numerical experiments against several state-of-the-art feature selection algorithms demonstrate that the proposed DMEA-FS outperforms its competitors in terms of both classification accuracy and metrics of features’ number.

Few-atom gold nanoclusters (AuNCs) have been fabricated and used for various fields owing to their remarkable optical and photophysical features. However, the rational design for the antibody-mediated synthesis of fluorescent AuNCs for direct antigen-antibody reactions remains unexplored. In this work, immunoglobulin G (IgG)-functionalized AuNCs (IgG-AuNCs) were successfully prepared via a facile and fast biomineralization process. The generated IgG-AuNCs can emit intense red fluorescence with a high photoluminescence quantum yield. Besides strong emission, the bioactivity of IgG on the IgG-AuNCs can be retained. Surface plasmon resonance measurements suggested that IgG-AuNCs can bind to goat anti-human IgG with an affinity constant of 6.21 × 10-8 M. A simple detection method was then developed using a dot-blot immunoassay with IgG-AuNCs as fluorescent tags. Experimental results confirmed that the IgG-AuNC-based fluorescent reporters had many advantages such as low nonspecific adsorption and good photostability, offering immense potential for the development of efficient biosensors. This work can be extended to other specific antibodies to produce multifunctional AuNCs and utilized to detect and monitor targeted analytes and biological events of interest.

The preparation of human hair keratin/calcium alginate blend films was used of calcium chloride as coagulation bath. Two components in the blend film have good compatibility due to the pres...

There exist at least two viewpoints on the internal model principle (IMP), namely the cancelation viewpoint and the geometrical viewpoint. However, neither of them is applicable to repetitive contr...

Alginate/gelatin blended hydrogel fibers were successfully prepared based on sodium alginate cross-linked by Ca2+ and gelatin cross-linked by oxidized starch through the wet spinning process. The structure, surface morphologies, swelling properties of alginate hydrogel fibers and alginate/gelatin blended hydrogel fibers were examined by Fourier transform infrared analysis (FTIR), cross-linking degree test, element analysis test and scanning electron microscopy (SEM). While mechanical properties test, water absorption and retention properties test provide the mechanical, water absorption and retention properties. FTIR, cross-linking degree and element analysis results indicate that there is gelatin remaining in alginate/gelatin blended hydrogel fibers. However, the cross-linking of gelatin by oxidized starch is not completed with some dissociative gelatin solvated in the coagulation bath. Among the mentioned fibers, FS10G2 is the best to improve the properties of alginate/gelatin blended hydrogel fibers with an optimal mass ratio of sodium and gelatin. The excessive additive amount of gelatin leads to the aggregation of gelatin in alginate/gelatin blended hydrogel fibers, making the worse compatibility between alginate and gelatin. It also destroys the mechanical properties of alginate/gelatin blended hydrogel fibers. These alginate/gelatin blended hydrogel fibers might have a potential application in the biomedical fields.

Microgrid (MG) usually operates in medium/low-voltage systems, where the line impedance parameters are mainly resistive, and traditional P–f / Q–U droop control is no longer applicable. When the virtual complex impedance method is adopted, the resistance component of line impedance can be counteracted by a virtual negative resistance. Unfortunately, the improper design of the virtual negative resistance will result in an unstable system due to the problem of line impedance parameter drift and estimation error. According to the line parameters characteristics of the off-grid MG with medium/low voltage, the P–U / Q–f droop control is adopted in this study, where the virtual complex impedance composed of a virtual negative inductance and a virtual resistance is introduced in the control loop. The virtual negative inductance is used to reduce the power coupling caused by the inductive component of the system impedance. The virtual resistance is implemented to enhance the resistive component and adjust the impedance matching degree for raising the accuracy of power sharing. However, the power sharing is still affected by the system hardware parameters; meanwhile, the voltage deviation caused by the droop control and the virtual impedance exists. In this study, a novel voltage stabilization and power sharing control method based on the virtual complex impedance is investigated to achieve accurate power sharing without the impact of hardware parameters variations and to improve the voltage quality. Moreover, the small-signal model of the inverter-based off-grid MG with the proposed controller is established, which can be utilized to analyze the stability and dynamic performance of the system. Meanwhile, the control parameters can be sequentially determined. Analysis shows that the strategy is robust against the line-impedance parameter drift and the estimation error and has a large stability margin and fast dynamic-response speed. Finally, numerical simulations and experimental results are provided to verify the effectiveness of the proposed control method in comparison with traditional frameworks.

