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This paper studies the disturbance observer-based adaptive fuzzy finite-time control issue of strict-feedback nonlinear systems. Specifically, to meet practical application requirement, the finite-time prescribed performance is considered, which can guarantee the tracking error enters into the prescribed bounded set in a known time. A disturbance observer is proposed to estimate the external disturbance. It is proved that the closed-loop system is semi-globally practically finite-time stable. Finally, simulation studies for a one-link manipulator are shown to verify the effectiveness of the proposed approach.

This article investigates the command filtering-based event-triggered adaptive fuzzy control problem for a class of stochastic nonlinear systems with stochastic faults and input saturation. The unknown nonlinear functions and system dynamic changes that caused by stochastic faults are approximated by fuzzy logic systems (FLSs). In order to reduce the computational burden, the command filtering design technique is incorporated into the adaptive fuzzy event-triggered control strategy. Finally, the effectiveness of the proposed method is verified by simulation studies, in which the uniform ultimate boundedness of the system is guaranteed and all the signals in the closed-loop system are bounded.

This paper investigated the adaptive fuzzy finite-time control problem for a class of high-order stochastic nonlinear systems with a class of exponential type prescribed performance function. It is assumed that the nonlinear functions in the controlled plant are unknown, in which fuzzy logic systems (FLSs) are utilized due to the approximation ability of any unknown continuous functions with arbitrary approximation errors. Based on the FLSs and backstepping design technique, an novel adaptive fuzzy tracking control strategy is proposed to guarantee that the closed-loop nonlinear system is semi-globally finite-time stable in probability via Lyapunov stability theory and It ${\hat o}$ formula. Compared with existing results, the transformed error signal was regarded as a stochastic variable. In addition, the expressions of the first and second-order partial derivatives of the transformed error signals are given in this paper. Finally, a simulation example with different covariance values is given to show the effectiveness of the proposed control strategy.

This article proposes a novel modeling and path planning framework to tackle a new problem, named road-network persistent surveillance problem (RPSP), in which the occurrence location and probability of the events are unknown. To capture such events, an unmanned ground vehicle (UGV) with certain detection ability must move along the road-network with different monitoring priorities to persistently monitor them. First, we presented an event-oriented modeling method to formulate the new problem. The monitoring effect of the road-network is quantified by uncertainty, which takes the detection ability of the sensor, the detection interval, and the monitoring weight into account. Based on the proposed model, we then designed a heuristic path planning algorithm from a decision-making perspective, which enables a UGV to persistently monitor the viewpoints of the road-network without traversing them. The simulation results and analysis demonstrate the feasibility and superiority of the proposed approach. Note to Practitioners —This article was motivated by developing an effective solution for unmanned ground vehicle (UGV) to execute road-network persistent surveillance tasks under outdoor circumstances. To the best of our knowledge, there is no other consideration dealing with this problem. To capture spatiotemporal random events, we proposed a new approach that enables UGV to persistently monitor an area with road-network performing in the weighted mode. Distinguished from the traditional path planning methods in the literature, the proposed approach considered the detection ability of the UGV. Consequently, the so-called “area-to-area” planning approach does not require traversing all the viewpoints. The approach can be used in road-network with any topology. Moreover, the difficulty in modeling road-network from the actual environment is degraded, and compared with the existing patrolling methods, the monitoring efficiency of the proposed approach is improved obviously.

Dielectric ceramics capacitors (DCC) with excellent energy storage performance (ESP) and charge-discharge performance (CDP) is very critical in the field of advanced electronics and power systems. A strategy that improves the ESP of 0.6Ba(Zr0.2Ti0.8)O3-0.4(Na0.5Bi0.5)TiO3 (BZT-NBT) ceramics was proposed via Sr2+ doping. XRD and SEM results confirmed that 0.6(Ba1-xSrx)(Zr0.2Ti0.8)O3-0.4(Na0.5Bi0.5)TiO3 (x = 0, 0.1, 0.2, 0.3, 0.4) (BSZT-NBT) ceramics formed dense and stable perovskite solid solutions. The relaxor ferroelectric (RFE) properties of BSZT-NBT ceramics were also well proved by dielectric behaviors. A large recoverable energy storage density (Wrec) and large efficiency (η) of 3.72 J cm−3 and 94.03 % (x = 0.3) can be simultaneously obtained at 289 kV cm-1. ESP of BSZT-NBT (x = 0.3) ceramics at 180 kV cm-1 exhibit good frequency (1−100 Hz) and temperature (room temperature (RT)-120 °C) stability. BSZT-NBT (x = 0.3) ceramics at 120 kV cm−1 exhibit a prominent power density (PD) and rapid discharge rate (t0.9) of of 37.62 MW cm−3 and 70.6 ns. All evidences confirm that introduction of Sr2+ into A-site of barium titanate-based ceramics could effectively improve ESP.

Real-time monitoring of the vortex flow field has vital significance for industrial processes, including metallurgy refining, rocket fuel system, and hydropower station operation. It can improve the yield rate and resource utilization and ensure high-efficiency sustainability production. However, the formation mechanism of a multiphase vortex, involving the dynamic tracking of multi-layer interface, critical multiphase coupling, and fluid-solid vibration wave transition, is still unclear. To address these matters, a numerical modeling-solving method for a multiphase vortex is proposed. With the coupled level-set and volume-of-fluid (CLSVOF) method, a fluid mechanic model is built up to acquire the interface evolution laws and multiphase coupling mechanism. A fluid-solid vibration dynamic model is conducted, and a displacement solution method with Flugge equations is presented to reveal vibration wave transition mechanism. A mesh updating method with spring smoothing and local reconstruction is presented to optimize the numerical process. Numerical results demonstrate that a critical penetrating state contains multiple solutions with the initial disturbance; the coupling energy shock induces a pressure oscillation phenomenon and appears intensive vibration with higher amplitudes and wider frequency bands. At the critical penetrating stage, the vibration signal takes on a transient distortion character, and its nonlinear shock components concentrate in the frequency range of 45–50 Hz.

