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Highly active Keggin-type silicotungstic acid (SiW12) with oxygen vacancy (Ov) defects was encapsulated into the magnetic floral N-doped carbon microspheres (γ-Fe2O3@NC-300) through the facile one-step air pyrolysis of the precursor comprising core–shell Fe3O4@polydopamine (Fe3O4@PDA) and SiW12 to prepare γ-Fe2O3@NC@SiW12-300. The fabricated catalysts were systematically characterized and subsequently employed for the oxidation desulfurization (ODS) of the model fuel. The magnetic floral γ-Fe2O3@NC@SiW12-300 catalyst exhibited nearly perfect catalytic activity, which under mild conditions could remove 100% amount of 4000 ppm DBT in model fuel within 20 min (0.03 g catalysts and n(H2O2)/n(S) of 2). The catalyst activity is mainly attributed to the high activity SiW12 with the Ov defect and its outstanding dispersibility in γ-Fe2O3@NC, along with the high number of exposed active sites. A selected catalyst, γ-Fe2O3@NC@SiW12-300, showed a noticeable turnover frequency (TOF) (110.07 h−1) and lower activation energy (38.79 kJ mol−1) in oxidative desulfurization (ODS) with good recyclability. HO˙ radical was found to be the active species involved in ODS as confirmed by the EPR and scavenger experiments. Additionally, the fabricated catalyst can be conveniently separated and recycled within an externally applied magnetic field. [ABSTRACT FROM AUTHOR]

Two‐dimensional metal‐organic frameworks (2D MOFs) with more exposed active sites and stable structures are auspicious electrode materials for high‐performance lithium‐ion batteries (LIBs). In the current work, ultrasonic treatment was used to prepare two‐dimensional CoNi‐BDC ultrathin nanosheets (N‐CoNi‐BDC) by coordinating Ni2+, Co2+, and benzenedicarboxylic acid (BDC). The N‐CoNi‐BDC was used as the anode of LIBs, demonstrating an exceptional reversible storage capacity of 1358 mAh g−1 after 200 cycles at 0.1 A g−1. In addition, a charging capacity of 757 mAh g−1 was observed at a high current density of 1 A g−1 without observable capacity degradation up to 300 cycles. The superior behavior of N‐CoNi‐BDC was attributed to its distinct morphology of ultrathin nanosheets with more exposed active sites and stable structures. Ex‐situ FTIR and XPS results revealed that the benzene ring, carboxylate, and Ni2+ in N‐CoNi‐BDC were the active parts of lithium‐ion storage. [ABSTRACT FROM AUTHOR]

Metal cutting is a complex process with strong randomness and nonlinear characteristics in its dynamic behavior, while tool wear or fractures will have an immediate impact on the product surface quality and machining precision. A combined prediction method comprising modal decomposition, multi-channel input, a multi-scale Convolutional neural network (CNN), and a bidirectional long-short term memory network (BiLSTM) is presented to monitor tool condition and to predict tool-wear value in real time. This method considers both digital signal features and prediction network model problems. First, we perform correlation analysis on the gathered sensor signals using Pearson and Spearman techniques to efficiently reduce the amount of input signals. Second, we use Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to enhance the local characteristics of the signal, then boost the neural network's identification accuracy. In addition, the deconstructed signal is converted into a multi-channel input matrix, from which multi-scale spatial characteristics and two-way temporal features are recovered using multi-scale CNN and BiLSTM, respectively. Finally, this strategy is adopted in simulation verification using real PHM data. The wear prediction experimental results show that, in the developed model, C1, C4, and C6 have good prediction performance, with RMSE of 8.2968, 12.8521, 7.6667, and MAE of 6.7914, 9.9263, and 5.9884, respectively, significantly lower than SVR, B-BiLSTM, and 2DCNN models. [ABSTRACT FROM AUTHOR]

