With the development of flexible electronics, flexible microheaters have been applied in many areas. Low power consumption and fast response microheaters have attracted much attention. In this work, systematic thermal and mechanical analyses were conducted for a kind of flexible microheater with two different wire structures. The microheater consisted of polyethylene terephthalate (PET) substrate and copper electric wire with graphene thin film as the middle layer. The steady-state average temperature and heating efficiency for the two structures were compared and it was shown that the S-shaped wire structure was better for voltage-controlled microheater other than circular-shaped structure. In addition, the maximum thermal stress for both structures was from the boundary of microheaters, which indicated that not only the wire structure but also the shape of micro heaters should be considered to reduce the damage caused by thermal stress. The influence resulting from the thickness of graphene thin film also has been discussed. In all, the heating efficiency for flexible microheaters can be up to 135 °C/W. With the proposed PID voltage control system, the response time for the designed microheater was less than 10 s. Moreover, a feasible fabrication process flow for these proposed structures combing thermal analysis results in this work can provide some clues for flexible microheaters design and fabrication in other application areas.

Music is an archaic form of emotional expression and arousal that can induce strong emotional experiences in listeners, which has important research and practical value in related fields such as emotion regulation. Among the various emotion recognition methods, the music-evoked emotion recognition method utilizing EEG signals provides real-time and direct brain response data, playing a crucial role in elucidating the neural mechanisms underlying music-induced emotions. Artificial intelligence technology has greatly facilitated the research on the recognition of music-evoked EEG emotions. AI algorithms have ushered in a new era for the extraction of characteristic frequency signals and the identification of novel feature signals. The robust computational capabilities of AI have provided fresh perspectives for the development of innovative quantitative models of emotions, tailored to various emotion recognition paradigms. The discourse surrounding AI algorithms in the context of emotional classification models is gaining momentum, with their applications in music therapy, neuroscience, and social activities increasingly coming under the spotlight. Through an in-depth analysis of the complete process of emotion recognition induced by music through electroencephalography (EEG) signals, we have systematically elucidated the influence of AI on pertinent research issues. This analysis offers a trove of innovative approaches that could pave the way for future research endeavors.

Global energy issues have spurred the development of energy storage technology, and gravity-based energy storage (GBES) technology has attracted much attention. This comprehensive review examines the principles, applications, and prospects of GBES technology, a promising solution for mitigating the intermittency of renewable energy sources and enhancing grid stability. GBES harnesses potential energy by elevating solid or liquid mediums, offering distinct advantages over other energy storage technologies such as pumped hydro storage and batteries. The study examines various GBES configurations, emphasizing the importance of system design, control strategies, and efficiency. This review also evaluates the economic, environmental, and social benefits of GBES, emphasizing its cost-effectiveness and potential for local economic growth. The need for policy support, technological innovation, and a robust regulatory framework is highlighted to promote the widespread adoption of GBES, which holds significant potential for enhancing grid stability and supporting the integration of renewable energy.

As a powerful tool in scientific research and industrial technologies, the cold atom absolute gravity sensor (CAGS) based on cold atom interferometry has been proven to be the most promising new generation high-precision absolute gravity sensor. However, large size, heavy weight, and high–power consumption are still the main restriction factors of CAGS being applied for practical applications on mobile platforms. Combined with cold atom chips, it is possible to drastically reduce the complexity, weight, and size of CAGS. In this review, we started from the basic theory of atom chips to chart a clear development path to related technologies. Several related technologies including micro-magnetic traps, micro magneto–optical traps, material selection, fabrication, and packaging methods have been discussed. This review gives an overview of the current developments in a variety of cold atom chips, and some actual CAGS systems based on atom chips are also discussed. We summarize by listing some of the challenges and possible directions for further development in this area.

Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram (EEG) devices, we developed a fast and accurate sleep stage classification method in this study with single-channel EEG signals for practical applications. The original sleep recordings were collected from the Sleep-EDF database. The wavelet threshold denoising (WTD) method and wavelet packet transformation (WPT) method were applied as signal preprocessing to extract six kinds of characteristic waves. With a comprehensive feature system including time, frequency, and nonlinear dynamics, we obtained the sleep stage classification results with different Support Vector Machine (SVM) models. We proposed a novel classification method based on cascaded SVM models with various features extracted from denoised EEG signals. To enhance the accuracy and generalization performance of this method, nonlinear dynamics features were taken into consideration. With nonlinear dynamics features included, the average classification accuracy was up to 88.11% using this method. In addition, with cascaded SVM models, the classification accuracy of the non-rapid eye movement sleep stage 1 (N1) was enhanced from 41.5% to 55.65% compared with the single SVM model, and the overall classification time for each epoch was less than 1.7 s. Moreover, we demonstrated that it was possible to apply this method for long-term sleep stage monitor applications.

Ferrite materials are used as magnetic shielding to reduce ultralow magnetic field noise and interference in ultrahigh precision magnetic measurement instruments, including atomic magnetometers. However, there was a shielding coupling between the inner and outer layers of magnetic shields, which affected the shielding effect of the magnetic shields. This study analyzed the magnetic field noise coupling between magnetic shields and measured the residual magnetic field. Then, after demagnetizing and compensating the coil to remove the residual magnetic field, a spin-exchange relaxation-free (SERF) magnetometer was used to study the coupling magnetic field noise on ferrite. A thermal magnetic noise of $210\,\,f^{-1/2}\,\,\cdot $ fT had a significant impact on the magnetic field noise of the Mn–Zn ferrite shield. The sensitivity decreased from 0.737 to 9.223 fT/Hz $^{1/2}$ at 30 Hz, and the magnetic field noise raised to $70.05\,\,f^{-1/2}\,\,\cdot $ fT. The study indicated that the secondary outer layer has a significant impact on the coupling magnetic field noise and magnetic shielding performance of ferrite, while the impact of thermal magnetic noise on the magnetic field noise of ferrite is related to frequency and bandwidth. The research provides a reference for the application of ferrite magnetic shields in atomic magnetometers and the development of shielding devices with wide-frequency electromagnetic shields.