Direct production of biodiesel by both esterification and transesterification of renewable oils with acid value (AV) of 9.6-28.9 mg KOH/g using calcined blast furnace dust is studied. Blast furnace dust calcined at 600-700 degrees C obviously promotes efficient biodiesel production because of the crystal form transformation from lead sulfate particles in dust into active lead oxide, lead sulfide, and lead particles. It is found for the first time that lead oxide, lead sulfide and lead particles can resist saponification from fatty acids to catalyze raw renewable oils to biodiesel with catalytic activity order as follows: lead oxide > lead > lead sulfide > lead sulfate. Biodiesel production process is optimized according to an orthogonal design with biodiesel yield of 92 wt% obtained at AV of 9.6 mg KOH/g (82 wt% after 5 cycles). Biodiesel yield of 84 wt% is achieved at AV of 28.9 mg KOH/g with acidity of 0.18 mmol/g. Calcined blast furnace dust presents potential applications in the production of biodiesel from renewable oils with high AVs. (C) 2021 Elsevier Ltd. All rights reserved.

Flexible power sources featuring high-performance, prominent flexibility and raised safety have received mounting attention in the area of wearable electronic devices. However, many great challenges remain to be overcome, notably the design and fabrication of flexible electrodes with excellent electrochemical performance and matching them with safe and reliable electrolytes. Herein, a facile approach for preparing flexible electrodes, which employs carbon cloth derived from commercial cotton cloth as the substrate of cathode and a flexible anode, is proposed and investigated. The promising cathode (NVPOF@FCC) with high conductivity and outstanding flexibility is prepared by efficiently coating Na3V2(PO4)2O2F (NVPOF) on flexible carbon cloth (FCC), which exhibits remarkable electrochemical performance and the significantly improved reaction kinetics. More importantly, a novel flexible quasi-solid-state sodium-ion full battery (QSFB) is feasibly assembled by sandwiching a P(VDF-HFP)-NaClO4 gel-polymer electrolyte film between the advanced NVPOF@FCC cathode and FCC anode. And the QSFBs are further evaluated in flexible pouch cells, which not only demonstrates excellent energy-storage performance in aspect of great cycling stability and high-rate capability, but also impressive flexibility and safety. This work offers a feasible and effective strategy for the design of flexible electrodes, paving the way for the progression of practical and sustainable flexible batteries.

Remote sensing technology is an effective tool for sensing the earth’s surface. With the continuous improvement of remote sensing technology, remote sensing detectors can obtain more spectral and spatial information, including clear feature contours, complex texture features and spatial layout rules. This information was detected in mineral resources. Surface substance identification, water pollution information monitoring and many other aspects have played an important role. The coding algorithm and defects, storage algorithm and interference from atmospheric cloud radiation information during the imaging process lead to varying degrees of distortion and deterioration of remote sensing images during imaging, transmission and storage. This makes it difficult to process, analyze and apply remote sensing images. Therefore, the design of a reasonable remote sensing image quality evaluation method is not only conducive to the remote sensing image quality evaluation in the real-time processing system of remote sensing image, but also conducive to the optimization of remote sensing image system and image processing algorithm. The application is worthwhile. In this paper, the deteriorating features of remote sensing images will change the statistical distribution. We propose a method for evaluating the quality of remote sensing images in depth learning. Feature learning and blurring as well as noise intensity classification for image remote sensing using convolutional neural network are carried out. The evaluation model is modified by masking effect and perceptual weighting factor, and the quality evaluation results of remote sensing images are obtained according to human vision. The research shows that this method can effectively solve the problem of removing and evaluating the noise of remote sensing image, and can effectively and accurately evaluate the quality of remote sensing image. It is also consistent with subjective assessment and human perception.

High-temperature tribology, which is often involved during hot metal forming, is controlled via oxidation on a rubbing surface. However, for high chromium stainless steel (ST), where oxidation is strongly inhibited, the effect of counterface materials on tribological behavior is yet to be elucidated. In this study, the effects of counterfaces on the tribological behavior of 253MA ST and mild steel (MS) are investigated via a ball-on-disc test at 900 °C using a 20 N load. The results reveal that high-speed steel (HSS) experiences severe abrasive wear with MS and causes severe sticking problems with ST. Si3N4 and SiC present substantially stronger abrasive wear resistance than HSS with MS, and the friction coefficients are dependent on the type of ceramic. Both ceramics can facilitate the establishment of a thick tribo-oxide layer (> 3 µm) on ST to prevent sticking; however, this is accompanied by severe pull-out and fracture wear. The effects of the counterface on the mechanical properties of the tribo-oxide layer, near-surface transformation, and the responses of the tribo-oxide layer to friction and wear are discussed. This study contributes to the understanding of interfacial tribological behaviors when different types of tools are used on MS and ST.

A generic framework is proposed for fault detection and recovery (FDR) in automated driving systems (ADSs) with inertial sensors, which is based on multitask one-dimensional convolutional neural networks (MONN). FDR plays an important role for safe and robust vehicle controls in driver-assistance and ADSs. Although model-based fault detection and recovery methods are well established in the literature, they need accurate model description and are often designed under ideal conditions. Data-driven fault diagnosis, however, might overlook apparent physical relations among sensors. In order to take advantage of both approaches, dynamic relations among measured physical quantities and temporal finite differences are introduced in a MONN as additional features. The proposed algorithm achieved more reliable performance and outperformed considered state-of-the-art methods under various testing conditions. Extensive experiments were conducted to empirically show the effectiveness of the proposed generic FDR framework.

