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Asthma is the most common chronic respiratory disease and has been declared a global public health problem by the World Health Organization. Due to the high heterogeneity and complexity, asthma can be classified into different 'phenotypes' and it is still difficult to assess the phenotypes and stages of asthma by traditional methods. In recent years, mass spectrometry-based proteomics studies have made significant progress in sensitivity and accuracy of protein identification and quantitation, and are able to obtain differences in protein expression across samples, which provides new insights into the mechanisms and classification of asthma.In this article, we summarize research strategies in quantitative proteomics, including labeled, label-free and targeted quantification, and highlight the advantages and disadvantages of each. In addition, new applications of quantitative proteomics and the current status of research in asthma have also been discussed. In this study, online resources such as PubMed and Google Scholar were used for literature retrieval.The application of quantitative proteomics in asthma has an important role in identifying asthma subphenotypes, revealing potential pathogenesis and therapeutic targets. But the proteomic studies on asthma are not sufficient, as most of them are in the phase of biomarker discovery.

An immune algorithm and a technique of sliding mode variable structure control (VSC) are proposed to achieve real-time and accurate control of wheeled robot in view of uncertainties for time-varying, strong coupling and nonlinear characteristics of wheeled robots, coupled with change of load and influence of external disturbance in traditional control algorithm based on classical control theory. First, the VSC parameters are adjusted online by the immune algorithm, which overcomes limitation that the parameters of reaching law need to be set in advance in conventional VSC. Secondly, the algorithm not only retains advantages of the traditional reaching law, but also effectively improves control quality and eliminates chattering of the system. The experimental results show that the control technique makes the wheeled robot move on sliding surface in an ideal way, for main concern of this paper is to provide an effective systemic for control of wheeled robot.

Computer-integrated manufacturing is a notable feature of Industry 4.0. Integrating machine learning (ML) into edge intelligent Industrial Internet of Things (IIoT) is a key enabling technology to achieve intelligent IIoT. To realize novel intelligent applications of edge-enhanced IIoT, ML methods are proposed to improve the cognitive ability of edge intelligent IIoT in this article. First, an ML-enabled framework of the cognitive IIoT is proposed. Second, the ML methods are presented to enhance the cognitive ability of IIoT including the ML model of IIoT, data-driven learning and reasoning, and coordination with cognitive methods. Finally, with a focus on the reconfigurable production line, a scenario-aware dynamic adaptive planning (DAP) with deep reinforcement learning (DRL) was conducted. The experimental results show that the DRL-based dynamic adaptive planning (DRL-based DAP) had good performance in an observable IIoT environment. The main purpose of this work is to point out the effects of ML-based optimization methods on the analysis of industrial IoT from the macroscopic view.

Aiming at the problems of high energy consumption, complex wiring, high layout cost, limited use environment, and limited function of conventional plant lighting equipment such as fluorescent lamps, sodium lamps, etc., this paper develops a type of laser device for plant growth with nanometer lasers based on the design of an intelligent control system of an immune algorithm, constant current driving circuit of the laser, pulse power supply, and rotatable intelligent platform to make the device more stable, reliable, practical, and energy efficient, and provides a useful reference for the innovation and application of materials, processes, and methods of plant lighting. The effects of nanometer laser light supplementation on the growth of purple lettuce, romaine lettuce, Chinese cabbage, and you-mai vegetable have been studied with the vegetables mentioned above as experimental materials and with natural light as the control sample. The results show that the nanometer laser device significantly increases stem height, stem thickness, leaf area, leaf number, and chlorophyll content, effectively promotes plant growth, and achieves efficient cultivation. In the future, studies of the effects of laser treatment on plant physiology and biochemistry will be sped up to explore the molecular biological mechanism of lasers to promote application and technological innovation of lasers in lighting for plant growth and the laser device in productivity.