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As pneumatic artificial muscles (PAMs) are similar to biological muscles in structure and movement mechanisms, parallel robots actuated by PAMs have development prospects in rehabilitation and industry, with advantages such as compliance, high safety, strong bearing capacity, and satisfactory dynamic performance. However, the parameter uncertainties and model complexity related to inherent characteristics of parallel robots actuated by PAMs (e.g., time-varying, coupling, hysteresis, creep, and high nonlinearity), bring challenges to accurate dynamic modeling and controller design. Therefore, to achieve satisfactory tracking performance, this article presents an adaptive compensation tracking controller with error constraints for parallel robots actuated by PAMs. The proposed controller deals with parameter uncertainties by estimating system parameters to ensure accurate tracking, which is indicated as an effective solution for a combination of PAMs and parallel robots. Furthermore, using desired trajectory signals in the complicated regression matrix, the online computational burden is significantly reduced. Moreover, to improve operation safety further, an auxiliary term with a theoretical demonstration guarantees that the tracking errors are maintained within allowable ranges. Then, the closed-loop stability is demonstrated by Lyapunov techniques. As far as we know, it is the first time that the challenges of parameter uncertainties, computational burdens, and error constraints of parallel robots actuated by PAMs are simultaneously addressed, which has both theoretical significance and practical value. Finally, the hardware experiments are implemented under different scenarios, and the results indicate that the proposed method achieves satisfactory tracking performance.

Predicting the quality of transmission (QoT) is a critical task in the management and optimization of modern fiber-optic networks. Traditional machine learning (ML) QoT prediction models, typically trained on pre-collected datasets, are designed to make long-term predictions once deployed. However, this static training strategy often falls short in the face of time-dependent network evolution and variations. We identify the root cause of these shortcomings as shifts in data distribution, which are not accounted for in conventional static models. In response to these challenges, we propose an online continual learning pipeline that is specifically designed for stable QoT prediction in optical networks. This pipeline directly addresses the problem of distribution shifts by continuously updating the prediction model in response to real-time network data. We explore and compare various strategies within this framework and demonstrate that the integration of the adaptive retraining strategy and the regularized online continual learning algorithm (OCL-REG) significantly enhances the QoT prediction stability while optimizing the resource efficiency. OCL-REG demonstrates superior adaptability and stability, achieving an average cumulative mean squared error (C-MSE) of 0.19 on a testbench with a data distribution shift sequence containing 1000 batches. Moreover, the OCL-REG model requires fewer samples for adaptation, averaging around 107 samples, compared to the conventional retraining strategy, which requires an average of 253 samples. Our approach presents a paradigm shift in QoT prediction, moving from a static to a dynamic, lifelong learning model that is more attuned to the evolving realities of real fiber-optic networks.

An improved understanding of complex solute transport processes in a single fracture is the foundation for advancing fracture network research. This study addresses a radial reactive transport problem in a fractured rock matrix system, with an emphasis on the role of non-equilibrium sorption. A new model that integrates a two-site nonequilibrium sorption framework was developed in a single fracture under a radial flow condition. Semianalytical models were derived utilizing Laplace transform techniques. These solutions offer insights into the spatio-temporal concentration, back diffusion and diffusive mass flux issues. When benchmarked against prior solutions, the approach presented here demonstrates superior accuracy and robustness. Several significant results were obtained. First, nonequilibrium sorption leads to a more pronounced peak in the breakthrough curves (BTCs) and diminishes the tailing of BTCs in the system. Second, a higher degradation rate for nonequilibrium sorption reduces the concentration of BTCs in the later stage but has minimal impact in the early stage. Third, after clean water injection, the back diffusion from the rock matrices into the fracture was observed, predominantly near the wellbore, accounting for the tailing behavior. Meanwhile, the diffusive mass flux exhibits a sharp rise and then declines in the early stages. Finally, an increased proportion of equilibrium sorption in the fracture leads to a decrease in the diffusive mass flux, whereas it displays the inverse trend in the rock matrix. In general, these findings underscore the essential role of nonequilibrium sorption considerations for the accurate representation of solute transport processes in fractured rock matrix systems.

The increasing complexity and dynamicity of future optical networks will necessitate accurate, fast, and low-cost quality-of-transmission (QoT) estimation. Machine learning-based QoT estimation models have shown promise in ensuring the reliability and efficiency of optical networks. However, the data-driven nature of these models impedes their application in practical settings. To address the problem of limited data availability in the target domain, known as the few-shot learning problem, we propose a domain adversarial adaptation method that aligns the distributions of representations from different source and target domains by minimizing the domain discrepancy quantified by the approximate Wasserstein distance. We demonstrate the method’s effectiveness through a theoretical proof and two example adaptations, i.e., from simulation to experimental data and from experimental to real network data. Our method consistently outperforms commonly used artificial neural networks (ANNs) and more advanced transfer learning approaches for various target domain data sizes. More profoundly, we show two ways to further improve the prediction accuracy, i.e., incorporating unlabeled target domain data in the training stage and utilizing the learned representations after training to train a new ANN with a reweighting strategy. In the adaptation to actual field data, our model, trained with only eight labeled network data samples, outperforms an ANN trained with 300 samples, thus reducing the labeled target domain data burden by more than 97%. The proposed method’s adaptability and generalizability make it a promising solution for accurate QoT estimation with low data requirements in future intelligent optical networks.

