Research on Application of Cognitive-Driven Human-Computer Interaction

Human-computer interaction is an important research content of intelligent manufacturing human factor engineering. Natural human-computer interaction conforms to the cognition of users' habits and can efficiently process inaccurate information interaction, thus improving user experience and reducing cognitive load. Through the analysis of the information interaction process, user interaction experience cognition and human-computer interaction principles in the human-computer interaction system, a cognitive-driven human-computer interaction information transmission model is established. Investigate the main interaction modes in the current human-computer interaction system, and discuss its application status, technical requirements and problems. This paper discusses the analysis and evaluation methods of interaction modes in human-computer system from three levels of subjective evaluation, physiological measurement and mathematical method evaluation, so as to promote the understanding of inaccurate information to achieve the effect of interaction self-adaptation and guide the design and optimization of human-computer interaction system. According to the development status of human-computer interaction in intelligent environment, the research hotspots, problems and development trends of human-computer interaction are put forward

Hainan sport tourism development—A SWOT analysis

Hainan, as a popular tourism destination, is well-promoted by the Chinese central government. In particular, both central and local governments encourage Hainan’s sport tourism-related professionals to develop sport tourism as one of the most important tourist activities in Hainan. However, previous research has not reported on Hainan’s sport tourism strengths, weaknesses, opportunities, and threats as a tourism destination or a sports event host. This study uses SWOT analysis to identify the strengths, weaknesses, opportunities, and threats in the context of Hainan’s sport tourism development. A total of 12 dimensions, including branding, culture, finance, infrastructure, location, market, nature, policy, product, specialty, sustainability, and tourist were generated from our data analysis. In addition, a total of five future directions, including emphasizing event-oriented sport tourism, prioritizing sport motivation, identifying major sport tourism markets, making the rational use of sport tourism resources, and nurturing sport culture, are recommended as a result of this study

Clinical efficacy and drug resistance of ceftazidime-avibactam in the treatment of Carbapenem-resistant gram-negative bacilli infection

ObjectiveTo examine the clinical efficacy, safety, and resistance of Ceftazidime-Avibactam (CAZ-AVI) in patients with Carbapenem-resistant Gram-negative bacilli (CR-GNB) infections.MethodsWe retrospectively analyzed relevant data of CR-GNB infected patients receiving CAZ-AVI treatment, analyzed relevant factors affecting drug efficacy, and compared the efficacy and safety with patients receiving Polymyxin B treatment.ResultsA total of 139 patients were included. Agranulocytosis, septic shock, SOFA score, and CAZ-AVI treatment course were independent risk factors affecting the prognosis of patients with CR-GNB infection treated with CAZ-AVI while prolonging the treatment course of CAZ-AVI was the only protective factor for bacterial clearance. The fundamental indicators showed no statistically significant differences between CAZ-AVI and Polymyxin B treatment groups. At the same time, the proportion of patients treated with monotherapy was significantly higher in the CAZ-AVI group than in the Polymyxin B group (37.2% vs. 8.9%, p < 0.05), the 30-day mortality rate of the CAZ-AVI treatment group (27.7% vs. 46.7%, p = 0.027) was lower than that of the Polymyxin B treatment group. The 30-day clinical cure rate (59.6% vs. 40% p = 0.030) and 14-day microbiological clearance rate (42.6% vs. 24.4%, p = 0.038) were significantly higher in the CAZ-AVI than in the Polymyxin B treatment group. Eighty nine patients were monitored for CAZ-AVI resistance, and the total resistance rate was 14.6% (13/89). The resistance rates of Carbapenem-resistant Klebsiella pneumoniae (CRKP) and Carbapenem-resistant Pseudomonas aeruginosa (CRPA) to CAZ-AVI were 13.5 and 15.4%, respectively.ConclusionCAZ-AVI has shown high clinical efficacy and bacterial clearance in treating CR-GNB infections. Compared with Polymyxin B, CAZ-AVI significantly improved the outcome of mechanical ventilation in patients with septic shock, agranulocytosis, Intensive Care Unit (ICU) patients, bloodstream infection, and patients with SOFA score > 6, and had a lower incidence of adverse events. We monitored the emergence of CAZ-AVI resistance and should strengthen the monitoring of drug susceptibility in clinical practice and the rational selection of antibiotic regimens to delay the onset of resistance