The effects of drought, a devastating natural disaster, have expanded due to intense global warming. Therefore, studying the spatio-temporal evolution and formation mechanism of drought is urgent and important. In this paper, the standardized precipitation evapotranspiration index (SPEI) of the source region of Yellow River was calculated for the years 1961–2015, and the heuristic segmentation and Modified Mann-Kendall methods were used to study the change points and trends of precipitation, temperature, and SPEI. The empirical orthogonal function was used to study the major modes of the SPEI, and cross wavelet analysis was used to demonstrate the relationship between SPEI and large-scale climate factors. The results initially demonstrated no change point and an insignificant positive trend in precipitation. Temperature revealed a significant increasing trend, and a change point was detected in 1997. SPEI indicated a change point in 1993, and a significant increasing trend was observed thereafter. These findings reveal that drought in the study area is highly related to the El Niño-Southern Oscillation. The North Atlantic and Arctic oscillations caused similar impacts on drought. By comparison, changes in SPEI were slightly related to the Pacific decadal oscillation. These results provide useful information for evaluating drought changes in the study area, early warning of drought, and water resource management.

A nagging problem for the decompostion of photocatalyst organic carrier can be expected to be resolved by shielding effect from our yolk-porous shell nanospheres. The nanospheres were synthesized by a facile strategy: polyporrole (PPy) and silver were deposited together on TiO2 by chemical oxidative polymerization; then PPy/Ag-coated TiO2 nanoparticles were encapsulated in silicon dioxide (SiO2) shell with polyethylene glycol (PEG) as a pore-forming agent via sol-gel method based on hydrolysis of tetraethyl orthosilicate (TEOS). After removing intermediary PPy between yolk and shell by calcination and washing off PEG in shell, yolk-porous shell (SiO2@void@Ag/TiO2) nanospheres were formed. The voids in SiO2@void@Ag/TiO2 can serve as photocatalytic reactors. The channels in porous shell at outer layer provide passages for light transmission, dye molecule accessing and degradants out. More importantly, the euphotic and porous shell exhibited an impressive protection to organic carrier, lest unfavorable decomposition occurred. Yolk-porous shell nanospheres showed commendable performance with >99.5% of dye removal efficiency under 3 h visible light irradiation, higher than pristine TiO2 and Ag/TiO2 nanoparticles, due to the synergy effect of robust adsorption capacity and photocatalysis. Our work could provide a good strategy for developing novel carrier-based photocatalysts for environmental remediation application, which can be readily extended to the combination of other nanophotocatalysts and organic carriers for enhancing sustainable photocatalytic performance.

Contraction mapping methods do not need to know about the concrete form of plant models like the Lyapunov method. This is the biggest advantage over other methods in the field of iterative learning control for example. However, it is difficult to use such a tool to analyze repetitive control systems. This paper proposes a contraction mapping method based on spectral theory to design a saturated repetitive controller for a class of nonlinear systems, where the derived necessary and sufficient condition on the spectral radius can reduce the conservatism as much as possible. The feasibility of our work is demonstrated through a robotic manipulator tracking example.

In a probe–drogue aerial refueling system, the drogue is affected not only by wind disturbances but also by strong disturbances from the tanker vortex and receiver forebody bow wave. Along with the aerodynamic disturbances acting on the receiver aircraft, it is difficult for the probe to capture the moving drogue in the docking stage. This paper studies the model of the probe–drogue aerial refueling system under aerodynamic disturbances, and proposes docking control method based on iterative learning control to compensate for the docking errors caused by aerodynamic disturbances. For receiver aircraft with different maneuverability, three control strategies are proposed to achieve a trade-off between safety and control precision. Furthermore, a practical method is proposed to predict the initial value of the learning controllers, which can significantly improve the iterative learning speed of the proposed methods. Finally, simulations demonstrate that the proposed control methods are simple and efficient for the docking control of autonomous probe–drogue aerial refueling.