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The enhanced remote area communication (eRAC) scenario is an important growth point in the communication market. In some remote areas where optical fiber access cannot be realized or the laying cost is too high, fixed wireless access (FWA) is an appropriate supplementary solution for eRAC. Adopting analog radio over fiber (A-RoF) technology to implement FWA can overcome the bandwidth limitation of electronic devices and realize high-frequency carrier communication economically to achieve high-capacity wireless communication. Also, probabilistic shaping (PS) technology can be combined with A-RoF to further improve the flexibility of the network and coverage of service provision. However, in the PS-A-RoF network, the high RF power introduces more undesired nonlinear effects into the network, and it is often necessary to deploy supervised machine learning (ML) compensation modules in wireless receivers (WRs). But the module performances are affected by the uneven probability distribution of PS-QAM constellation points. In this paper, we employ the PS-A-RoF nonlinear model to theoretically investigate the correlation between the distribution of training symbols and the wireless A-RoF system’s performance. Our analysis reveals that reducing the variance of training symbol power contributes to a lower BER in the A-RoF network. We introduce a borderline random over-sampling (B-ROS) that matches with the PS-A-RoF nonlinear model, instead of the mainstream ROS, which is only at the data level. Based on the B-ROS scheme, only the minority examples below the borderline are over-sampled to reach a better variance performance. Introducing the B-ROS method into the supervised complex value nonlinear compensation module can further improve the decision accuracy of WRs with the restoration of phase information, without increasing additional computational resource consumption. The vector noise power, training symbol power variance, and noise factor metrics have been calculated to optimize the borderline value of our ML-based approach. We also present experimental data on the proof-of-concept A-RoF experiment for PS-64QAM. The results demonstrate a promising nonlinear compensation performance of the B-ROS WR, and the optimal borderline agrees well with the one deduced from the theoretical model under certain transmission conditions. Our proposed B-ROS scheme lessens the training size demand and can improve the receiver sensitivity by 0.51 dB compared to the common ML-based WR and by 0.7 dB compared to the conventional ROS scheme.

As an essential part of modern power systems, power electronics have improved system performance. They also brought new dynamic and transient problems and posed significant challenges to precise and efficient simulation. Traditional modeling and solving methods fail to balance between the simulation accuracy and efficiency, and they are not flexible enough to adapt to converters with various new topologies. This article proposed a widely applicable modeling and simulation method based on the unit switching circuit that greatly promote the simulation efficiency and modeling simplicity by converting switching circuits into constant sub-grid equations with injection functions. Critical issues for engineering applications, such as the blocking state and the triggering deadband of converters, were also discussed and addressed by boundary balance and conduction path estimation. The error and stability of the solving method were qualitatively analyzed, with further discussions on the validity and numerical features. Several cases, including common and general cases and a real project of power electronic microgrid, tested and verified the accuracy, generalization ability, and efficiency of the proposed method.

A nanofibrous composite reverse osmosis (RO) membrane with a polyamide barrier layer containing interfacial water channels was fabricated on an electrospun nanofibrous substrate via an interfacial polymerization process. The RO membrane was employed for desalination of brackish water and exhibited enhanced permeation flux as well as rejection ratio. Nanocellulose was prepared by sequential oxidations of 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) and sodium periodate systems and surface grafting with different alkyl groups including octyl, decanyl, dodecanyl, tetradecanyl, cetyl, and octadecanyl groups. The chemical structure of the modified nanocellulose was verified subsequently by Fourier transform infrared (FTIR), thermal gravimetric analysis (TGA), and solid NMR measurements. Two monomers, trimesoyl chloride (TMC) and m-phenylenediamine (MPD), were employed to prepare a cross-linked polyamide matrix, i.e., the barrier layer of the RO membrane, which integrated with the alkyl groups-grafted nanocellulose to build up interfacial water channels via interfacial polymerization. The top and cross-sectional morphologies of the composite barrier layer were observed by means of scanning electron microscopy (SEM), atomic force microscopy (AFM), and transmission electron microscopy (TEM) to verify the integration structure of the nanofibrous composite containing water channels. The aggregation and distribution of water molecules in the nanofibrous composite RO membrane verified the existence of water channels, demonstrated by molecular dynamics (MD) simulations. The desalination performance of the nanofibrous composite RO membrane was conducted and compared with that of commercially available RO membranes in the processing of brackish water, where 3 times higher permeation flux and 99.1% rejection ratio against NaCl were accomplished. This indicated that the engineering of interfacial water channels in the barrier layer could substantially increase the permeation flux of the nanofibrous composite membrane while retaining the high rejection ratio as well, i.e., to break through the trade-off between permeation flux and rejection ratio. Antifouling properties, chlorine resistance, and long-term desalination performance were also demonstrated to evaluate the potential applications of the nanofibrous composite RO membrane; remarkable durability and robustness were achieved in addition to 3 times higher permeation flux and a higher rejection ratio against commercial RO membranes in brackish water desalination.