This paper investigates a novel strategy which can address the fault-tolerant control (FTC) problem for nonlinear strict-feedback systems containing actuator saturation, unknown external disturbances, and faults related to actuators and components. In such method, the unknown dynamics including faults and disturbances are approximated by resorting to Neural-Networks (NNs) technique. Meanwhile, a back-stepping technique is employed to build a fault-tolerant controller. It should be stressed that the main advantage of this strategy is that the NN weights are updated online based on gradient descent (GD) algorithm by minimizing the cost function with respect to NNs approximation error rather than regarding weights as adaptive parameters, which are designed according to Lyapunov theory. In addition, the convergence proof of NN weights and the stability proof of the proposed FTC method are given. Finally, simulation is performed to demonstrate the effectiveness of the proposed strategy in dealing with unknown external disturbances, actuator saturation and the faults related to the components and actuators, simultaneously.

Cross-linked polymer microspheres have potential applications in many fields. Compared with seed polymerization, two-part dispersion polymerization is a very attractive method for preparing micron-sized cross-linked microspheres. However, the current dispersion polymerization would be adversely affected by the addition of more than 1 wt% of cross-linker at a time and even agglutination occurred. In this study, a facile method for the preparation of highly cross-linked microspheres by two-step dispersion polymerization of a chain transfer agent has been developed. The relationship between cross-linker and transfer has been studied in detail, and microspheres can be simultaneously maintained by adding the cross-linker ethylene glycol dimethacrylate (EGDMA) at concentrations of up to 10%.

In this study, a simple technology for a self-driving system called “driver assistance system” is developed based on embedded image identification. The system consists of a camera, a Raspberry Pi board, and OpenCV. The camera is used to capture lane images, and the image noise is overcome through color space conversion, grayscale, Otsu thresholding, binarization, erosion, and dilation. Subsequently, two horizontal lines parallel to the X-axis with a fixed range and interval are used to detect left and right lane lines. The intersection points between the left and right lane lines and the two horizontal lines can be obtained, and can be used to calculate the slopes of the left and right lanes. Finally, the slope change of the left and right lanes and the offset of the lane intersection are determined to detect the deviation. When the angle of lanes changes drastically, the driver receives a deviation warning. The results of this study suggest that the proposed algorithm is 1.96 times faster than the conventional algorithm.

In order to reduce the dimension of wind farm model and improve the simulation efficiency under the premise of ensuring the accuracy, this article proposes a dynamic equivalent method for permanent magnet synchronous generator (PMSG) based wind farm cluster utilizing the adaptive extended Kalman filters. Combining the dynamic model of PMSG, the equivalent wind turbine parameters that need to be identified are incorporated into the extended state variables, and an online parameters identification method based on extended Kalman filter (EKF) theory is put forward. To eliminate the influence of random noise on the accuracy of identification, the adaptive technology is introduced into EKF to estimate the process and measurement noise covariance in real time through the Sage–Husa estimator. Thus, a wind farm cluster equivalent model is obtained. Finally, the effectiveness and robustness of the proposed method are verified by time-domain simulation and frequency-domain simulation in IEEE 39-bus system.

Vehicle steer-by-wire system (SBW) can improve the handling stability, passive safety, active safety and drive comfort significantly, which becomes a hot topic in research. However, the safety problem of SBW is one of the most critical problem in its practical application. In order to solve the safety reliability problem of SBW, a dual-redundancy steer-by-wire system (DSBW) is proposed in this paper by applying the redundancy theory, in which no system failure is achieved when any single random electrical fault occurs, showing that DSBW is a system-level dual-redundancy system rather than a system with local redundancy. Finally, a DSBW prototype is manufactured to prove its high safety reliability. Fault injection experiments of the system are performed, in which DSBW works normally when any single electrical fault occurs.

Due to the advantages of low porosity, low permeability, high ductility, and excellent capacities for creep and damage self-healing, salt rock is internationally considered as the ideal medium for underground storage of energy and disposal of radioactive waste. As one of the most important mechanical properties of salt rock, creep properties are closely related to the long-term operation stability and safety of salt rock underground storage cavern. A comprehensive review on the creep properties and constitutive model of salt rock is put forward in this paper. The opinions and suggestions on the research priority and direction of salt rock's mechanical properties in the future are put forward: (1) permeability variation of salt rock under the coupling effect of temperature and stress; (2) damage mechanism and evolution process under the effect of creep-fatigue interaction and low frequency cyclic loading; (3) microdeformation mechanisms of salt rock and the relationship between microstructure variations and macrocreep behavior during creep process; (4) the establishment of the creep damage constitutive model with simple form, less parameters, easy application, and considering the damage self-healing ability of salt rock simultaneously.

To explore the response characteristics of soil under dynamic loads, cyclic impact tests with varying axial compressions and impact velocities were conducted using SHPB tests. The test results were analyzed to study the mechanical properties and energy dissipation characteristics due to impact loads. In terms of mechanical properties, for the same impact times, the peak stress of the specimen with axial compression was found to be greater than that without axial compression, but the final strain was lower than that without axial compression, indicating that axial compression affects the peak stress and final strain of the specimen. The dynamic stress strengthening of the specimen under cyclic impact loads has also been analyzed. In terms of energy dissipation, the effects of axial compression, impact velocity, and impact times on the ratios of dissipated energy, reflected energy, and transmitted energy to incident energy were compared, and it was found that the ratio of the dissipated energy did not exceed 24% under any condition. In terms of damage evolution, the damage variable gradually decreases with an increase in impact times. A critical value of the damage variable was determined, above which the transmittance continued to decrease, the reflectivity gradually increased, and the dissipation rate remained stable.

