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This study aims to evaluate the thermal performance of sodium alginate (SA) aerogel attached to nano SiO₂ and its radiative cooling effect on firefighting clothing without environmental pollution.

SA/SiO₂ aerogel with refractory heat insulation and enhanced radiative cooling performance was fabricated by freeze-drying method, which can be used in firefighting clothing. The microstructure, chemical composition, thermal stability, and thermal emissivity were analyzed using Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analyzer and infrared emissivity measurement instrument. The radiative cooling effect of aerogel was studied using thermal infrared imager and thermocouple.

When the addition of SiO₂ is 25% of SA, the prepared aerogel has excellent heat insulation and a high radiative cooling effect. Under a clear sky, the temperature of SA/SiO₂ aerogel is 9.4°C lower than that of pure SA aerogel and 22.1°C lower than that of the simulated environment. In addition, aerogel has more exceptional heat insulation effect than other common fabrics in the heat insulation performance test.

SA/SiO₂ aerogel has passive radiative cooling function, which can efficaciously economize global energy, and it is paramount to environment-friendly cooling.

This method could pave the way for high-performance cooling materials designed for firefighting clothing to keep maintain the wearing comfort of firefighters.

SA/SiO₂ aerogel used in firefighting clothing can release heat to the low-temperature outer space in the form of thermal radiation to achieve its own cooling purpose, without additional energy supply.

Online open innovation platforms provide opportunities for product users to participate in the innovation process and contribute their ideas to the platform. Nonetheless, they also present a significant challenge for platform managers, who select high-quality innovations from a massive collection of information with diverse quality.

In this study, the authors employed a machine learning method to automatically collect a real dataset of 2,276 innovations and 30,004 detailed comments from the online platform of IdeaExchange and then conducted empirical experiments to verify the study hypothesis.

Results show that extraversion, conscientiousness and openness to experience positively and directly influenced the quality of their innovation. Furthermore, an individual's social network position mediated among extraversion, neuroticism, conscientiousness and openness to experience and the quality of an innovation.

Results showed that extraversion, conscientiousness and openness to experience positively and directly influenced the quality of their innovation. Furthermore, an individual's social network position mediated among extraversion, neuroticism, conscientiousness, openness to experience and the quality of innovations.

This study combined the Big Five personality traits theory and social network theory to examine the association between user intrinsic personality traits, social network position and the quality of their innovative ideas in the context of online innovation platforms. Additionally, the findings provide new insights for platform managers on how to select high-quality innovation information by considering user personality traits and their social network position.

Ensuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).

Drawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.

Three numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.

By employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.