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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.

Designing advanced electrocatalysts for oxygen evolution at large current density (>500 mA cm-2 ) is critical to practical water splitting applications. Herein, a novel quasi-parallel NiFe layered double hydroxide (NiFe LDH) nanosheet arrays with pattern alignment on Ni foam was developed. The initial α-Ni(OH)2 layer induced effective coprecipitation between Ni2+ and Fe3+ for the formation of LDH phase, guaranteeing the electronic pulling effect among metal cations and enhancing the interaction between active materials and substrate for excellent adhesion and electrical conductivity. Quasi-parallel NiFe LDH nanoarrays exhibited outstanding oxygen evolution activity with a small Tafel slope of 30.1 mV dec-1 and overpotentials of 196, 255, and 284 mV at a current density of 10, 500, and 1000 mA cm-2 in 1.0 m KOH solution, respectively, and high stability over 40 h at 750 mA cm-2 . This work presents a new strategy towards fabricating electrode materials with exceptional performance.

As a connector, the spline is widely applied in gear transmission systems. In transmitting torque, the load and deformation of the spline are affected by the applied assembly method, and this effect leads a change in the time-varying meshing stiffness (TVMS) of the gear pair. Consequently, the nonlinear characteristics and vibration performance of the whole system can be affected. In this study, the TVMS of gear systems that use two different spline assembly methods, namely, the-side-fit and the-major-diameter-fit, is predicted by finite element contact analysis. Subsequently, the nonlinear dynamic model of the spline–gear system is established and its accuracy is verified through a vehicle vibration experiment. The numerical results reveal that the system assembled through the-side-fit and without spline exhibits a diverse range of periodic, sub-harmonic, and chaotic behaviours at high speed, whereas no bifurcation is observed through the-major-diameter-fit. As the magnitude of interference increases in the-major-diameter-fit, the dynamic transmission error decreases. Therefore, different assembly methods can affect the nonlinear characteristic. Moreover different magnitudes of interference in the-major-diameter-fit also have effects on the nonlinear characteristics and vibration performance of spline–gear systems.

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.

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 efficiency of metal belt CVT is an important index to evaluate its transmission performance, and the greater the axial deformation of the pulley, the greater the efficiency loss. In this paper, the deformation of pulley under different passive pressures is measured by experiment, and the reliability of the optimized 3D CAD model is verified by comparing with the data of the simulation model. And based on multi Island genetic algorithm (MIGA), using ISIGHT to integrate CATIA and ABAQUS, and the maximum stress is used as the constraint condition, penalty function integrate pulley deformation, mass and moment of inertia of the smallest to construct single objective function to optimize a CAD model, finally the test sample analysis verify the reliability of the optimization results. Research results show that, the optimized pulley mass was reduced by 16.96% and the inertia was reduced by 11.7%. Comparing the axial deformation of different speed ratios of the pulley, the deformation is smaller than that before the optimization, laid the foundation for the study of pulley transmission efficiency.

A method for diagnosis of misfire fault in internal combustion engine based on exhaust density of HC, CO2, O2 and the engine’s work parameters are presented in this paper. Rough sets theory is used to simplify attribute parameter reflecting exhaust emission and conditions of internal combustion engine and in which unnecessary properties are eliminated. The engine’s work parameters, exhaust emission with misfire fault and without fault are tested by the experimentation of CA6100 engine. A diagnosis model which describing the relationship between the misfire degree and the internal combustion engine’s exhaust emission and work parameters is established based on rough sets theory and RBF neural network. The model reduces the sample size, optimizes the neural network, increase the diagnosis correctness. The model is also trained by test data and MATLAB software. The model has been used to diagnosis internal combustion engine misfire fault, the result illustrates that this diagnosis model is suitable. This system can reduce input node number and overcome some shortcomings, such as neural network scale is too large and the rate of classification is slow.