In this paper, the fixed-time synchronization issue of complex networks under stochastic fluctuations and impulsive effects is studied. First, a novel lemma for the fixed-time stability of nonlinear impulsive system with stochastic perturbations is given. Different from some early published works in which the comparative system technique was used to explore the stability of impulsive systems, the settling time is estimated based on direct calculation approach, which end up more accurate settling time estimation. Then, with the help of this lemma, a simple continuous control protocol is designed and some sufficient conditions for the fixed-time synchronization of the complex networks are given by employing the inequality technique and Lyapunov function method. At the end, two examples are given to verify the validity of obtained theoretical results.

Rail defect detection is crucial to rail operations safety. Addressing the problem of high false alarm rates and missed detection rates in rail defect detection, this paper proposes a deep learning method using B-scan image recognition of rail defects with an improved YOLO (you only look once) V3 algorithm. Specifically, the developed model can automatically position a box in B-scan images and recognize EFBWs (electric flash butt welds), normal bolt holes, BHBs (bolt hole breaks), and SSCs (shells, spalling, or corrugation). First, the network structure of the YOLO V3 model is modified to enlarge the receptive field of the model, thus improving the detection accuracy of the model for small-scale objects. Second, B-scan image data are analyzed and standardized. Third, the initial training parameters of the improved YOLO V3 model are adjusted. Finally, the experiments are performed on 453 B-scan images as the test data set. Results show that the B-scan image recognition model based on the improved YOLO V3 algorithm reached high performance in its precision. Additionally, the detection accuracy and efficiency are improved compared with the original model and the final mean average precision can reach 87.41%.

In this study, Cf/SiC composites with excellent mechanical and thermal properties were prepared by combining binder jetting (BJ) additive manufacturing with liquid silicon infiltration (LSI) process. The introduction of Cfinto the Cf/SiC mixed powder reduced its spreading ability, which reduced the density, strength, and precision of the Cf/SiC green parts. However, phenolic resin infiltration and pyrolysis (PRIP) treatment compensated for the decrease in the density of the green parts resulting from the introduction of Cf. By optimizing the number of PRIP cycles to increase the pyrolytic carbon (PyC) content in the carbonized parts, the Cfin the green parts successfully prevented the reaction with molten Si in the LSI and played an important role in strengthening and toughening the composites. The flexural strength, fracture toughness, and thermal conductivity of the Cf/SiC composites reached the maximum values of 316 ± 16 MPa, 4.81 ± 0.12 MPa·m1/2, and 140 W/m·K, respectively. This study presents future opportunities for the cost-effective and efficient industrial manufacturing of Cf/SiC complex structures.

The contra-rotating open rotor (CROR) engine has gained attention for its outstanding fuel-saving potential. Attention needs to be paid to the propulsion efficiency of the contra-rotating open rotor, as it is the key to realizing the engine’s fuel-saving advantage. In this paper, the performance of CRORs with different lean angles is investigated by numerical simulation. Furthermore, the mechanism of the influence of leaned blades on the propulsion efficiency of the CROR is analyzed. The results show that the positive leaned blades (pressure-surface down) significantly improve the propulsion efficiency of the CROR, with a maximum improvement of 1.34%. In contrast, the negative leaned blades (suction surface down) reduce propulsion efficiency. Due to the radial equilibrium equation, the positive lean increases the radial pressure gradient. The increased gradient enhances flow on the suction surface and reduces the flow reversal region, thus improving the propulsion efficiency. At low advance ratios, the enhanced radial flow due to the leaned blades makes the optimization less effective. By comparing the inlet conditions of the front and rear rotors, it was found that the front rotor was less likely to have a flow reversal region compared to the rear rotor. If a flow reversal region occurs in the channel, it is necessary to introduce leaned blades. The most recommended lean angle is the one that makes the flow reversal region disappear exactly.

Calcite mesocrystals were proposed, and have been widely reported, to form in the presence of polymer additives via oriented assembly of nanoparticles. However, the formation mechanism and the role of polymer additives remain elusive. Here, inspired by the biomineralization process of sea urchin spine comprising magnesium calcite mesocrystals, we show that calcite mesocrystals could also be obtained via attachment of amorphous calcium carbonate (ACC) nanoparticles in the presence of inorganic zinc ions. Moreover, we demonstrate that zinc ions can induce the formation of temporarily stabilized amorphous nanoparticles of less than 20 nm at a significantly lower calcium carbonate concentration as compared to pure solution, which is energetically beneficial for the attachment and occlusion during calcite growth. The cation-mediated particle attachment crystallization significantly improves our understanding of mesocrystal formation mechanisms in biomineralization and offers new opportunities to bioprocess inspired inorganic ions regulated materials fabrication.Zinc was used as a cationic additive to mediate the crystallization of amorphous 3 calcium carbonate, leading to the formation of calcite mesocrystals via a particle 4 attachment crystallization mechanism.

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.