The 2024 aluminum alloy was prepared with different ultrasonic processes. Effects of ultrasonic treatment parameters including ultrasonic power, treatment time, treatment temperature, and frequency resonance, as well as C2Cl6 degasser on degassing of the 2024 aluminum alloy were investigated. Results indicate that increasing ultrasonic power at the same ultrasonic treatment time can improve the degassing effect. The optimum degassing efficiency can be obtained under the resonant ultrasound condition. With the combination of 1% C2Cl6 addition and 150 W ultrasonic treatment for 40 s, the hydrogen content of the alloy is decreased by 52.9%. At the same time, the tensile strength and elongation are increased by 28.3% and 92.3%, respectively, and the yield strength is slightly increased by 6.7%. The degassing mechanism is also discussed.

We successfully synthesized nanoflower-like δ-MnO2, rod-shaped MnOOH, nano-small granular Mn3O4, and the cuboidal and spindle shaped MnCO3 via a facile one-pot hydrothermal method by adjusting the concentration of two sorts of short-chain surfactants with special catechol structures (3-Hydroxytyramine hydrochloride (DA) and 3,4-dihydroxybenzenepropanoic acid (DHCA)) in KMnO4 solution. The XRD and FTIR data demonstrated that the crystal form, morphology and size of the crystal were controlled by small molecule ligands. Afterwards, the relationship between manganese oxides and electrochemical properties was further explored. The results illustrated that nanoflower-like δ-MnO2 with layer structures have the highest specific capacitance of 307.4 F g−1 at a scan rate of 5 mV s−1.

In this article, we focus on a wireless-powered body area network in which the simultaneous wireless information and power transfer (SWIPT) technique is adopted. We consider two scenarios based on whether sensor nodes (SNs) are equipped with battery. For the first time, energy consumption minimization with throughput heterogeneity (ECM-TH) problem is addressed for both scenarios. For the battery-free scenario, a low-complexity time allocation scheme is proposed. This scheme solves the ECM-TH problem based on a hybrid method of gradient descent and bisection search algorithms. Consequently, compared with the interior-point method, our scheme has a lower computational complexity for the same energy consumption performance of the network. For the battery-assisted scenario, the nonconvex ECM-TH problem is first transformed into a convex optimization problem by introducing auxiliary variables. Then, a joint time and power allocation scheme based on the Lagrange dual subgradient method is proposed to solve it. Compared with the battery-free scenario, energy consumption and outage probability are both decreased in the battery-assisted scenario. Moreover, we address a special case wherein the feasible set of the above-mentioned ECM-TH problems may be empty owing to poor channel conditions or high throughput requirements of SNs.

Factors like intensive industrial and socio-economic development can lead to the deterioration of the water environment. At the same time, economic, social, and population sustainability rely on good water conditions in the river. Therefore, it is important to calculate the water environment capacity and allocate it accordingly. Our research selects the economic, social, and environmental factors to construct a water environment capacity allocation model using the analytic network process. Allocation results are as follows: there are Liaozhong County, Tiexi District, and Liaoyang County, where the water environment capacity allocation value exceeds 10 000 t/a at 18 336.25, 12 743.19, and 10 585.53 t/a, respectively. Dashiqiao City’s water environmental capacity allocation is the least, at only 78.30 t/a. In the paper, the goal to allocate water environment capacity from the three major rivers to the administrative area is achieved in Liaoning Province, thus making the allocation result more comprehensive and fairer. It provides a scientific basis for controlling the water environment in Liaoning Province.

In recent years, deep convolution neural network has been widely used in image super-resolution and achieved great performance. As the network becomes deeper and deeper, the accuracy of reconstruction is higher and higher. However, it also brings a large increase in the number of parameters and computational complexity, which makes the practical application more and more difficult. In this paper, we propose an efficient image super-resolution method based on fusion diversion network (FDN), where diversion and fusion block serves as the basic build module. By using the fusion and diversion mechanism, the information can be fully interactive and transferred in the network, and the expression ability of the model can be effectively improved. Extensive experimental results show that even with much fewer layers, the proposed FDN achieves the competitive results in both accuracy and speed.

The atmospheric CO2 concentration continues a rapid increase to its current record high value of 416 ppm for the time being. It calls for advanced CO2 capture technologies. One of the attractive technologies is physical adsorption-based separation, which shows easy regeneration and high cycle stability, and thus reduced energy penalties and cost. The extensive research on this topic is evidenced by the growing body of scientific and technical literature. The progress spans from the innovation of novel porous adsorbents to practical separation practices. Major CO2 capture materials include the most widely used industrially relevant porous carbons, zeolites, activated alumina, mesoporous silica, and the newly emerging metal-organic frameworks (MOFs) and covalent-organic framework (COFs). The key intrinsic properties such as pore structure, surface chemistry, preferable adsorption sites, and other structural features that would affect CO2 capture capacity, selectivity, and recyclability are first discussed. The industrial relevant variables such as particle size of adsorbents, the mechanical strength, adsorption heat management, and other technological advances are equally important, even more crucial when scaling up from bench and pilot-scale to demonstration and commercial scale. Therefore, we aim to bring a full picture of the adsorption-based CO2 separation technologies, from adsorbent design, intrinsic property evaluation to performance assessment not only under ideal equilibrium conditions but also in realistic pressure swing adsorption processes.

This paper concerns with the stability analysis for a class of discrete-time switched Takagi–Sugeno (T–S) fuzzy systems. By establishing a semitime-dependent Lyapunov function and exploring the property of mode-dependent average dwell-time switching, improved stability criteria are provided for switched T–S fuzzy systems containing both stable and unstable modes. Then, slow and fast switching strategies are designed, which are adopted for stable and unstable subsystems, respectively. Especially, we also provide the stability conditions for switched systems with all modes stable and unstable. Finally, the validities and advantages of provided techniques are illustrated by some simulation examples.