With a focus on the problems of active power sharing and voltage deviation of parallel-connected inverters in an islanded micro-grid (MG), in this study, the power-voltage droop controller and the inner voltage regulator are redesigned based on a total sliding-mode control (TSMC) technique. As for the power-voltage droop control loop, a droop control relation error between the active power and the point-of-common-coupling (PCC) voltage amplitude is defined. Then, the TSMC scheme is adopted to reach the new droop control relation, where the active power sharing and voltage amplitude recovery can be achieved simultaneously. Owing to the faster dynamic response and strong robustness provided by the TSMC framework, high-precision active power sharing during transient state also can be ensured without the influence of line impedances. The power allocation error can be improved by more than 81.2% and 50% than the conventional and proportional-integral (PI)-based droop control methods, respectively, and the voltage deviation rate can be reduced to 0.16%. Moreover, a small-signal model of the TSMC-based droop-controlled system is established, and the influences of control parameters on the system stability and the dynamic response are also investigated. The effectiveness of the proposed control method is verified by numerical simulations and experimental results.

2D metal phosphide loop-sheet heterostructures are controllably synthesized by edge-topological regulation, where Ni2 P nanosheets are edge-confined by the N-doped carbon loop, containing ultrafine NiFeP nanocrystals (denoted as NiFeP@NC/Ni2 P). This loop-sheet feature with lifted-edges prevents the stacking of nanosheets and induces accessible open channels for catalytic site exposure and gas bubble release. Importantly, these NiFeP@NC/Ni2 P hybrids exhibit a remarkable oxygen evolution activity with an overpotential of 223 mV at 20 mA cm-2 and a Tafel slope of 46.1 mV dec-1 , constituting the record-high performance among reported metal phosphide electrocatalysts. The NiFeP@NC/Ni2 P hybrids are also employed as both anode and cathode to achieve an alkaline electrolyzer for overall water splitting, delivering a current density of 10 mA cm-2 with a voltage of 1.57 V, comparable to that of the commercial Pt/C||RuO2 couple (1.56 V). Moreover, a photovoltaic-electrolysis coupling system can as well be effectively established for robust overall water splitting. Evidently, this ingenious protocol would expand the toolbox for designing efficient 2D nanomaterials for practical applications.

Unmanned Aerial Vehicle (UAV) has a rapid growth in recent years. An efficient air traffic system for UAVs working at a low altitude which is rich of obstacles is desired. However, as far as we know, the work on the generation of such an air traffic network at a low altitude lacks attention. It is expected to develop an algorithm in order to generate a traffic network automatically similar to the complex road traffic network by taking a matrix or image marked with obstacles as input. Considering those situations above, this paper addresses (i) automatic air traffic network generation that is suitable for UAVs working at a low altitude, (ii) several line segments (airlines) fitting for a curve, and (iii) a graph modeling. A simple experiment is also performed to show the effectiveness of the method proposed.

For most people on the ground, facing an unwanted drone buzzing around overhead, there is not a lot that we can do, especially if it is out of gun (radio wave gun or shotgun) range. A solution to this is to use intercept drones that seek out and bring down other drones. In order to make the interception autonomous, an image-based visual servo algorithm is designed with a forward-looking monocular camera. The control command, namely the angular velocity and thrust, is generated for intercept drones to implement accurate and fast interception. The proposed method is demonstrated in both hardware-in-the-loop simulation and demonstrative flight experiments.

Microplastics have been of great concern in recent years due to bioaccumulation and their toxic effects on organisms. However, few studies have focused on microplastics in the natural river ecosystem and the relationship between microplastics and microbes. Therefore, to understand the concentration and characteristics of microplastics and explore the impact of microplastics on the microbial community, sediment samples were collected from the Huangjinxia Reservoir, which is the water source of a water diversion project in western China. Results showed that the concentration of microplastics in the study area ranged from 233.33 ± 70.24 items·kg−1 to 870 ± 238.12 items·kg−1, with an average of 558.10 ± 291.45 items·kg−1. After clustering the sediments according to the microplastic concentration, there was a significant difference in the Chao1 index of microbial community between groups, indicating that microplastics might have affected microbial diversity of the sediments. Additionally, Anosim, MRPP, and Amova analyses indicated that microplastics might have an impact on the structure and composition of microbial communities. Moreover, function prediction assays suggested that microplastics might have differential impacts on various microbial community functions. To our knowledge, this is the first study to explore the impact of microplastics on microbes in sediments of a natural river ecosystem, providing a basis for further study of the interaction between microplastics and microbes in similar habitats.

