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Under purely inorganic conditions, a synthesis route was devised wherein elements were introduced stepwise via coprecipitation based on differences in compound solubility. This synthesis method can change the microscopic morphology of the material without relying on a templating agent, resulting in the formation of the multilayered lamellar Ce/Eu codoped zinc oxide solid solution (ZCEOSS) with a self-assembled nested imbrication structure. The study improves the critical matter of corrosion by focusing on the electron and energy transfer mechanisms. By introduction of the bandgap modulator cerium element and fluorescence enhancer europium element into the ZnO material, the anticorrosion material has been successfully endowed with both photocathodic protection and luminescent initiative/stress dual corrosion defense functions. Due to the energy level staircase protection mechanism synergistically generated by the 4f electron shell of rare-earth elements in concert with semiconductor zinc oxide, the energy band positions were modulated to progressively guide the direction of electron flow, thereby suppressing corrosion reactions. In particular, the ZCEOSS material synthesized by doping 1% cerium and 7% europium and adding rare-earth elements at pH 7 exhibited the best corrosion inhibition performance. After immersion in simulated seawater for 96 h, Tafel polarization test results showed that compared to epoxy resin and ZnO anticorrosion systems, the ZCEOSS anticorrosion system exhibited significantly improved corrosion inhibition efficiency with enhancements of 1028.3 and 402.9%, respectively. This study provides new insights into the development of highly efficient inorganic anticorrosion materials.
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Room temperature vulcanised (RTV) silicone rubber coatings effectively enhance the insulation properties of electrical equipment. However, RTV coatings are prone to internal defects caused by the coating process and the effects of aging during service, which can lead to debonding of the coatings. Internal debonding defects are challenging to detect and can ultimately lead to accidents due to a reduction in the insulation capacity of the equipment. To visualize the internal defect morphology of RTV coatings and quantify the defect size, an ultrasonic pulse-echo-based method for detecting and imaging debonding defects is proposed. The method involves the development of a finite element model to investigate how ultrasonic waves propagate in RTV coatings and the influence of ultrasonic probes and inspection conditions on defect echoes. Furthermore, an ultrasonic detection system specifically designed for RTV coating debonding defects is constructed. This system utilizes wavelet packets in the time-frequency domain to analyze the echo signals in both normal and defective regions. The three-dimensional reconstruction of the debonding defect morphology is accomplished by integrating ultrasonic echo amplitude and position information. Finally, the size of the debonding defects is quantified using an adaptive threshold segmentation method. The findings indicate that ultrasound waves reflected in RTV materials propagate as spherical waves, with the acoustic energy primarily concentrated near the acoustic axis. As the propagation distance increases, the sound beam disperses along the axis and extends beyond the transducer, resulting in a decrease in the sound field's directionality. The developed visual reconstruction method in this study offers the capability of three-dimensional visualization for defects present within RTV coatings, including their length, width, and depth. The accurate determination of defect size is achieved through the utilization of the adaptive threshold segmentation method, yielding an average error rate of 5.7 % across different defect types. In comparison, the maximal interclass variance method (OTSU) and the fuzzy C-means (FCM) method produced results with error rates of 9.8 % and 7.9 %, respectively. The research presented in this paper enables precise assessment of debonding defect severity and establishes a reliable foundation for on-site inspection, operation, and maintenance of RTV coatings.
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As one of the important psychological stress reactions, Micro-expressions (MEs) are spontaneous and subtle facial movements, which usually occur in a high-stake situation and can reveal genuine human feelings and cognition. ME, Recognition (MER) has essential applications in many fields such as lie detection, criminal investigation, and psychological healing. However, due to the challenges of learning discriminative ME features via fleeting facial subtle reactions as well as the shortage of available MEs data, this research topic is still far from well-studied. To this end, in this paper, we propose a deep prototypical learning framework, namely ME-PLAN, with a local attention mechanism for the MER problem. Specifically, ME-PLAN consists of two components, i.e., a 3D residual prototypical network and a local-wise attention module, where the former aims to learn the precise ME feature prototypes through expression-related knowledge transfer and episodic training, and the latter could facilitate the attention to the local facial movements. Furthermore, to alleviate the dilemma that most MER methods need to depend on manually annotated apex frames, we propose an apex frame spotting method with Unimodal Pattern Constrained (UPC) and further extract ME key-frames sequences based on the detected apex frames to train our proposed ME-PLAN in an end-to-end manner. Finally, through extensive experiments and interpretable analysis regarding the apex frame spotting and MER on composite-database, we demonstrate the superiority and effectiveness of the proposed methods.
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Cara , Reconocimiento en Psicología , Bases de Datos Factuales , Emociones , HumanosRESUMEN
The detection of attentive mental state plays an essential role in the neurofeedback process and the treatment of Attention Deficit and Hyperactivity Disorder (ADHD). However, the performance of the detection methods is still not satisfactory. One of the challenges is to find a proper representation for the electroencephalogram (EEG) data, which could preserve the temporal information and maintain the spatial topological characteristics. Inspired by the deep learning (DL) methods in the research of brain-computer interface (BCI) field, a 3D representation of EEG signal was introduced into attention detection task, and a 3D convolutional neural network model with cascade and parallel convolution operations was proposed. The model utilized three cascade blocks, each consisting of two parallel 3D convolution branches, to simultaneously extract the multi-scale features. Evaluated on a public dataset containing twenty-six subjects, the proposed model achieved better performance compared with the baseline methods under the intra-subject, inter-subject and subject-adaptive classification scenarios. This study demonstrated the promising potential of the 3D CNN model for detecting attentive mental state.
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Algoritmos , Interfaces Cerebro-Computador , Electroencefalografía , Humanos , Redes Neurales de la ComputaciónRESUMEN
OBJECTIVES: Coronavirus disease 2019 (COVID-19) is a global pandemic. Critically ill patients often require prolonged intubation for mechanical ventilation to support breathing; thus, the artificial airway must be managed by tracheotomy. Therefore, studies exploring appropriate and safe methods for tracheotomy that minimize the risks of nosocomial transmission are important. METHODS: A retrospective analysis of the clinical characteristics of 14 critically ill patients with COVID-19, who underwent bedside tracheotomy from March to April 2020 was conducted to summarize the indications for tracheotomy and key points related to personal protective equipment and surgical procedures. RESULTS: All 14 patients were diagnosed with COVID-19 and were critically ill. All tracheotomies were performed in the late phase of the infection course. The interval between the infection and tracheotomy was 33 days, and the median interval between intubation and tracheotomy was 25.5 days. The reverse transcription-polymerase chain reaction results of secretions from the operative incision and inside the tracheotomy tube were negative. Twelve patients improved after tracheotomy, with SpO2 levels maintained above 96%. One patient died of progressive respiratory failure; another patient died of uncontrolled septic shock. No medical staff who participated in the tracheotomy was infected. CONCLUSIONS: Tracheotomy in critically ill patients with COVID-19 who meet the indications for tracheotomy potentially represents a safer approach to manage the airway and help improve the treatment outcomes. A tracheotomy performed in the late phase of the disease has a relatively low risk of infection. Adherence to key steps in the tracheotomy procedure and donning adequate personal protection will help medical staff avoid infection.