This article investigates the adaptive fuzzy fault-tolerant control problem for a class of strict-feedback stochastic nonlinear systems with quantized input signal. A hysteretic quantizer is utilized to avoid chattering caused by quantized input signals. The fuzzy-logic systems are utilized to approximate the unknown nonlinear functions and also to construct the fuzzy state observer, which is used to estimate the immeasurable state vector. The actuator faults considered in this article are loss of effectiveness and lock-in-place faults. By using the Lyapunov stability theory, the closed-loop stochastic nonlinear system is guaranteed to be stable in probability, and all the signals of the closed-loop system are bounded in probability in the presence of quantized input and actuator faults. Finally, a simulation example is given to verify the validity of the proposed control strategy.

Electrically responsive electrochemical actuators that contain a polymer electrolyte membrane laminated between two electrodes have attracted great attention due to their potential applications in smart electronics, wearable devices, and soft robotics. However, some challenges such as the achievement of large bending strain under low applied voltage and fast ion diffusion and accumulation still exist to be resolved. The key to the solution lies in the choice of electrode materials and the design of electrode structures. In this study, an engineering electrochemical actuator that presents large bending strain under low applied voltage based on MXene/polystyrene-MXene hybrid electrodes is developed. The developed electrochemical actuator based on the MXene/polystyrene-MXene 3D-structure is found to exhibit large bending strain (ca. 1.18%), broad frequency bandwidth, good durability (90% retention after 10,000 cycles) and considerable Young’s modulus (ca. 246 MPa). The high-performance actuation mainly stems from the excellent properties of MXene and 3D-structure of the electrode. The MXene provides excellent mechanical strength and high electrical conductivity which facilitate strong interaction and rapid electron transfer in electrodes. The 3D architectures formed by polystyrene microspheres generate unimpeded ion pathways for ionic short diffusion and fast injection. This study reveals that the 3D-structure hybrid electrodes play a crucial role in promoting the performance of such electrochemical actuators.

At present, the energy consuming during the electrolytic copper foil preparation accounts for more than 75% of the total energy consumption. In real-life production, the process parameters are set by the operator empirically and the system may not work at the operating point with minimum energy consumption. Therefore, it is critical to establish an effective model for predicting electrolysis energy consumption to guide the parameters design. In this paper, a novel hybrid model (named PSVM-PMLP-MLR) based on stacked ensemble learning is proposed. The model is divided into two parts: the base-learning model and the meta-learning model. The support vector machine (SVM) model and multilayer perceptron (MLP) model with different input structures are established by the former first. Then the particle swarm algorithm is employed to determine the optimal value of SVM parameters and the optimal weight of MLP by minimizing the mean absolute percentage error (MAPE). The multiple linear regression (MLR) is finally employed as a meta-learning machine to compute the final predictions. Experimental results show that the regression coefficient of this model reached 0.987, and compared with the traditional SVM and MLP models, the accuracy of the model is improved by 10.29% and 8.28%, respectively.

The commutation failure of Line Commutated Converter-High Voltage Direct Current (LCC-HVDC) cannot be avoided. This paper presents a novel transient stability assessment approach for AC/DC hybrid system considering continuous commutation failure. First, the conception and mathematical background of Maximum Lyapunov Exponent (MLE) of switching system is introduced, and the switching compensation matrix is derived. Next, the switching system model of AC/DC hybrid system considering continuous commutation failure caused by AC faults is established. On the basis of switching compensation, the MLE of switching system is calculated accurately, and the MLE based criteria are proposed to assess the transient stability of the switching system. Finally, the modified 39-bus system with LCC-HVDC added and the real power system are used as test systems to investigate the validity and effectiveness of the proposed transient stability assessment approach.

Organic photovoltaic cells (OPVs) have great potential for driving indoor electronic devices for internet of things (IoTs). Under indoor illumination, the thick active layer can produce high photocurrent due to the large light-harvesting, whereas the trap states usually dominate the charge recombination in such thick-film devices and limit the largest possible photocurrent. Herein, we adopted a strategy of insulating polymer dilution to suppress the charge trapping states in the indoor organic photovoltaic (IOPV) devices with the different active layer thickness from 140 to 1000 nm. A diluting strategy with 5% insulating polystyrene effectively reduces the trap state density, delivering an excellent fill factor of around 67% and growth of short-circuit current density with increasing the thickness to 1-micron under 2700 K light-emitting diode tube. For the diluted ultra-thick blend, the reduced trap states suppress the exciton quenching at traps, accelerate the dissociation of intra-moiety polaron pairs and improve the transport properties, enabling more photogenerated carriers and low photocurrent loss in ultra-thick devices. This work provides an effective diluting strategy of decreasing trap state density for realizing ultra-thick IOPV devices with high performance and demonstrates the key role of charge trapping in IOPV performance.

Solar thermochemical CO2-to-fuel conversion via two-step redox cycles is a promising strategy to solve climate change and energy storage challenges simultaneously. However, ideal thermochemical materials possessing moderate operation temperature, high yield, good stability, and rapid kinetics operating under isothermal and near-isothermal conditions are still missing. Here, we propose Sr-doped SmMnO3 for moderate-temperature near-isothermal CO2-to-fuel conversions. A record-high isothermal cycle CO yield (376.1 μmol g−1) at no more than 1300 °C is reported based on Sm0.6Sr0.4MnO3. No obvious decay is observed during 14 cycles although grain sizes have increased to some extent. The weakened Mn–O bond induced by Sr doping helps to create more oxygen vacancies, and thus contributes to enhanced yield of both O2 and CO. The oxidation reaction is fast with a CO yield of 58.6 μmol g−1 in 15 minutes, which is 5.6 times as high as that of CeO2 under the same cycling conditions between 1300 °C and 1000 °C. Detailed kinetics for the oxidation step is unveiled based on the Sestak–Berggren model. In addition, SmxSr1−xMnO3 can capture full-spectrum solar energy with a solar absorptance of more than 86.6% in stark contrast to 13.5% for CeO2, making the required concentration ratio to drive reactions decrease by 33.4%. This work provides new materials for high yield and stable thermochemical CO2-to-fuel conversion under moderate-temperature near-isothermal conditions, and paves the way for the development of CO2 splitting techniques directly driven by low-concentration solar energy with high efficiencies.