Chitosan was added to cotton fabrics coated with sodium phytate and 3-Aminopropyl triethoxysilane (APTES) to further enhance the fire retardant property by the layer-by-layer (LBL) assembly. The surface morphologies before and after flaming and structure of coated cotton fabrics were evaluated by scanning electron microscopy and Fourier transform infrared analysis. The thermal degradation properties, fire retardancy, and combustion properties of this system were investigated by Fourier transform infrared analysis, thermogravimetric analysis coupled with Fourier transform infrared analysis (TG-FTIR) in the N2 atmosphere, the thermogravimetric analysis in air atmosphere, limiting oxygen index test (LOI), vertical burning test and cone calorimeter test, respectively. Sodium phytate, chitosan and hydrolyzed APTES were successfully deposited and well distributed on the surface of cotton fibers. The LOI value of the coated cotton fabric with 15BL was 29.0%, and it extinguished at once in the vertical burning test, while the coated cotton fabrics with 5BL and 10BL were burned out. The deposition of sodium phytate, chitosan and hydrolyzed APTES led to the decrease of heat release rate, total heat release, total smoke release and smoke production rate. The promoted fire retardancy was assigned to thermally stable residuals formed on the surface of cotton fibers, which prohibited heat/mass transfer from combustion zone to polymer degradation zone. TG-FTIR results indicated that flame retardant cotton fabrics produced more nonflammable gases (CO2 and H2O) and less combustible gases than them of the control sample did, increasing flame retardant properties of flame retardant cotton fabrics.

Velocity estimation is essential for multicopters to guarantee flight stability and maneuverability. For such a purpose, this paper proposes a new method for multicopter velocity estimation based on visual and inertial information in GPS-denied or confined environments. In this method, no map, artificial landmark of the environment is required, and only the off-the-shelf onboard sensors in a multicopter including a low-cost Inertial Measurement Unit (IMU), a downward-looking monocular camera and an ultrasonic range finder facing downwards are exploited to constitute the vision motion constraint. This constraint connects metric velocity with the point correspondences between successive images in which an efficient approach based on Mean Shift (MS) algorithm is developed to detect outliers and select optimal matching points. Then, it is theoretically verified that the estimation system is observable based on observability analysis. Furthermore, combined with the vision motion constraint and a multicopter dynamic model, the metric velocity is estimated using a standard Linear Kalman Filter (LKF). Finally, the proposed method is tested with a collection of synthetic data from simulation as well as flight experiments using real data from DJI Matrice 100 and Guidance. The simulation and experimental results indicate that the proposed method can accurately estimate the velocity of the multicopter in GPS-denied or confined environments.

Low-cost and highly effective transition metal oxides are being widely researched as one of the most promising electrocatalysts for the oxygen evolution reaction (OER). However, traditional transition metal oxides suffer from sluggish reaction kinetics due to their intrinsically poor electronic conductivity. Herein, we demonstrate a facile polydopamine-assisted carburization strategy for the confined synthesis of novel NiOx/Ni ultrathin heterostructured nanosheets. Benefiting from the large exposed surface area and fast charge transfer, the obtained ultrathin NiOx/Ni heterostructured nanosheets exhibit an overpotential of 358 mV at a current density of 10 mA cm-2 and a small Tafel slope of 51 mV dec-1, outperforming other reported representative nickel oxide based materials and commercial Ir/C catalysts. In addition, a sustainable and efficient overall water-splitting electrolyzer integrated ultrathin NiOx/Ni nanosheets with commercial Pt/C can be effectively and stably driven by solar cells.

