In this work, different lanthanides (Tm3+, Er3+; Yb3+, Ho3+, Nd3+) were doped into NaYF4 via a high-temperature coprecipitation method, and followed by SiO2 coating to improve the water dispersibility, resulting in NaYF4:Tm3+, Er3+@NaYF4@SiO2 and NaYF4:Yb3+, Ho3+@NaYF4:Nd3+@SiO2 nanoparticles (NPs). The two NPs both exhibited the temperature-dependent second near-infrared (NIR-II) downshifting luminescence over the physiological range. The luminescence ratio of Tm3+ emission at 1460 nm to Er3+ emission at 1525 nm (Tm3+:3H4 → 3F4; Er3+:4I13/2 → 4I13/2) varies with temperature increase, as well as Yb3+ emission at 980 nm and Ho3+ emission at 1150 nm (Yb3+:2F5/2 → 2F7/2; Ho3+:5I6 → 5I8). The highest relative sensitivity of NaYF4:Tm3+, Er3+@NaYF4@SiO2 and NaYF4:Yb3+, Ho3+@NaYF4:Nd3+@SiO2 aqueous suspension is 0.36% K−1 (at 298 K) and 0.76% K−1 (at 343 K), respectively. The biological tests prove the good biocompatibility and low toxicity of the water-soluble NPs. In vitro tissue penetration experiments verify a much better penetration ability of the synthesized NaYF4:Tm3+, Er3+@NaYF4@SiO2 compared with NaYF4:Yb3+, Ho3+@NaYF4:Nd3+@SiO2 NPs. The excellent physiological luminescent thermometry with favor wave penetration depth provides a promising platform in deep tissue temperature measurement, which is very important in vivo biosensing.@en

As an emerging field of communication, Vehicular Social Networks (VSNs) can reduce traffic congestion while enhancing road safety by sharing data among groups of commuters. In VSNs, Vehicular Cloud Server (VCS) based data sharing technology with encrypted primitives allows local users to outsource encrypted data for reducing the storage burden on the user side and sharing data without location restrictions. However, existing data encryption solutions that have been applied in VSNs environments still encounter weaknesses in efficiency, security, or privacy due to the following problems: (1) lack of effective access policies for flexible authorizing ciphertext to multiple data users; (2) data breaches caused by malicious data publishers; (3) necessity in hiding the private information of receivers. To date, no such solution has been available that securely enables one-to-many user authorization with privacy protection, while greatly resisting malicious data publishers. We propose a Sanitizable Access Control System with Policy-protection (SASP) for VSNs in this paper. Our SASP enables a sanitizer to test and sanitize encrypted data to defend against malicious data publishers, ensuring that the plaintext can only be recovered if an authorized user has a valid key. Furthermore, in our SASP system, the access policy is separated into attribute names and attribute values. Wherein, the attribute values contain a lot of private information, which is hidden in the ciphertext to guarantee data users' privacy. Rigorous security analysis and performance evaluations demonstrate the practicality of SASP for VSNs.@en

The deep reinforcement learning-based energy management strategies (EMS) have become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are changed, the neural network will be retrained, which is a time-consuming and laborious task. A more efficient way of choosing EMS is to combine deep reinforcement learning (DRL) with transfer learning, which can transfer knowledge of one domain to the other new domain, making the network of the new domain reach convergence values quickly. Different exploration methods of DRL, including adding action space noise and parameter space noise, are compared against each other in the transfer learning process in this work. Results indicate that the network added parameter space noise is more stable and faster convergent than the others. In conclusion, the best exploration method for transferable EMS is to add noise in the parameter space, while the combination of action space noise and parameter space noise generally performs poorly. Our code is available at https://github.com/BIT-XJY/RL-based-Transferable-EMS.git.@en