Three-dimensional polyaniline (PANI) hydrogel was used as the conductive medium to improve the methane (CH4) production from the anaerobic degradation of organics in wastewater. The porous structure and hydrophilic surface of the PANI hydrogel promoted the adhesion of the anaerobes. The PANI hydrogel existed as a conductive emeraldine base (EB) form with a conductivity of 0.42 S/cm, and had a good biocompatibility with the microorganisms in the anaerobic system. The conductive PANI hydrogel was added into the anaerobic sludge as the conductive medium of the direct interspecies electron transfer (DIET) between bacteria and archaea, accelerating CH4 production during the biodegradation of organic pollutants. The results indicated that the CH4 production rate was increased by 10.50%, 14.21%, 28.77% and 19.30% from the anaerobic system with adding 1000, 2000, 3000 and 4000 mg/L of PANI hydrogel. The proportion of Methanosaeta in the anaerobic sludge with the ability of DIET was increased to 64.74% after adding the PANI hydrogel. The conductive PANI hydrogel served as an electronic channel to enrich the microorganism with the DIET ability, which was responsible for PANI hydrogel improved CH4 production.

A hybrid scheme that combines the pre-corrected fast Fourier transform (p-FFT) and iterative Kirchhoff approximation (IKA) is developed to solve the electromagnetic scattering from multiple complex targets above a three-dimensional (3D) rough surface. Based on the domain distribution method (DDM), multiple targets are divided into an FFT region, and the rough surface is divided into an IKA region. Scattering on the FFT region and IKA region is calculated by p-FFT and IKA, respectively. In addition, iterative strategies are considered in both solving interaction between facets in the IKA region and interactions between FFT region and IKA region. However, interactions between FFT region and IKA region are the most time-consuming in the entire computational process. To overcome this computational bottleneck, the combined multilevel fast multipole algorithm (MLFMA), ray-propagation fast multipole algorithm (RPFMA) and fast far-field approximation (FAFFA) are incorporated into interactions between FFT region and IKA region. They accelerate the matrix vector multiplication by “interpolation –translation –anterpolation”, while their translators are different, which result in their different computation efficiencies and application conditions. To effectively combine these three algorithms, the IKA region is divided into MLFMA area, RPFMA area and FAFFA area according to the distance, where interactions between these areas and the targets are accelerated by MLFMA, RPFMA and FAFFA, and the surface outside these areas is discarded. Compared with MLFMA, the accuracy and efficiency of this hybrid method are verified.

The donor:acceptor (D:A) blend ratio plays a very important role in affecting the progress of charge transfer and energy transfer in bulk heterojunction (BHJ) organic solar cells (OSCs). The proper D:A blend ratio can provide maximized D/A interfacial area for exciton dissociation and appropriate domain size of the exciton diffusion length, which is beneficial to obtain high-performance OSCs. Here, we comprehensively investigated the relationship between various D:A blend ratios and the charge transfer and energy transfer mechanisms in OSCs based on PBDB-T and non-fullerene acceptor IT-M. Based on various D:A blend ratios, it was found that the ratio of components is a key factor to suppress the formation of triplet states and recombination energy losses. Rational D:A blend ratios can provide appropriate donor/accepter surface for charge transfer which has been powerfully verified by various detailed experimental results from the time-resolved fluorescence measurement and transient absorption (TA) spectroscopy. Optimized coherence length and crystallinity are verified by grazing incident wide-angle X-ray scattering (GIWAXS) measurements. The results are beneficial to comprehend the effects of various D:A blend ratios on charge transfer and energy transfer dynamics and provides constructive suggestions for rationally designing new materials and feedback for photovoltaic performance optimization in non-fullerene OSCs

Twin-roll casting (TRC) of Mg alloys with high Al content has unique advantages and broad development prospects. However, the severe segregation of high-alloyed Mg caused by TRC technology is difficult to solve at present, which restricts its practical application. In this work, the homogenization of coarse segregated phases in a twin-roll cast Mg–8Al–2Sn–1Zn (ATZ821) alloy was successfully achieved by a method combining solid-solution heat treatment and controlled rolling, which is suitable for industrial-scale production. In addition, the evolution of the microstructure was studied in detail. It was found that phases dynamically precipitated and dissolved repeatedly during the hot rolling process, and the evolution of entire second phases can be classified into three stages, i.e., the net dissolution, balancing and net precipitation stages. During the process, the large-size chain-like eutectic phases evolved into a fine and nearly spherical (∼0.4 µm) morphology that was uniformly dispersed in the Mg-matrix due to the thermodynamic coupling and the Ostwald ripening effect. Moreover, a fine grain microstructure (∼4 µm) was obtained. The formidable segregation problem of twin-roll cast Mg alloys with high Al content was solved, which is an important finding for the industrial application of high-alloyed twin-roll cast sheets.