The dynamic characteristics of a multi-span double-curved brick arch thin-shell factory of Changleyuan in Baoji City and the dynamic response to train vibration load were studied using field dynamic tests and finite-element numerical simulations, and a vibration evaluation of the thin-shell factory was carried out. The results showed that the first-order frequency of the thin-shell factory was 6.24 Hz in the horizontal direction (east–west) and 9.31 Hz in the vertical direction. Moreover, it was established that the horizontal vibration is the overall vibration of the factory, while the vertical vibration is the individual vibration of the double-curved brick arch. In addition, the self-oscillation frequency obtained from the numerical simulation results was greater compared with the field measurements, with a maximum error rate of 7.14%. Both in acceleration and velocity, the vertical vibration for each measurement point was larger than the horizontal vibration, and the farther away from the railroad, the smaller the vibration. The vibration of the velocity at the bottom of the arch was almost the same as that at the top of the arch, while the acceleration vibration at the bottom of the arch was significantly larger than that at the top of the arch, with an average amplitude of 40.64%. For every 20 km/h increase in train running speed, the average increase in vertical acceleration amplitude, vertical velocity amplitude, horizontal acceleration amplitude, and horizontal velocity amplitude for each measurement point of the thin-shell factory was 35.4%, 29.8%, 23.7%, and 12.5%, respectively. When v = 150 km/h, the maximum velocity amplitude for each measurement point of the thin-shell factory was 1.163 mm/s, which is less than the security specification limit of 2.5 mm/s, such that the security of the thin-shell factory meets the requirement, and the maximum horizontal velocity amplitude was 0.272 mm/s, which is close to the integrity specification limit of 0.27 mm/s, such that the integrity of the thin-shell factory just exceeds the requirement; so it is suggested that train running speeds should not exceed 150 km/h and that the thin-shell factory needs to strengthen the monitoring and protection of its integrity. [ABSTRACT FROM AUTHOR]

This study puts forward a powerful new strategy that allows highly efficient generation of the shaping transverse nondiffracting caustics that concentrate around the expected curve. First, under the condition of stationary phase approximation, as the typical example of caustic beams, the analytical formula of nondiffracting deltoid and pentagram caustics is theoretically derived. Based on the obtained analytical formula, the desired caustics of deltoid and pentagram are numerically simulated. Hence, their optical shape and the propagation characteristics of nondiffracting caustics can be investigated. Then, based on the given generating mechanism, the corresponding experimental system to experimentally explore the nondiffracting deltoid and pentagram caustics with good self‐healing properties is constructed. Compared with the previous generating methods, the proposed method has a high light energy utilization. [ABSTRACT FROM AUTHOR]

Stochastic configuration network (SCN) is a mathematical model of incremental generation under a supervision mechanism, which has universal approximation property and advantages in data modeling. However, the efficiency of SCN is affected by some network parameters. An optimized searching algorithm for the input weights and biases is proposed in this paper. An optimization model with constraints is first established based on the convergence theory and inequality supervision mechanism of SCN; Then, a hybrid bat-particle swarm optimization algorithm (G-BAPSO) based on gradient information is proposed under the framework of PSO algorithm, which mainly uses gradient information and local adaptive adjustment mechanism characterized by pulse emission frequency to improve the searching ability. The algorithm optimizes the input weights and biases to improve the convergence rate of the network. Simulation results over some datasets demonstrate the feasibility and validity of the proposed algorithm. The training RMSE of G-BAPSO-SCN increased by 5.57 × 10 − 5 and 3.2 × 10 − 3 compared with that of SCN in the two regression experiments, and the recognition accuracy of G-BAPSO-SCN increased by 0.07% on average in the classification experiments. [ABSTRACT FROM AUTHOR]

The tendon-to-bone interface is a special structure connecting the tendon and bone and is crucial for mechanical load transfer between dissimilar tissues. After an injury, fibrous scar tissues replace the native tendon-to-bone interface, creating a weak spot that needs to endure extra loading, significantly decreasing the mechanical properties of the motor system. Macrophages play a critical role in tendon-bone healing and can be divided into various phenotypes, according to their inducing stimuli and function. During the early stages of tendon-bone healing, M1 macrophages are predominant, while during the later stages, M2 macrophages replace the M1 macrophages. The two macrophage phenotypes play a significant, yet distinct, role in tendon-bone healing. Growing evidence shows that regulating the macrophage phenotypes is able to promote tendon-bone healing. This review aims to summarize the impact of different macrophages on tendon-bone healing and the current immunomodulatory biomaterials for regulating macrophages, which are used to promote tendon-bone healing. Although macrophages are a promising target for tendon-bone healing, the challenges and limitations of macrophages in tendon-bone healing research are discussed, along with directions for further research. [ABSTRACT FROM AUTHOR]

Since the rolling bearing is complex during the signal acquisition process, there is a certain loss during the process of collecting the vibration signal. This has led to the weakness of the early fault characteristics of the rolling bearing, affecting the accuracy of the rolling bearing fault feature extraction. In response to the above problems, an early fault detection method based on the Improved Deep Principal Component Analysis (ID-PCA) is proposed. The proposed method uses the time-series characteristic information of the vibration signal to establish a model, which solves the problem that the principal component analysis method cannot detect the vibration signal directly. Through the deep decomposition theorem, a multi-layer data processing model is established to fully mine the weak fault features in the vibration signal. It can solve the problem of inaccurate early fault detection results due to weak fault feature information. The reliability of this method is proved theoretically through sensitivity analysis. Finally, through experimental simulation, the accuracy and feasibility of this method are proved from the perspective of practice. [ABSTRACT FROM AUTHOR]