Relative pose estimation is critical for collaborative multi-agent systems. To achieve accurate and low-cost localization in cluttered and GPS-denied environments, we propose a novel relative pose estimation system based on a designed active infrared coded target. Specifically, each agent is equipped with a forward-looking monocular camera and a unique infrared coded target. The target with the unique lighted LED arrangement is detected by the camera and processed with an efficient decoding algorithm. The relative pose between the agent and the camera is estimated by combining a PnP algorithm and a Kalman filter. Various experiments are performed to show that the proposed pose estimation system is accurate, robust and efficient in cluttered and GPS-denied environments.

This paper presents an additive-state-decomposition-based attitude tracking control method for a class of bank-to-turn aerial vehicles subject to unknown disturbances and nonlinear coupling. This method ‘additively’ decomposes the original tracking problem into two more tractable problems, namely a tracking problem for a deterministic nonlinear ‘primary’ system, and a disturbance rejection problem for a linear time-invariant ‘secondary’ system. Based on the decomposition, a backstepping controller is designed for the primary system to track the reference attitude signal, and a proportional-integral controller is applied to the secondary system to compensate for the disturbances. Finally, the two designed controllers are combined to achieve the original control objective. By using additive state decomposition, the proposed control method with two degrees of freedom can consider tracking task and disturbance rejection task respectively. Simulation results illustrate that the proposed controller can track the reference attitude signal and compensate for disturbances meanwhile. Additionally, the ASD-based controller outperforms the traditional backstepping controller in the presence of unknown disturbances and input delay, and the robustness of the full system can be improved by adjusting the controller parameters of the secondary system.

A convenient oil-water separator of polybutylmethacrylate/graphene (PBMA/GE)-deposited polyethylene terephthalate (PET) nonwoven was fabricated by a casting coating method. The resulting nonwovens were characterized by Scanning electron microscopy (SEM), Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS) and contact angle measurements. The results reveal a hydrophobic and oleophilic surface with micro-nanoscale hierarchical network structures was formed by PBMA, GE and PET fiber web. Furthermore, we demonstrate the separation of oil–water mixtures using a facile apparatus, solely driven by gravity. As-prepared PBMA/GE-coated nonwovens exhibit high oil-water separation efficiency of over 95% up to eight cycles. Our finding as a facile, inexpensive, high-performance oil-water separator have a promising practical application in the oil spill cleanup and the removal of organic pollutants from water surface.

In order to ensure the normal operation and increase the safety, reliability and mission dependability of quadcopters, the fault detection and diagnosis is very important. In this paper, the problem of fault detection and diagnosis is investigated for the homogenous quadcopter team subject to non-negligible wind disturbance. However, it is difficult to accurately diagnose the fault, because it is coupled with the wind disturbance, whose effect is difficult to eliminate. For this problem, first, the observability is analyzed for the sum effect of the fault and the wind disturbance of one quadcopter, for the fault of one quadcopter, and for the fault of the homogenous quadcopter team, respectively. By analysis, the fault in the situation of the homogenous quadcopter team is observable under some reasonable assumptions. Based on this, a procedure is further proposed to detect and diagnose the fault for the homogenous quadcopter team, eliminating the effect of the wind disturbance. Finally, the simulation demonstrates the effectiveness of the proposed procedure.

Based on the theory of complex variable functions, the stress field in an infinite matrix containing an arbitrary shape inclusion with a functionally graded coating is analyzed. The elastic properties in the functionally graded coating change continuously and arbitrarily along the normal direction of the inclusion. By using the method of piecewise homogeneous layers and the technique of conformal mapping, the complex potential functions in the matrix, coating and inclusion are derived in the form of Laurent series and Faber series, respectively. The influences of different varying Young’s modulus on the interfacial stresses are discussed by numerical examples for various shape inclusions, including ellipse, triangle, square and rectangle. It is shown that the magnitude and distribution of interfacial stresses for arbitrary shape inclusions can be successfully designed and controlled by adding a functionally graded coating with proper varying elastic properties along the normal direction. The results for some special cases are compared with previous literature and found in good agreement.

