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In recent years, deep eutectic systems (DES) emerged as novel vehicles for facilitating the transdermal delivery of various drugs, including polysaccharides, proteins, insulin, vaccine, nanoparticles, and herb extracts. The objective of this study is to conduct a comprehensive review of the application of DES to transdermal drug delivery, based on previous work and the reported references. Following a brief overview, the roles of DES in TDDS, the modes of action, as well as the structure–activity relationship of DES are discussed. Particularly, the skin permeation of active macromolecules and rigid nanoparticles, which are the defining characteristics of DES, are extensively discussed. The objective is to provide a comprehensive understanding of the current investigation and development of DES-based transdermal delivery systems, as well as a framework for the construction of novel DES-TDDS in the future. [ABSTRACT FROM AUTHOR]

Aiming to resolve the problem of redundant information concerning rolling bearing degradation characteristics and to tackle the difficulty faced by convolutional deep learning models in learning feature information in complex time series, a prediction model for remaining useful life based on multiscale fusion permutation entropy (MFPE) and a multiscale convolutional attention neural network (MACNN) is proposed. The original signal of the rolling bearing was extracted and decomposed by resonance sparse decomposition to obtain the high-resonance and low-resonance components. The multiscale permutation entropy of the low-resonance component was calculated. Moreover, the locally linear-embedding algorithm was used for dimensionality reduction to remove redundant information. The multiscale convolution module was constructed to learn the feature information at different time scales. The attention module was used to fuse the feature information and input it into the remaining useful life prediction module for evaluation. The appropriate network structure and parameter configuration were determined, and a multiscale convolutional attention neural network was designed to determine the remaining useful life prediction model. The results show that the method demonstrates effectiveness and superiority in degrading the feature information representation and improving the remaining useful life prediction accuracy compared with other models. [ABSTRACT FROM AUTHOR]

This study investigates the online spectating behavior of sports fans. Due to the great mobility and low opportunity/switching costs, webcast sports fans' spectating behaviors are distinct from those associated with traditional spectating channels such as stadium attendance or TV viewership. We explore the unique characteristics of online webcast demand in professional sports leagues by rigorously modeling all three spectating choices of sports fans. To consider the substitute relationship of the three spectating choices simultaneously, we employ a BLP (Berry–Levinsohn–Pakes)-style random coefficient model. For the analysis, we collect a comprehensive game-level dataset from the Korean Professional Baseball Organization (KBO) League fan samples from three different channels: online webcast viewership, stadium attendance, and TV viewership. We find that the demand for online webcasts is distinctive compared to that of traditional spectating channels. Notably, we find that the impact of team performance is three times stronger than that of TV viewership demand and that the impact of game quality is four times stronger than that of attendance demand. In contrast, a nonperformance variable is relatively less effective in attracting sports fans to online broadcasting. Furthermore, we find evidence of a strong retention effect of online webcast viewers. Our findings indicate that the previous spectating experience of online webcasts increases the next-time choice of sports fans for the webcast because the genuine spectating experience with distinctive webcast services (such as real-time interactive communication or various supplementary programs) can induce consumers to revisit the channel. [ABSTRACT FROM AUTHOR]

It was discovered that large-amplitude axial oscillation can occur on a rotor with an internally hydrogenated deformable part (HDP) in a rotation–translation nanoconverter. The dynamic outputs of the system were investigated using molecular dynamics simulations. When an input rotational frequency (100 GHz > ω > 20 GHz) was applied at one end of the rotor, the HDP deformed under the centrifugal and van der Waals forces, which simultaneously led to the axial translation of the other end of the rotor. Except at too high an input rotational frequency (e.g., >100 GHz), which led to eccentric rotation and even collapse of the system, the present system could generate a periodic axial oscillation with an amplitude above 0.5 nm at a temperature below 50 K. In other ranges of temperature and amplitude, the oscillation dampened quickly due to the drastic thermal vibrations of the atoms. Furthermore, the effects of the hydrogenation scheme and the length of HDP on the equilibrium position, amplitude, and frequency of oscillation were investigated. The conclusions can be applied to the design of an ideal nano-oscillator based on the present rotation–translation converter model. [ABSTRACT FROM AUTHOR]

It was discovered that a sudden jump of the output torque moment from a rotation transmission nanosystem made from carbon nanotubes (CNTs) occurred when decreasing the system temperature. In the nanosystem from coaxial-layout CNTs, the motor with specified rotational frequency (ωM) can drive the inner tube (rotor) to rotate in the outer tubes. When the axial gap between the motor and the rotor was fixed, the friction between their neighbor edges was stronger at a lower temperature. Especially at temperatures below 100 K, the friction-induced driving torque increases with ωM. When the rotor was subjected to an external resistant torque moment (Mr), it could not rotate opposite to the motor even if it deformed heavily. Combining molecular dynamics simulations with the bi-sectioning algorithm, the critical value of Mr was obtained. Under the critical torque moment, the rotor stopped rotating. Accordingly, a transmission nanosystem can be designed to provide a strong torque moment via interface friction at low temperature. [ABSTRACT FROM AUTHOR]