Stemness Analysis Uncovers That The Peroxisome Proliferator-Activated Receptor Signaling Pathway Can Mediate Fatty Acid Homeostasis In Sorafenib-Resistant Hepatocellular Carcinoma Cells

Hepatocellular carcinoma (HCC) stem cells are regarded as an important part of individualized HCC treatment and sorafenib resistance. However, there is lacking systematic assessment of stem-like indices and associations with a response of sorafenib in HCC. Our study thus aimed to evaluate the status of tumor dedifferentiation for HCC and further identify the regulatory mechanisms under the condition of resistance to sorafenib. Datasets of HCC, including messenger RNAs (mRNAs) expression, somatic mutation, and clinical information were collected. The mRNA expression-based stemness index (mRNAsi), which can represent degrees of dedifferentiation of HCC samples, was calculated to predict drug response of sorafenib therapy and prognosis. Next, unsupervised cluster analysis was conducted to distinguish mRNAsi-based subgroups, and gene/geneset functional enrichment analysis was employed to identify key sorafenib resistance-related pathways. In addition, we analyzed and confirmed the regulation of key genes discovered in this study by combining other omics data. Finally, Luciferase reporter assays were performed to validate their regulation. Our study demonstrated that the stemness index obtained from transcriptomic is a promising biomarker to predict the response of sorafenib therapy and the prognosis in HCC. We revealed the peroxisome proliferator-activated receptor signaling pathway (the PPAR signaling pathway), related to fatty acid biosynthesis, that was a potential sorafenib resistance pathway that had not been reported before. By analyzing the core regulatory genes of the PPAR signaling pathway, we identified four candidate target genes, retinoid X receptor beta (RXRB), nuclear receptor subfamily 1 group H member 3 (NR1H3), cytochrome P450 family 8 subfamily B member 1 (CYP8B1) and stearoyl-CoA desaturase (SCD), as a signature to distinguish the response of sorafenib. We proposed and validated that the RXRB and NR1H3 could directly regulate NR1H3 and SCD, respectively. Our results suggest that the combined use of SCD inhibitors and sorafenib may be a promising therapeutic approach

Optimal Design of Virtual Reality Visualization Interface Based on Kansei Engineering Image Space Research

To effectively organize design elements in virtual reality (VR) scene design and provide evaluation methods for the design process, we built a user image space cognitive model. This involved perceptual engineering methods and optimization of the VR interface. First, we studied the coupling of user cognition and design features in the VR system via the Kansei Engineering (KE) method. The quantitative theory I and KE model regression analysis were used to analyze the design elements of the VR system’s human–computer interaction interface. Combined with the complex network method, we summarized the relationship between design features and analyzed the important design features that affect users’ perceptual imagery. Then, based on the characteristics of machine learning, we used a convolutional neural network (CNN) to predict and analyze the user’s perceptual imagery in the VR system, to provide assistance for the design optimization of the VR system design. Finally, we verified the validity and feasibility of the solution by combining it with the human–machine interface design of the VR system. We conducted a feasibility analysis of the KE model, in which the similarity between the multivariate regression analysis of the VR intention space and the experimental test was approximately 97% and the error was very small; thus, the VR intention space model was well correlated. The Mean Square Error (MSE) of the convolutional neural network (CNN) prediction model was calculated with a measured value of 0.0074, and the MSE value was less than 0.01. The results show that this method can improve the effectiveness and feasibility of the design scheme. Designers use important design feature elements to assist in VR system optimization design and use CNN machine learning methods to predict user image values in VR systems and improve the design efficiency. Facing the same design task requirements in VR system interfaces, the traditional design scheme was compared with the scheme optimized by this method. The results showed that the design scheme optimized by this method better fits the user’s perceptual imagery index, and thus the user’s task operation experience was better