Application of complementary relationship (CR) approaches using only routine meteorological data is a very convenient method of estimating actual evapotranspiration (ETa). Reanalysis datasets and remote sensing data provide good tools to overcome the difficulties in obtaining observation data. This study of the Hotan River Basin (HRB) in northwest China serves as a prime example for estimation of ETa during 2006–2014 by using the modified generalized CR. Based on comparison and analysis, the maximum potential evaporation calculated by the Penman-based equation was adopted. The estimated ETa rates were verified using a regional water balance method at annual time scales because of the limited available data. The calibration parameter was calibrated based on the elevation and underlying surface types. The mean annual ETa ranged from 2.3 mm to 800 mm during 2006–2014. ETa rates in the plains regions were higher than those in the mountainous regions. Most of ETa was concentrated in the months of May to September. A water deficit occurred in the middle and lower regions, while a water surplus occurred in the upper regions. This study not only provided a new concept for calibration, but also a potential solution for different underlying surfaces and time scales.

Solar thermal membrane distillation is a promising technology for low-cost desalination and water purification. A novel photo-thermal conversion membrane composited by polyacrylonitrile (PAN) nanofiber layer and polyvinylidene fluoride (PVDF) with antimony tin oxide (ATO) as solar-thermal nanoparticles (PVDF-ATO/PAN nanofiber membrane) was fabricated by sequent electrospinning techniques. The dual-layer membrane with hydrophilic PAN and hydrophobic PVDF-ATO was examined by microscopic evaluation and contact angle measurement. In situ temperature increase on membrane superficial layer (PVDF-ATO) driven by solar light illumination was confirmed by thermal imaging system. PAN layer provides water transport channels for facilitating water flow of highly efficient membrane distillation and avoids the salting assembly during the vigorous evaporation. With the help of hydrophilic PAN layer, our PVDF-ATO/PAN nanofiber membrane can fulfill highly effective localized heating transfer with an evaporate rate of 0.93 kg m−2 h−1, showing great potential for low-cost water desalination and scalable water purification.

The emergence of drug-resistant bacterial strains has challenged the use of traditional antibiotic therapies. Therefore, there is an urgent need to develop novel antibacterial materials or methods to effectively eradicate bacteria. Herein, we demonstrated a novel antibacterial system consisting of cupric oxide nanoparticles combined with ascorbic acid (CuO NPs/AA), which could act as a potent nanoantibiotic targeting both Escherichia coli (gram-negative) and Staphylococcus aureus (gram-positive) in vivo and in vitro, without noticeable cytotoxicity to mammalian tissue. The CuO NP/AA antibacterial system killed these microbes through a combined mechanism involving biofilm inhibition, cell membrane destruction, DNA damage, and enzyme inactivation. The antibacterial properties of CuO NPs/AA were mainly attributed to the generation of reactive oxygen species (e.g., ·OH) produced through the intrinsic ascorbate oxidase- and peroxidase-like activities of CuO NPs, highlighting their use as antibacterial nanozymes to treat bacterial infections.

The Weihe River Basin (WRB) in a monsoon transition zone of East Asia interacts with multiple weather systems and is susceptible to floods and droughts. We developed a framework based on the Empirical Orthogonal Function analysis, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise analysis, and moving-average based Spearman rank correlation to identify the spatial patterns of the dry/wet conditions from 1470 to 2016, decompose the dry/wet index into the leading components with a period between 2 and 3 years and 100+ years, and discover their driving forces on multiple time scales. Results show that WRB can be divided into four distinguishable hydroclimatic subregions located in the southeast, west, central, and north. The East Asian summer monsoon and South Asian summer monsoon impact this region mainly on the inter-annual and inter-decadal scales, while the impacts of ENSO, PDO, AO, and NAO are dominant on the multi-decadal and centennial scales.

This study aims to identify the fatigue lifetime of a thin-walled hollow spherical rubber isolator subjected to harmonic and random vibrations. Firstly, the nonlinear characteristics of the rubber materials are tested and analyzed through vibration experiments. The relationship among the nonlinear stiffness, nonlinear damping, and relative displacement amplitude is established. Secondly, an accelerated lifetime experiment under random excitation is carried out. The correlation between the root-mean-squared acceleration response of the random vibration system and the stiffness attenuation of the rubber isolator is established and modeled. A lifetime prediction method for rubber isolators is proposed based on the model. Finally, the influence of different loads on the lifetime of the rubber isolator is investigated. These methods provide an additional rationale for the further theoretical research and practical engineering application of rubber damping systems.

