Encapsulated Aerogel Fiber Mimicking the“Core–Shell”Structure of Polar Bear Hair for Thermal Insulation

Designing and large-scale production of woven aerogel fibers with superior thermal insulation and multifunctionality to meet human demand for warm textiles is a major challenge.In recent research,an encapsulated aerogel fiber that mimics the structure of polar bear hair has exhibited superior thermal insulation performance and mechanical strength.This innovation disrupts the current state of thermal insulation textiles,addresses the brittleness issue of aerogel fibers,and significantly enhances the processability of products.This study lays the groundwork for manufacturing efficient and sustainable thermal insulation textiles,which have immense potential in areas such as military attire and spacesuits in extreme cold environments.

Comparison of the Photo-thermal Energy Conversion Behavior of Polar Bear Hair and Wool of Sheep

The unique photo-thermal energy conversion property of polar bear hairs has long been regarded as an essential element to enable this creature to survive in extremely cold conditions. However, the relevant research was ineffectual to provide sufficient evidence of its solar energy harvesting property. In this paper, the properties of polar bear hairs were analyzed and compared systematically with those of domestic sheep wool through the measurements in the aspects of photo-thermal conversion effi- ciency, scanning electron microscope, fluorescence spectral and transmission of UV-visible spectra. Moreover, this study was much more focused on exploring ultraviolet utilization property of polar bear hair than previous research. The research results demonstrated that the photo-thermal property of polar bear hair was superior to those of wool fiber, especially in harvesting ultraviolet part. The potential benefits of this research lie in the development of bionic solar energy collective devices, especially in artificial solar energy collection fibers and textile products.

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced tools.The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security.The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms.Here,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting security.Then,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more effectively.The Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis function.Moreover,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security framework.The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.