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BackgroundDrug coated balloon (DCB) angioplasty can provide sustained anti-restenotic efficacy without the limitations of permanent vascular implantation and is presumably ideal for treating intracranial atherosclerotic disease. However, the safety of paclitaxel in the neurovasculature remains a concern.Methods242 patients with angiographically verified symptomatic stenosis >70% in intracranial arteries treated with DCB angioplasty were reviewed divided into two groups: group A, patients with stenotic intracranial arteries; and group B, patients with acute, subacute, or chronic occluded intracranial arteries. The primary endpoint was any stroke or death within 30 days. The secondary endpoint was arterial restenosis of >50% during follow-up.Results16 major and 12 minor complications occurred among 245 procedures (6.5% and 4.9%, respectively). Five patients died within 30 days after the procedure (2.1%, 5/242). 12 major and 12 minor complications occurred among 211 procedures in group A (5.7% and 5.7%). In group B, four major complications occurred among 34 procedures (11.8%). Hyperperfusion and perforator stroke accounted for half of all complications (53.6%, 15/28). Restenosis >50% was present in eight lesions during the follow-up period (4.8%, 8/167).ConclusionsAfter treatment with DCB angioplasty, complications were no different from those after standard balloon angioplasty or stenting. This study suggests that DCB angioplasty may be a safe and effective procedure for intracranial arterial stenosis.

Self-triggered pseudo terminal ingredients are proposed in this study for the classic dual-mode robust model predictive control (RMPC) of discrete-time linear systems. By resorting to the off-line design of pseudo terminal ingredients, viz. pseudo terminal set and pseudo terminal cost, the optimization problem to be solved online is transformed into several subproblems with short prediction horizons. Furthermore, the controller design is based on the nominal system state, which enables a self-triggered mechanism during the implementation. The proposed approach is able to steer the system state into a predetermined terminal constraint set via intermittent samplings. The resultant computational and communicational burden for the online part is significantly reduced, which brings great convenience for band-limited practical systems. Simulations demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

As an important component of microgrid system, the grid-connected inverter is composed of power electronic devices, which makes the system lack physical inertia and exhibit low output impedance. Especially in the transition process of grid connection, a large current impact will be generated, which may make the system unstable. Therefore, in response to these problems, a nonlinear control method is proposed that enables the grid-connected inverter to switch freely between grid-connected mode and island mode. The linear control part introduces the mechanical equations of the rotor of the synchronous motor, which has virtual inertia and damping, and at the same time derives the corresponding mathematical model. As regards the nonlinear control part, the nonlinear controller is designed by integrating the integral sliding mode control method and backstepping control method. Meanwhile, considering the uncertainties including external disturbances, measurement errors and modelling errors, an observer based on the radial basis function neural network (RBFNN) is proposed to estimate the uncertainties to offset their influence. Then, the stability of the control system is proved by Lyapunov stability criterion. Finally, the simulation results prove that the proposed control scheme can realize the stability of the microgrid system in several operation modes and ensure the sound dynamic performance with quick response comparing with other nonlinear control methods.

A permanent magnet linear synchronous motor (PMLSM) control strategy based on improved neural networks is proposed for the problem of inaccurate decoupling and poor adaptive performance of the traditional proportional–integral (PI) control method. In this paper, incremental broad learning (IBL) is selected as the neural networks training model. IBL is improved on the basis of broad learning, which can dynamically change the number of nodes while reducing the model training time. The IBL controller design process does not require a mathematical model of the control system. It can be designed based on data obtained from existing systems or factories without the necessary expertise, and can better enhance the nonlinearity of the controller. First, the IBL controller is trained and built by collecting relevant data based on the entire dynamic model of PMLSM. Then, test datasets and error evaluation metrics are used to determine the optimal hyperparameters of the IBL model. Finally, simulations and experiments in dSPACE show that the method proposed in this paper outperforms the conventional vector control method in terms of robustness and adaptivity. [ABSTRACT FROM AUTHOR]

Aiming at the problem of hybrid energy storage faults in large interconnected power systems, an integrated fault-tolerant control scheme is proposed to deal with actuator faults and external disturbances. Firstly, the hybrid energy storage system is introduced based on the traditional interconnected power system, and a new topological structure is given. Secondly, the state-space model of each area in the given topological structure is established considering the external disturbance and actuator fault of the power system. Then the sliding mode observer is designed and the fault-tolerant control of interconnected power systems is realized according to the estimated value and sliding mode technology. Finally, the simulation results on the model of four-area interconnected power system show that the proposed observer can realize fault estimation in a short time, and the fault-tolerant control effect is satisfied with nearly 30% reduction of regional control error compared with the traditional method.