Autopilot systems on unmanned aerial vehicles (UAVs) are safety-critical systems whose requirements on reliability and safety are ever-increasing. However, testing a complex autopilot control system is an expensive and time-consuming task, which requires massive outdoor flight tests during the whole development stage. This paper presents an indoor automatic test platform for autopilot systems aiming to significantly improve the development efficiency and safety level of UAVs. First, a unified modeling framework is proposed for different types of aerial vehicles to make it convenient to share common modeling experience and failure modes. Then, a real-time simulation platform is developed by using automatic code generation and FPGA-based hardware-in-the-loop simulation methods to ensure simulation credibility on software and hardware levels. Finally, an automatic test framework is proposed to traverse test cases during real-time flight simulation and assess the test results. In the verification part, the accuracy and credibility of the simulation platform are verified by comparing the obtained results with experimental results, and several successful applications on multicopters demonstrate the practicability of the proposed platform.

In order to strengthen flow and heat transfer performance of the smooth rectangular microchannel (SRM), a novel of bionic rectangular microchannel (BRM) with a hydraulic diameter of 1000 um was designed based on the bionic wall roughness(BWR) of dragonfly wings structure. The effect of BWR and different microchannel lengths, and BWR with different tiny structures on flow and heat transfer was investigated, and the optimal of BWR's parameter, arrangement, combination and Re number range were studied. The results indicated that the influence of BWR on the flow and heat transfer performance is significantly better than that of SRM. When the Re is 5830, the heat transfer performance of the BRM model is 2.08 times that of SRM, and the heat enhancement factor is 1.233. Moreover, the optimal BRM's maximum velocity of the fluid is 19.6 m/s, which is 3.1 times of that of SRM, and the maximum temperature is 14.3 K lower than that of SRM, and an enhancement of 218% in heat transfer was achieved compared to a SRM, and the thermal enhancement factor is 1.34. Compared with BWR effect, the influence of the entry effect on heat transfer can be ignored, and the existence of BWR increases the number and scope of fluid disturbance and brings a large number and a wide range of high speed vortex flow appears in BRM, and they are regularly distributed after each group of BWR and the central of the microchannel, which further increases the flow velocity and disturbance, and reduces the range of low-speed and high-temperature region near the wall, and finally enhances flow and heat transfer performance.

Aerial refueling is capable of increasing the endurance and flight range of aircraft. However, during the docking phase of aerial refueling, the docking risk increases as the receiver aircraft approaches the tanker aircraft. A high docking risk indicates docking failure or even flight collision. In order to guarantee docking reliability and safety, a stochastic approximation method is proposed to predict the docking success probability during the docking phase of aerial refueling. If the success probability is lower than the specified value, the receiver aircraft needs to increase its relative distance from the tanker aircraft to reduce the risk. In our method, a stochastic differential equation is used to model the relative motion between the receiver and tanker aircrafts, where the receiver aircraft flies along a predefined flight path with influence of additive wind perturbations. The correlation between the magnitude of wind perturbations and the distance between the receiver and tanker aircrafts is considered. Then, based on the established relative motion model, the probability that the receiver aircraft enters the target set during a given time interval is predicted by the Markov chain stochastic approximation method. The docking success probability is computed by propagating the transition probabilities of the Markov chain backwards starting from the target set during the time interval. Comparing with the widely used Monte Carlo method in previous studies, our method demonstrates substantial effectiveness and efficiency.

This paper deals with the problem of pose estimation (or motion estimation) for multirotor unmanned aerial vehicles (UAVs) by using only an off-board camera. An extended Kalman filter (EKF) is often adopted to solve this problem. However, the accuracy and robustness of an EKF are limited partly by the usage of an existing linear constant-velocity process model applicable to many rigid objects. For such a reason, a nonlinear constant-velocity process model featured with the characteristics of multirotor UAVs is proposed in this paper, the superiority of which is explained from the perspective of observability. With the new process model and a generic camera model, a practical EKF method suitable for conventional cameras and fish-eye cameras is then proposed. By taking EKF implementation into account, a general correspondence method that could handle any number of feature points is further designed. Simulation and real experiments show that the proposed EKF method is more robust against noise and occlusion than currently employed filtering methods.

