In order to meet the guidance, publicity and commercial functions, various types of billboards have become important permanent facilities in the airport terminal, which are distributed all over the terminal. The advertising materials inside billboards have certain fire hazards, and there is a lack of research on the fire risk of advertising materials at present. Therefore, it is necessary to study the fire risk of advertising materials in airport terminal. Taking PVC board, a commonly used advertising material, as the research object, Pyrosim was used to model and analyze its fire, and the characteristics of fire spread, smoke flow, and distribution of combustion products such as CO and CO2 in the terminal building were obtained. This study explores the fire combustion characteristics of advertising materials in civil airport terminals, providing a basis for fire prevention management in civil airport terminals.

The fast-growing field of nanotheranostics is revolutionizing cancer treatment by allowing for precise diagnosis and targeted therapy at the cellular and molecular levels. These nanoscale platforms provide considerable benefits in oncology, including improved disease and therapy specificity, lower systemic toxicity, and real-time monitoring of therapeutic outcomes. However, nanoparticles' complicated interactions with biological systems, notably the immune system, present significant obstacles for clinical translation. While certain nanoparticles can elicit favorable anti-tumor immune responses, others cause immunotoxicity, including complement activation-related pseudoallergy (CARPA), cytokine storms, chronic inflammation, and organ damage. Traditional toxicity evaluation approaches are frequently time-consuming, expensive, and insufficient to capture these intricate nanoparticle-biological interactions. Artificial intelligence (AI) and machine learning (ML) have emerged as transformational solutions to these problems. This paper summarizes current achievements in nanotheranostics for cancer, delves into the causes of nanoparticle-induced immunotoxicity, and demonstrates how AI/ML may help anticipate and create safer nanoparticles. Integrating AI/ML with modern computational approaches allows for the detection of potentially dangerous nanoparticle qualities, guides the optimization of physicochemical features, and speeds up the development of immune-compatible nanotheranostics suited to individual patients. The combination of nanotechnology with AI/ML has the potential to completely realize the therapeutic promise of nanotheranostics while assuring patient safety in the age of precision medicine.

Business organizations use job advertisements to find and attract the high-quality workforce they need. Skillfully crafted job advertisements not only provide job-related information to job seekers but also help develop a strong employer brand in the employee market. Based on signaling theory and person-environment fit theory, we propose that the content and specificity of information provided in job advertisements influence job advertisement effectiveness through various mechanisms. In a scenario-based experiment on 310 young job seekers, we probed the direct and indirect effects of job advertisement informativeness on job pursuit intentions. Using structural equations modelling and multi-group path analysis, the mediating roles of perceived job appropriateness and ad truthfulness, along with the moderating role of previous employment experience, were examined. By manipulating the information content of a hypothetical job advertisement, we demonstrated that: a) both advertisement informativeness and perceived job appropriateness had positive direct effects on application intentions, while the latter had a greater effect; b) perceived job appropriateness mediated the relationship between advertisement informativeness and job pursuit intentions; c) the indirect (mediated) effect of advertisement informativeness on application intentions was moderated by previous employment experience; d) perceived ad truthfulness did not exert any significant effect on application intentions. These findings imply that HR practitioners should provide specific information in job postings to help candidates, especially those with less work experience, evaluate how well the job suits them and increase their motivation to apply.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.