This paper presents a new control strategy for stochastic distribution shape tracking regarding non-Gaussian stochastic non-linear systems. The objective can be summarised as adjusting the probability density function (PDF) of the system output to any given desired distribution. In order to achieve this objective, the system output PDF has first been formulated analytically, which is time-variant. Then, the PDF vectorisation has been implemented to simplify the model description. Using the vector-based representation, the system identification and control design have been performed to achieve the PDF tracking. In practice, the PDF evolution is difficult to implement in real-time, thus a data-driven extension has also been discussed in this paper, where the vector-based model can be obtained using kernel density estimation (KDE) with the real-time data. Furthermore, the stability of the presented control design has been analysed, which is validated by a numerical example. As an extension, the multi-output stochastic systems have also been discussed for joint PDF tracking using the proposed algorithm, and the perspectives of advanced controller have been discussed. The main contribution of this paper is to propose: (1) a new sampling-based PDF transformation to reduce the modelling complexity, (2) a data-driven approach for online implementation without model pre-training, and (3) a feasible framework to integrate the existing control methods. [ABSTRACT FROM AUTHOR]

Summary: In this article, we propose a deep Koopman model predictive control (MPC) strategy to improve the transient stability of power grids in a fully data‐driven manner. Due to the high‐dimensionality and the nonlinearity of the transient process, we use the Koopman operator to map the original nonlinear dynamics into an infinite dimensional linear system. To facilitate the control design, we first utilize the deep neural network method to efficiently train observable functions to approximate the Koopman operator so that the obtained dynamics in the high dimensional space is a linear system. We then propose an MPC strategy for the obtained high dimensional linear system. The proposed control strategy utilizes energy storage units, which inject or absorb real power at the synchronous generator buses to enhance the transient stability. Simulation studies implemented on the IEEE 9‐bus 3‐machine test system and the IEEE 39‐bus 10‐machine test system illustrate the performance of the proposed deep Koopman MPC strategy. The results demonstrate that the proposed control strategy effectively enhances the transient stability of the system even in the presence of severe faults. [ABSTRACT FROM AUTHOR]

Due to the strict personnel control measures in COVID-19 epidemic, the control system cannot be maintained and managed manually. This puts forward higher requirements for the accuracy of its fault-tolerant performance. The control system plays an increasingly important role in the rapid development of industrial production. When the sensor in the system fails, the system will become unstable. Therefore, it is necessary to accurately and quickly diagnose the faults of the system sensors and maintain the system in time. This paper takes the control system as the object to carry out the fault diagnosis and fault-tolerant control research of its sensors. A network model of wavelet neural network is proposed, and an improved genetic algorithm is used to optimize the weights and thresholds of the neural network model to avoid the deficiencies of traditional neural network algorithms. For the depth sensor of a certain system, an online fault diagnosis scheme based on RBF (Radial Basis Function) neural network and genetic algorithm optimized neural network was designed. The disturbance fault, "stuck" fault, drift fault and oscillation fault of the depth sensor are simulated. Simulation experiments show that both online fault diagnosis schemes can accurately identify sensor faults and the genetic algorithm optimized neural network is superior to RBF neural network in both recognition accuracy and training time under the influence of COVID-19. [ABSTRACT FROM AUTHOR]

The energy efficiency and output power of a quantum thermoelectric system with multiple electric currents and only one heat current are studied. The system is connected to the hot heat bath (cold bath) through one terminal (multiple terminals). In such configurations, there are multiple thermoelectric effects coexisting in the system. Using the Landauer–Büttiker formalism, we show that the cooperation between the two thermoelectric effects in the three-terminal thermoelectric systems can lead to markedly improved performance of the heat engine. Such improvement also occurs in four-terminal thermoelectric heat engines with three output electric currents. Cooperative effects in these multi-terminal thermoelectric systems can considerably enlarge the physical parameter region that realizes high energy efficiency and output power. For refrigeration, we find that the energy efficiency can also be substantially improved by exploiting the cooperative effects in multi-terminal thermoelectric systems. All these results reveal a useful approach toward high-performance thermoelectric energy conversion in multi-terminal mesoscopic systems. [ABSTRACT FROM AUTHOR]