The high-density polyethylene (HDPE) composites filled with four kinds of carbon blacks (CB) were prepared to investigate the effects of the size and content of the CB on the electrical properties of the HDPE/CB composites. It was found that the composites presented conductive percolation behavior, the CB with different size filled-systems had different percolation thresholds; the higher the structure property of the CB, the larger the particle surface area, and the smaller the particle size, the better was the conductivity, and the lower was the percolation threshold of the composites. In addition, the HDPE/CB composites showed significant positive temperature coefficient (PTC) effect and negative temperature coefficient effect. The bigger the filler particle diameter, the higher was the PTC intensity of the HDPE composite systems under the same CB weight fraction.

For multicopters, failures may abort missions, crash multicopters, and moreover, injure or even kill people. In order to guarantee flight safety, a system of prognostics and health management should be designed to prevent or mitigate unsafe consequences of multicopter failures, where health evaluation is an indispensable module. This paper proposes a health evaluation method of multicopters based on Stochastic Hybrid System (SHS). In the SHS model, different working conditions (health statuses) of multicopters are modeled as discrete states, and system behaviors of different working conditions are modeled as continuous dynamics under discrete states. Then, the health of multicopters is quantitatively measured by a definition of health degree, which is a probability measure describing an extent of system degradation from an expected normal condition. On this basis, the problem of multicopter's health evaluation is transformed to a hybrid state estimation problem. In this case, a modified interacting-multiple-model algorithm is proposed to estimate the real-time distribution of hybrid state, and evaluate multicopter's health. Finally, a case study of multicopter with sensor anomalies is presented to validate the effectiveness of the proposed method.

Health performance prediction of a dynamical system aims at determining the probability or possibility that the system state will remain in a permitted area (safe set) or reach a forbidden area (unsafe set) at a future time instance. This paper proposes a health performance prediction algorithm for large-scale Stochastic Linear Hybrid Systems (SLHS) with small failure probability. In the studied SLHS, the continuous variable evolution is described by a set of stochastic linear differential equations, and the discrete state evolution is modeled by a first-order Markov chain. Furthermore, a safe set of the SLHS is described by a permitted area in the hybrid state space. Given an initial condition, a hybrid state evolution algorithm is proposed referring to the execution of stochastic hybrid systems. On this basis, a concept of health degree is introduced to evaluate the health performance of the studied SLHS. Finally, a multicopter with sensor anomalies is studied to validate the availability and effectiveness of the proposed method.

Two luminescent coordination polymers (LCPs), namely [CdL2]n (1) and [ZnL2]n (2) based on a new ligand 2-(2-methyl–benzimidazole-1-yl)-benzoic acid (HL), have been synthesized and characterized by single crystal X-ray diffraction, powder X-ray diffraction, infrared spectroscopy, elemental analysis, and thermogravimetric analysis. 1 and 2 exhibit similar 1D looped chains with different coordination modes of L. Both LCPs show strong fluorescent emission in the visible region. The fluorescence quenching response of 1 and 2 towards Fe3+ over other metal ions in DMF has been demonstrated by quenching titrations. Thus, 1 and 2 can be used as potential and highly selective luminescent sensors for Fe3+ ions.

Multicopters are attracting more and more attention these years. In the design stage, designers and users wonder if an assembled multicopter can meet their performance requirements, such as hovering endurance, system efficiency, maximum load, maximum pitch, and maximum flight distance. However, in practice, they used to evaluate the performance of a multicopter through lots of flight experiments or by experience, which are normally inefficient and costly. This motivates us to propose a comprehensive offline evaluation algorithm of multicopter performance. The performance indices considered are mainly determined by the propulsion system, including motors, propellers, electronic speed controllers, and batteries. Therefore, in the first stage of this research, models are established for the components of a propulsion system. In order to facilitate the application, only technical specifications of components offered by manufacturers are required as the input of the models. Based on the models and their relationships, equations describing performance indices are established and then solved to perform the evaluation. Finally, several examples are given to demonstrate the efficiency of the proposed evaluation method. As a result, a website ( www.flyeval.com ) is established, which can provide users with the performance evaluation mentioned in this paper.