Dialyzed tilapia skin collagen sponge (DTSCS) and self-assembled tilapia skin collagen sponge (STSCS) were prepared by freeze-drying. The raw components used in the fabrication of DTSCS and STSCS were separated and purified from tilapia fish skin. It is anticipated that these collagen sponges could be developed into medical dressings for hemostasis and wound healing. The aim of the present research was to explore the possibility of DTSCS and STSCS as medical dressings and compare their differences by scanning electron microscopy (SEM), water absorption measurement, differential scanning calorimetry (DSC), measurement of porosity, cytotoxicity, hemolysis, in vivo biocompatibility, and evaluation of hemostatic performance and wound healing. The results indicate that DTSCS and STSCS are suitable materials for use in medical applications with a loose and porous structure, high water absorption, high porosity, and high thermal stability. The materials also displayed good biocompatibility, including excellent blood compatibility, a lack of cytotoxicity, with no apparent rejection following implantation. STSCS exhibited rapid hemostasis and promoted healing, with slightly greater efficacy than DTSCS. The hemostatic properties and promotion of healing in DTSCS was similar to that of commercial bovine collagen sponge. Therefore, DTSCS and STSCS both represented excellent potential candidate materials for use as hemostatic agents and wound dressings.

Automation and intelligent health monitoring technologies will be one of the most important development directions for rail transit operation and maintenance considering the continuous expansion of...

The development of novel hemostatic agents with distinct modes of action from traditional ones remains a formidable challenge. Self-assembling peptide hydrogels have emerged as a new hemostatic material, not only because of their inherent biocompatibility and biodegradability but also their designability. Especially, rational molecular design can make peptides and their hydrogelation responsive to biological cues. In this study, we demonstrated that transglutaminase-catalyzed reactions not only occurred among designed short peptide I3QGK molecules but also between the peptide and a natural polysaccharide O-carboxymethyl chitosan. Because Factor XIII in the blood can rapidly convert into activated transglutaminase (Factor XIIIa) upon bleeding, these enzymatic reactions, together with the electrostatic attraction between the two hemostatic agents, induced a strong synergetic effect in promoting hydrogelation, blood coagulation, and platelet adhesion, eventually leading to rapid hemostasis. The study presents a promising strategy for developing alternative hemostatic materials and methods.

This article investigates the adaptive fuzzy tracking control problem for a class of strict-feedback nonlinear systems with time-varying input delay and full state constraints. By using state vector transformation and barrier Lyapunov function techniques, the effect of time-varying input delay and full state constraints are compensated. The unknown nonlinear functions are approximated by utilizing fuzzy logic systems, and then a novel adaptive fuzzy backstepping control strategy is proposed to guarantee that the closed-loop nonlinear system is semiglobally ultimately uniformly bounded. Finally, two simulation examples of an inverted pendulum system and three-degrees-of-freedom helicopter nonlinear system are studied to verify the effectiveness of the proposed control strategy.

In automotive field, the term Internet of Vehicles (IoV) is a sub-application of Internet of Things (IoT).The communication scenario of IoV usually changes in the space-time dimension. Unfortunately, vehicles can not select the optimal routing policy when facing the dynamic environment. Thus, in this paper, we present a V2X Communication protocol based on Nakagami-m Outage Probability (VCNOP) to improve Packet Delivery Ratio (PDR) and Average-End-to-End-Delay (AE2ED). We consider Road-Side-Units (RSU)-assisted communication system to help find the best routing path, and the outage probability to measure the impact of the physical layer on routing protocol. Meanwhile, the dynamic broadcasting mechanism considering the various vehicle velocity and density are applied to increase the accuracy of routing decisions. We utilize vehicle flow model combining realistic Harbin map by SUMO to provision a realistic scenario. Following this, simulation results in NS3 show the advantage of VCNOP compared with other protocols in terms of PDR and AE2ED.

Via a traditional sintering process, (1-x) [0.85BaTiO3-0.15Bi(Mg2/3Nb1/3)O3] - x Na0.5Bi0.5TiO3 (x = 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1) (BT-BMN-NBT) ceramics were synthesized. The sintering temperature and density of the BT-BMN-NBT ceramics decreased with the increasing NBT content. All samples exhibited a pseudocubic phase and formed stable solid solutions with dense microstructures. The crystal structure of the BT-BMN-NBT ceramics gradually transformed from BT to NBT with increasing NBT content. The introduction of NBT with a high maximum polarization (Pmax) could not only enhance the relaxor ferroelectric (RFE) characteristics of the BT-based RFE, but also compensate for the reduction in Pmax of BT-based RFE that is due to the enhanced RFE characteristics. The temperature that corresponds to the maximum dielectric constant increases monotonously with the NBT content. Pmax of the BT-BMN-NBT ceramics increases significantly as the NBT content increases. The best energy storage (ES) performances (x = 0.3) of a releasable energy density (Wrec) of 2.91 J/cm3 and 85.55% efficiency were realized at 200 kV/cm. Compared with the unmodified BT-BMN ceramics (1.67 J/cm3), Wrec was improved by 1.48 times, which originated from the NBT modification induced increase in Pmax (39.5 μC/cm2) to 2.34 times the nominal BT-BMN value (16.9 μC/cm2). The results prove that the addition of NBT could improve the ES performances of BT-based relaxor ferroelectrics at low-field by increasing the polarization.

The charge transfer is a key issue in the development of efficient photoelectrodes. Here, we report a method using F-doping and dual-layer ultrathin amorphous FeOOH/CoOOH cocatalysts coupling to enable the inactive α-Fe2O3 photoanode to become highly vibrant for the oxygen evolution reaction (OER). Fluorine doping is revealed to increase the charge density and improve the conductivity of α-Fe2O3 for rapid charge transfer. Furthermore, ultrathin FeOOH was deposited on F-Fe2O3 to extract photogenerated holes and passivate the surface states for accelerated charge carrier transfer. Moreover, CoOOH as an excellent cocatalyst was coated onto FeOOH/F-Fe2O3 with the photoassisted electrodeposition method remarkably expediting OER kinetics through an optional pathway of holes utilized by Co species. Ultimately, the CoOOH/FeOOH/F-Fe2O3 photoanode exhibits a satisfactory photocurrent density (3.3-fold higher than pristine α-Fe2O3) and a negatively shifted onset potential of 80 mV. This work showcases an appealing maneuver to activate the water oxidation performance of the α-Fe2O3 photoanode by an integration strategy of heteroatom doping and cocatalyst coupling. © 2020 American Chemical Society.

