Patients' response during the co-circulation of multiple respiratory diseases in China-based on the self-regulation common-sense model.

Background: Following the COVID-19 pandemic, another large-scale respiratory epidemic has emerged in China, causing significant social impact and disruption. The article is to explore the patients' psychological and behavioral responses to the enhancement of healthcare quality. Methods: Based on the five dimensions of the Self-Regulation Common-Sense Model, we developed an interview outline to explore the process by which patients identify disease symptoms to guide action plans and coping strategies. The researchers used a semi-structured interview format to simultaneously collect data online and offline. This study gathered data from 12 patients with mixed respiratory infections, comprising 58% females and 42% males; the average age was 30.67 years (SD 20.00), with 91.7% infected with two pathogens and 8.3% with three. The data analysis employed the KJ method, themes were inductively analyzed and categorized from semi-structured interview results, which were then organized into a coherent visual and logical pathway. Key results: The study identified 5 themes: (1) Autonomous Actions Prior to Seeking Medical Care; (2) Decision-Making in Seeking Hospital Care; (3) Disease Shock; (4) Public Crisis Response; (5) Information Cocoon. Conclusion: The pandemic of respiratory infectious diseases has not ceased in recent years. Following the COVID-19 pandemic, China is now facing a trend of concurrent epidemics involving multiple respiratory pathogens. This study centers on patients' health behaviors, exploring the potential relationships among various factors that affect these behaviors. The aim is to provide references and grounds for the improvement of healthcare services when such public health events reoccur.

Application and integration of deep learning in FAIMS for identifying acetone concentration.

In practical applications, analytical instruments are used for both qualitative and quantitative analysis. However, for high-field asymmetric-waveform ion mobility spectrometry (FAIMS), most studies to date have been focused on the qualitative analysis of substances, with limited research on quantitative analysis. Explored here is the feasibility of using deep learning in FAIMS for quantitative analysis, aided by redesigning the FAIMS upper computer. Integrating spectrum creation and deep learning analysis into the FAIMS upper computer boosts the processing and analysis of FAIMS data, laying a foundation for applying FAIMS practically. For analysis using image processing, multiple FAIMS spectral lines obtained under different conditions are converted into a three-dimensional thermodynamic map known as a FAIMS spectrum, and multiple FAIMS spectrum are preprocessed to obtain the data set of this experiment. The principles of partial-least-squares regression and the XGBoost and ResNeXt models are introduced in detail, and the data are analyzed using these models, while exploring the effects of different model parameters and determining their optimal values. The experimental results show that the pre-trained ResNeXt deep learning model performs the best on the test set, with a root mean square error of 0.86 mg/mL, indicating the potential of deep learning in realizing quantitative analysis of substances in FAIMS.

The Influence of College Students' Innovation and Entrepreneurship Intention in the Art Field of Art Film and Television Appreciation by Deep Learning Under Entrepreneurial Psychology.

There are many films and televisions (FATs) on the Internet, but the quality is uneven. This study explores the ability of college students to screen good films and resist bad films in television works in such a large environment. In the deep learning model of FAT, the ability of college students to think about the ideas expressed and the degree of influence on college students' values are analyzed. Based on this conceptual basis, a questionnaire is designed for the intention and influencing factors of college students' FAT innovation and entrepreneurship. It reflects the influence of concentration on FAT learning, the cognitive level of deep learning, the ability to process deep learning ideas, the feeling of the teaching process, and the process of self-learning, which all positively impact college students' FAT entrepreneurial intentions. The importance of innovative deep learning is highlighted, which proves that a good deep learning course guidance method can improve students' interest and ability and provide a reference for relevant colleges and universities to cultivate pertinent talents of the field of FAT.