Research on Optimization Method of VR Task Scenario Resources Driven by User Cognitive Needs

Research was performed in order to improve the efficiency of a user’s access to information and the interactive experience of task selection in a virtual reality (VR) system, reduce the level of a user’s cognitive load, and improve the efficiency of designers in building a VR system. On the basis of user behavior cognition-system resource mapping, a task scenario resource optimization method for VR system based on quality function deployment-convolution neural network (QFD-CNN) was proposed. Firstly, under the guidance of user behavior cognition, the characteristics of multi-channel information resources in a VR system were analyzed, and the correlation matrix of the VR system scenario resource characteristics was constructed based on the design criteria of human–computer interaction, cognition, and low-load demand. Secondly, analytic hierarchy process (AHP)-QFD combined with evaluation matrix is used to output the priority ranking of VR system resource characteristics. Then, the VR system task scenario cognitive load experiment is carried out on users, and the CNN input set and output set data are collected through the experiment, in order to build a CNN system and predict the user cognitive load and satisfaction in the human–computer interaction in the VR system. Finally, combined with the task information interface of a VR system in a smart city, the application research of the system resource feature optimization method under multi-channel cognition is carried out. The results show that the test coefficient CR value of the AHP-QFD model based on cognitive load is less than 0.1, and the MSE of CNN prediction model network is 0.004247, which proves the effectiveness of this model. According to the requirements of the same design task in a VR system, by comparing the scheme formed by the traditional design process with the scheme optimized by the method in this paper, the results show that the user has a lower cognitive load and better task operation experience when interacting with the latter scheme, so the optimization method studied in this paper can provide a reference for the system construction of virtual reality

Research on the Prediction of A-Share “High Stock Dividend” Phenomenon—A Feature Adaptive Improved Multi-Layers Ensemble Model

Since the “high stock dividend” of A-share companies in China often leads to the short-term stock price increase, this phenomenon’s prediction has been widely concerned by academia and industry. In this study, a new multi-layer stacking ensemble algorithm is proposed. Unlike the classic stacking ensemble algorithm that focused on the differentiation of base models, this paper used the equal weight comprehensive feature evaluation method to select features before predicting the base model and used a genetic algorithm to match the optimal feature subset for each base model. After the base model’s output prediction, the LightGBM (LGB) model was added to the algorithm as a secondary information extraction layer. Finally, the algorithm inputs the extracted information into the Logistic Regression (LR) model to complete the prediction of the “high stock dividend” phenomenon. Using the A-share market data from 2010 to 2019 for simulation and evaluation, the proposed model improves the AUC (Area Under Curve) and F1 score by 0.173 and 0.303, respectively, compared to the baseline model. The prediction results shed light on event-driven investment strategies

Ku-band 500W Amplitude-phase Coherence Pulsed MPM for Ocean Exploration

The spatial high-resolution synthetic aperture radar (SAR) for ocean exploration puts forward higher requirement for output power and amplitude-phase consistency of the pulsed TWT amplifier. The kW class pulsed TWT amplifier based on broadband power combining is the key technology for space SAR applications. In this paper, the factors affecting the amplitude and phase consistency of MPM are studied, and a phase consistency compensation circuit was designed to improve the phase consistency of multiple sets of products. A microwave power model (MPM) with dual TWTs output integrated architecture was proposed which consists of two short travelling wave tubes (TWT), two solid-state power amplifiers (SSPA), and an electronic power conditioner (EPC). More than 45% efficiency is achieved in 15% duty cycle within 600 MHz bandwidth, and the phase consistency was less than ± 5 degrees.With the improvement of synthesis efficiency, higher synthesis power and higher sensitivity of ocean wide scanning and detection can be obtaine