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The driving experiment of SiO2 microspheres in a water environment was carried out by using tapered fiber microstructures to transmit short pulse lasers. The fiber microstructure can generate plasma and spherical shock waves to drive SiO2 microspheres. Through theoretical simulation, the propagation characteristics of shock waves and the dynamic characteristics of microspheres were studied. In the experiment, a high-speed COMS camera was used to capture the images of shock wave diffusion and microsphere motion. A linear relationship between the driving behavior of microspheres and the laser energy distribution is observed. The driving behavior of microspheres is attributed to the resultant force caused by spherical shock wave diffusion. We find that the initial driving velocity approximately follows the inverse quadratic function of the radius ratio of the spherical wave, which is consistent with the experimental results. Compared with the traditional technology, this method has the advantages of directional stability, good security, anti-interference, and so on. It can be used for stable directional driving of micron objects in a water environment.

Stress imbalance significantly affects the performance of a press-pack insulated gate bipolar transistor (IGBT). Time-variant loads and conditions lead to the stress fluctuations, exacerbating the impacts. The conventional reliability optimization faces efficiency barriers due to the nested time-variant reliability analysis and design optimization. In this paper, a time-variant reliability optimization approach for press-pack IGBTs is proposed to address the efficiency issue of the IGBT reliability optimization. The performance functions of the maximum and typical stresses are formulated as the optimization objective and constraint. A time-variant reliability optimization model is formulated considering the stress balance reliability degradation within the service cycle. A decoupling algorithm is proposed to transform the nested optimization into a sequential iteration of static reliability optimization and time-variant reliability analysis. The reliability analysis utilizes the performance function continuity in the time domain to reduce the evaluations for the most likelihood points, thereby enhancing efficiency. Numerical and experimental results on an actual IGBT demonstrate the accuracy of the stress balance performance analysis. The time-variant reliability optimization based on the performance functions improves the stress balance performance by 16.3% and meets the reliability requirements within the service cycle. Compared with the conventional double-loop approach, the difference between the solution of the proposed approach with the reference solution is 0.4%, and the efficiency is 334 times that of the double-loop approach. The performance advantages in accuracy and efficiency exhibit the application potential of this approach.

ObjectiveRed blood cell (RBC) parameters are associated with outcomes following aneurysmal subarachnoid hemorrhage (aSAH), but their predictive value remains uncertain. This study aimed to detect the association between RBC parameters and functional outcome in aSAH patients undergoing surgical clipping.MethodsThis retrospective observational study included aSAH patients who underwent surgical clipping at Affiliated Hospital of North Sichuan Medical College between August 2016 and September 2019. The functional outcome following aSAH was assessed by modified Rankin Scale (mRS), and mRS 3–6 was defined as poor functional outcome.ResultsOut of 187 aSAH patients included (62% female, 51–66 years old), 73 patients had poor functional outcome. Multivariate logistic regression of admission parameters showed that World Federation of Neurosurgical Societies (WFNS) grade (odds ratio [95% CI]: 1.322 [1.023–1.707], p = 0.033) and white blood cell (WBC) (odds ratio [95% CI]: 1.136 [1.044–1.236], p = 0.003) were independently associated with poor functional outcome. In postoperative parameters, RBC distribution width (RDW) (odds ratio [95% CI]: 1.411 [1.095–1.818], p = 0.008), mean platelet volume (MPV, odds ratio [95% CI]: 1.253 [1.012–1.552], p = 0.039) and admission WFNS grade (odds ratio [95% CI]: 1.439 [1.119–1.850], p = 0.005) were independently associated with poor functional outcome. The predictive model including WFNS grade, admission WBC, and postoperative RDW and MPV had significantly higher predictive power compared to WFNS grade alone (0.787 [0.722–0.852] vs. 0.707 [0.630–0.784], p = 0.024). The combination of WFNS grade and WBC on admission showed the highest positive predictive value (75.5%) and postoperative RDW and MPV combined with admission WFNS grade and WBC showed the highest negative predictive value (83.7%).ConclusionPostoperative RDW is independently associated with poor functional outcome in aSAH patients undergoing surgical clipping. A combined model containing postoperative RDW may help predict good outcome in patients with aSAH after timely aneurysm clipping.

IntroductionPost-neurosurgical central nervous system (CNS) infection caused by multidrug-resistant (MDR)/extensively drug-resistant (XDR) Gram-negative bacteria remains a major clinical challenge. This study describes our experience of treating such patients with combined intraventricular (IVT) and intravenous (IV) polymyxin B administration.MethodsThis retrospective study included six patients with post-neurosurgical CNS infections of carbapenem-resistant Acinetobacter baumannii (CRAB) or carbapenem-resistant Klebsiella pneumoniae (CRKP). All patients were treated in the intensive care unit (ICU) of First Affiliated Hospital, Zhejiang University School of Medicine (Hangzhou, China) between November 2020 and November 2021, and all received IVT plus IV polymyxin B. Data including patients' characteristics, therapeutic process, symptoms, cerebrospinal fluid (CSF) examination, laboratory tests, and complications were collected.ResultsSix patients with post-neurosurgical CNS infection were enrolled in the study. The patients comprised five males and one female, and the average age was 58 years (range, 38–73 years). Four out of the six cases were CRAB-positive in CSF culture, while two cases were CRKP-positive. The mean duration of polymyxin B administration was 14 ± 5.69 days (range, 6–20 days). The average period of patients reaching CSF sterilization was 10.33 ± 3.67 days (range, 5–14 days). All six cases were cured without acute kidney injury or epilepsy.ConclusionIVT plus IV polymyxin B is a safe and effective treatment for post-neurosurgical patients with intracranial infection caused by MDR/XDR Gram-negative bacteria.

Hybrid electric vehicles equipped with continuously variable transmission show dramatic improvements in fuel economy and driving performance because they can continuously adjust the operating points of the power source. This article proposes an optimal control strategy for continuously variable transmission–based hybrid electric vehicles with a pre-transmission parallel configuration. To explore the fuel-saving potential of the given configuration, a ‘control-oriented’ quasi-static vehicle model is built, and dynamic programming is adopted to determine the optimal torque split factor and continuously variable transmission speed ratio. However, a single-criterion cost function will lead to undesirable drivability problems. To tackle this problem, the main factors affecting the driving performance of a continuously variable transmission–based hybrid electric vehicle are studied. On that basis, a multicriterion cost function is proposed by introducing drivability constraints. By varying the weighting factors, the trade-off between fuel economy and drivability can be evaluated under a predetermined driving cycle. To validate the effectiveness of the proposed method, simulation experiments are performed under four different driving cycles, and the results indicate that the proposed method greatly enhanced the drivability without significantly increasing fuel consumption. Compared to a single-criterion cost function, the use of multiple criteria is more representative of real-world driving behaviour and thus provides better reference solutions to evaluate suboptimal online controllers.

The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort.