Personal thermal management (PTM) is a promising approach for maintaining the thermal comfort zone of the human body while minimizing the energy consumption of indoor buildings. Recent studies have reported the development of numerous advanced textiles that enable PTM systems to regulate body temperature and are comfortable to wear. Herein, recent advancements in thermoregulatory clothing for PTM are discussed. These advances in thermoregulatory clothing have focused on enhancing the control of heat dissipation between the skin and the localized environment. We primarily summarize research on advanced clothing that controls the heat dissipation pathways of the human body, such as radiation-and conductance-controlled clothing. Furthermore, adaptive clothing such as dual-mode textiles, which can regulate the microclimate of the human body, as well as responsive textiles that address both thermal performance (warming and/or cooling) and wearability are discussed. Finally, we include a discussion on significant challenges and perspectives in this field, including large-scale production, smart textiles, bioinspired clothing, and AI-assisted clothing. This comprehensive review aims to further the development of sustainably manufactured advanced clothing with superior thermal performance and outstanding wearability for PTM in practical applications.

Scalability and automation are two cornerstones for advanced manufacturing where laser-induced graphene (LIG) can play a key role. However, it is well known that LIG, employed as an electrode material for electrochemical storage devices, has a severely limited energy storage capability, thus presenting a major roadblock to mass commercial adoption. Herein, a technique based on the in situ electrodeposition of polypyrrole (PPy) onto three-dimensional (3D) porous LIG (LIG@PPy) is proposed for overcoming the inherent charge storage limit of LIG. For demonstration, zinc-ion hybrid microsupercapacitors (ZHMSCs) have been realized by integrating LIG@PPy cathodes with Zn anodes. As a key advantage, the conformal deposition of PPy with high specific capacity significantly enhances the charge storage capacity of the LIG skeleton and improves conductivity while maintaining the structural integrity of the porous structure to ensure fast charge diffusion kinetics. Further benefiting from the use of multivalent ions and asymmetrical electrodes, the prototyped ZHMSC exhibits a wide voltage window (1.7 V) and a remarkable areal capacitance/energy density (149 mF cm −2 /54 μWh cm −2 ), which represents a 200× enhancement from the pristine LIG counterpart. This work provides a simple strategy for unlocking the full energy storage potential of LIG without sacrificing any of its existing advantages, overcoming a major bottleneck that had plagued LIG as a practical cathode material for microsupercapacitors and other energy storage devices.

The dropout rate of massive open online courses (MOOC) has been significantly high, which makes its prediction an important problem. In this paper, we try to transfer the knowledge gained in the field of Natural Language Processing into the field of MOOC dropout prediction, due to the high similarity between them. More specifically, we attempt to study and show the powerful use of attention and conditional random field, both of which have been very popular architectures when solving NLP problems. A novel neural network structure is designed as the combination of these techniques. Extensive experimental results demonstrate that the proposed approach is effective.INDEX TERMS Deep learning, MOOC, Conditional random field.

Maintaining a reasonably stable body temperature is crucially vital for a variety of human activities in an energy-conservation strategy. However, it is well-known that metal-like materials, utilized as radiative reflectors...