The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women’s Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients’ clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A “threshold” was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course.

Background This paper describes the design and test of an automated functional electrical stimulation (FES) system for poststroke rehabilitation training. The aim of automated FES is to synchronize electrically induced movements to assist residual movements of patients. Methods In the design of the FES system, an accelerometry module detected movement initiation and movement performed by post-stroke patients. The desired movement was displayed in visual game module. Synergy-based FES patterns were formulated using a normal pattern of muscle synergies from a healthy subject. Experiment 1 evaluated how different levels of trigger threshold or timing affected the variability of compound movements for forward reaching (FR) and lateral reaching (LR). Experiment 2 explored the effect of FES duration on compound movements. Results Synchronizing FES-assisted movements with residual voluntary movements produced more consistent compound movements. Matching the duration of synergy-based FES to that of patients could assist slower movements of patients with reduced RMS errors. Conclusions Evidence indicated that synchronization and matching duration with residual voluntary movements of patients could improve the consistency of FES assisted movements. Automated FES training can reduce the burden of therapists to monitor the training process, which may encourage patients to complete the training.

Coupled spin-crossover complexes in supramolecular systems feature rich spin phases that can exhibit collective behaviors. Here, we report on a molecular-level exploration of the spin phase and collective spin-crossover dynamics in metallo-supramolecular chains. Using scanning tunneling microscopy, spectroscopy, and density functional theory calculations, we identify an antiferroelastic phase in the metal-organic chains, where the Ni atoms coordinated by deprotonated tetrahydroxybenzene linkers on Au(111) are at a low-spin (

To improve the economic and environmental sustainability of agriculture, information is needed on how to target research, teaching, and outreach programs. However, conducting survey research in general, and with agricultural producers specifically, is increasingly challenging given issues such as declining response rates and limited resources. While studies examining the best practices for promoting higher response rates exist, few focus explicitly on agricultural producers. In three separate surveys conducted with agricultural producers in South Dakota in 2018 and 2019, we included experiments testing how token pre-incentives, a research partnership, and response mode options impacted response rates. We also examined how sample source and email augmentations influence survey responses. The study findings indicate that providing pre-incentives and multiple simultaneous response options can increase response rates with agricultural producers. On the other hand, email augmentation to mail surveys, sample source, and identification of select institutional research partnerships appear to have minimal effects.

Recently pedestrian detection has progressed significantly. However, detecting pedestrians of small scale or in heavy occlusions is still notoriously difficult. Besides, the generalization ability of pre-trained detectors across different datasets remains to be improved. Both of these issues can be attributed to insufficient training data coverage. To cope with this, we present an efficient data augmentation scheme by transferring pedestrians from other datasets into the target scene with a novel Attribute Preserving Generative Adversarial Networks (APGAN). The proposed methodology consists of two steps: pedestrian embedding and style transfer. The former step can simulate pedestrian images of various scale and occlusion, in any pose or background, thus greatly promoting the data variation. The latter step aims to make the generated samples more realistic while guarantee the data coverage. To achieve this goal, we propose APGAN, which pursues both good visual quality and attribute preserving after style transfer. With the proposed method, we can make effective sample augmentations to improve the generalization ability of the trained detectors and enhance its robustness to scale change and occlusions. Extensive experiment results validate the effectiveness and advantages of our method.

The data transmission between 5G floating base stations will be an important application area of the free space optical (FSO) link, but the problem of atmospheric disturbance will directly affect the application effect. An atmospheric turbulence suppression algorithm based on avalanche photodiode adaptive gain control (APDAGC) for the laser transmission terminal was proposed to suppress atmospheric turbulence. The given design mechanism and experimental results demonstrate its function to improve the performance of FSO. When APDAGC is over 1 km FSO, the variance of intensity fluctuation is lowered from 0.046 to 0.009, with the bit error rate reduced from 4.82E-6 to 1E-12. Comparatively, as APDAGC is over 6.5 km FSO, the variance of intensity fluctuation is decreased from 1.767 to 0.376, with the bit error rate varying from 4.6E-2 to 2.4E-4. Moreover, it was used for the first FSO transmission of 5G floating base station signals on an airship platform.

Targeted drug delivery remains attractive but challenging for cancer therapy. Cell-mediated drug delivery has emerged as a promising strategy to improve targeted drug delivery to tumors due to the intrinsic ability of certain types of cells (e.g., macrophage) to pass through physiological barriers and specifically home to tumors. To fundamentally understand how macrophage-based drug carriers transport and interact with the tumor microenvironment, we developed a tumor-microenvironment-on-a-chip (TMOC) model that enables the coculture of tumor spheroids and macrophages in a three-dimensional (3D) gel matrix. By introducing drug-loaded macrophages, the TMOC model allows real-time observation of macrophage migration toward the tumor, infiltration into tumor spheroids, and subsequent response of tumor to drugs. Our results demonstrated the superior capability of macrophages migrating toward the tumor and infiltrate tumor spheroids. Drug loading in macrophages had minimum effect on their cell viability, and drug-carrying macrophages exhibited greater tumor cell cytotoxicity compared to their nanoparticle counterparts. Our work highlighted the great potential of macrophages as novel drug carriers for targeted drug delivery, and the TMOC model serves as a versatile platform to enable quick evaluation of such cell-mediated drug delivery systems.