#12 - Electronic Cigarettes Induce Chronic Obstructive Pulmonary Disease in a Pre-Clinical Model

Background: Chronic obstructive pulmonary disease (COPD) is currently listed as the 4th leading cause of death and is projected to be the 3rd leading cause of death by 2020. Cigarette smoking is considered to be the leading cause of COPD in the developed world; however, with the emerging popularity of electronic cigarettes (e-cigarettes), the impact of e-cigarette vapor on the development of COPD requires further attention. COPD is characterized by limitations in expiratory airflow, emphysematous destruction of the lungs, bronchitis, and chronic inflammation of the lung tissue. Most of the e-cigarettes are introduced as a healthier tool to help people to quit the traditional cigarette. This is a device that effectively transports evaporated liquid nicotine to the lungs. Users can choose the nicotine concentration of the e-cigarette liquid (e-liquid) that loaded into the device\u27s cartridge. When inhaled, the e-liquids (nicotine) is heated to produce vapor that enters the lungs. Hypothesis: We hypothesized that nicotine-containing e-cigarettes induce the exacerbation of COPD in a pre-clinical model. Methods: Twenty Scnn1b-Tg+ mice were exposed to nicotine-containing e-cigarette vapor for ten days, each mice was exposed two hours per day. This transgenic animal model exhibits mucus hypersecretion and defective mucus clearance in the lung closely mimicking human COPD onset and progression. After treatment, bronchoalveolar lavage (BAL) fluid, lung tissues, and serum were collected to assess for inflammation, fibrosis, and mucus accumulation. Results: Inflammatory cytokines such as CXCL1, MMP-2, and CX3CL1 concentrations were significantly higher in animals exposed to e-cigarette vapor. Moreover, e-cigarette exposure increased mucus accumulation and fibrosis production in the bronchioles of the Scnn1b-Tg+ mice. Conclusion: These results suggest that nicotine-containing e-cigarette vapor induce the exacerbation of COPD in our animal model. Future studies will focus on chronic exposure of e-cigarette vapor to evaluate the mechanism of nicotine-containing e-cigarettes on the pathogenesis of COPD

Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation

Most adaptive neural control schemes are based on stochastic gradient-descent backpropagation (SGBP), which suffers from local minima problem. Although the recently proposed regularized online sequential-extreme learning machine (ReOS-ELM) can overcome this issue, it requires a batch of representative initial training data to construct a base model before online learning. The initial data is usually difficult to collect in adaptive control applications. Therefore, this paper proposes an improved version of ReOS-ELM, entitled fully online sequential-extreme learning machine (FOS-ELM). While retaining the advantages of ReOS-ELM, FOS-ELM discards the initial training phase, and hence becomes suitable for adaptive control applications. To demonstrate its effectiveness, FOS-ELM was applied to the adaptive control of engine air-fuel ratio based on a simulated engine model. Besides, controller parameters were also analyzed, in which it is found that large hidden node number with small regularization parameter leads to the best performance. A comparison among FOS-ELM and SGBP was also conducted. The result indicates that FOS-ELM achieves better tracking and convergence performance than SGBP, since FOS-ELM tends to learn the unknown engine model globally whereas SGBP tends to “forget” what it has learnt. This implies that FOS-ELM is more preferable for adaptive control applications

Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (ELM), to build a backup air-ratio model. With the prediction from the model, a limited air-ratio control performance can be maintained even when the lambda sensor does not work. Such strategy is realized as fault tolerance control. In order to verify the effectiveness of the proposed fault tolerance air-ratio control strategy, a model predictive control scheme is constructed based on the kernel ELM backup air-ratio model and implemented on a real engine. Experimental results show that the proposed controller can regulate the air-ratio to specific target values within a satisfactory tolerance under external disturbance and the absence of air-ratio feedback signal from the lambda sensor. This implies that the proposed fault tolerance air-ratio control is a promising scheme to maintain air-ratio control performance when the lambda sensor is under failure or warming up