The 1.55 μm laser technology is widely applied in military, information communication, biomedicine and other fields. With the deepening development of these application areas, the demand for novel 1.55 μm laser gain media is becoming increasingly urgent. This study reports a novel Yb3+, Er3+co-doped KBa0.94Ca0.06Y(MoO4)3(KBCYM) crystal. In this crystal, Yb3+serves as a sensitizer, significantly enhancing the emission intensity of Er3+in both visible and near-infrared bands. Notably, when the concentration of Yb3+reaches 6 mol%, the emission intensity peaks at 1.55 μm. Optical cross-section calculations reveal that the crystal exhibits a low laser pumping threshold at this concentration, demonstrating its potential as a laser gain medium. However, the crystal inevitably generates thermal effects during operation, which may adversely affect its performance. Therefore, real-time monitoring of the operating temperature is crucial. The thermal stability of the crystal was evaluated by measuring the temperature dependence of its luminescence intensity in the near-infrared band. Remarkably, even when the temperature rises to 553 K, the emission intensity at 1.55 μm only decreases by 10.9%. Additionally, the temperature sensing performance was evaluated using fluorescence intensity ratio techniques, yielding absolute and relative sensitivities of 0.00981 K–1at 453 K and 1.32%/K at 303 K, respectively, highlighting its potential for optical temperature sensing. Finally, through leveraging the unique properties of Yb3+, Er3+: KBCYM crystals, we successfully developed 1.55 μm luminescent optical devices with practical applications. These devices not only exhibit efficient luminescent performance, but also possess a self-temperature measurement function, opening up new avenues for the further development of laser technology.

Abstract Robust model predictive control (RMPC) has gained much attention in the past two decades. For classic finite-horizon RMPC, a terminal constraint set together with a terminal cost function are usually employed for the purpose of closed-loop stability. However, computational burden and feasibility region may need to be carefully addressed when the prediction horizon length becomes large, especially for the cases with additive disturbances. In this paper, a novel RMPC approach with pseudo terminal designs is proposed. Specifically, a bank of pseudo terminal sets (PTSs) are designed given a predefined terminal constraint set. Besides, a pseudo terminal cost (PTC) term is considered for each PTS. With the proposed approach, a long-horizon RMPC problem can be divided into several short-horizon ones. It is shown that both the PTSs and the corresponding PTCs can be computed off-line recursively. In the online implementation, selecting a PTS is realized in a self-triggered way. Meanwhile, a time-varying cost function, which is always an upper bound of the infinite-horizon cost function, is optimized online. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is associated with a dismal prognosis, and prediction of the prognosis of HCC can assist in therapeutic decision-makings. An increasing number of studies have shown that the texture parameters of images can reflect the heterogeneity of tumors, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of this study was to investigate the prognostic value of computed tomography (CT) texture parameters in patients with HCC after hepatectomy and to develop a radiomics nomogram by combining clinicopathological factors and the radiomics signature. Methods: In all, 544 eligible patients were enrolled in this retrospective study and were randomly divided into the training cohort (n= 381) and the validation cohort (n= 163). The tumor regions of interest (ROIs) were delineated, and the corresponding texture parameters were extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in the training cohort, and a radiomics signature was established. Then, the radiomics signature was further validated as an independent risk factor for overall survival (OS). The radiomics nomogram was established based on the Cox regression model. The concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram. Results: The radiomics signature was formulated based on 7 OS-related texture parameters, which were selected in the training cohort. In addition, the radiomics nomogram was developed based on the following five variables: α-fetoprotein (AFP), platelet-to-lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and radiomics score (Rad-score). The nomogram displayed good accuracy in predicting OS (C-index = 0.747) in the training cohort and was confirmed in the validation cohort (C-index = 0.777). The calibration plots also showed excellent agreement between the actual and predicted survival probabilities. The DCA indicated that the radiomics nomogram showed better clinical utility than the clinicopathologic nomogram. Conclusion: The radiomics signature is a potential prognostic biomarker of HCC after hepatectomy. The radiomics nomogram that integrated the radiomics signature can provide a more accurate estimation of OS than the clinicopathologic nomogram for HCC patients after hepatectomy.

Randomized studies have obtained varying findings regarding the benefits and toxicities of bevacizumab in the treatment of nonsmall cell lung cancer (NSCLC). It is unclear whether the discrepancies among trials are due to ethnicracial differences. We therefore performed a meta-analysis of all published, randomized, controlled clinical trials involving bevacizumab in patients with NSCLC to assess its effectiveness and safety in Asian and non-Asian populations.