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Lightweight and flexible energy storage devices are urgently needed to persistently power wearable devices, and lithium-sulfur batteries are promising technologies due to their low mass densities and high theoretical capacities. Here we report a flexible and high-energy lithium-sulfur full battery device with only 100% oversized lithium, enabled by rationally designed copper-coated and nickel-coated carbon fabrics as excellent hosts for lithium and sulfur, respectively. These metallic carbon fabrics endow mechanical flexibility, reduce local current density of the electrodes, and, more importantly, significantly stabilize the electrode materials to reach remarkable Coulombic efficiency of >99.89% for a lithium anode and >99.82% for a sulfur cathode over 400 half-cell charge-discharge cycles. Consequently, the assembled lithium-sulfur full battery provides high areal capacity (3 mA h cm −2 ), high cell energy density (288 W h kg −1 and 360 W h L −1 ), excellent cycling stability (260 cycles), and remarkable bending stability at a small radius of curvature (<1 mm). Lightweight and flexible energy storage devices are needed to persistently power wearable devices. Here the authors employ metallized carbon fabrics as hosts for sulfur and lithium to achieve flexibility, electrochemical stability and high energy density in a lithium-sulfur battery.

A washer is a common structural element that is directly used along the loading path of a bolted connection. Pre-load on a bolted connection directly impacts its load bearing capacity and pre-load monitoring is an important aspect of structural health monitoring (SHM). With the change of the pre-load on a bolted connection, the loading force on the washer will change and, therefore, the outer diameter and outer circumferential length of the washer will change. Taking advantage of the high sensitivity and the small size of a Fiber Bragg Grating (FBG) sensor, we propose an innovative smart washer encircled by an FBG sensor that can directly measure the circumferential strain change and, therefore, the pre-load on the washer. For protection, the FBG is embedded in a pre-machined groove along the circumferential surface of the washer. A theoretical approach is used to derive the linear relationship between the applied load and the circumferential strain of the washer. To validate the functionality of the FBG-enabled smart sensor for in situ bolt pre-load monitoring, a simple but effective testing apparatus is designed and fabricated. The apparatus involves a bolt, the FBG-enabled washer, a metal plate, and a nut. The bolt has an embedded FBG along its axial direction for precise axial strain and, therefore, force measurement. With the calibrated axial force measuring bolt, in situ experiments on the FBG-enabled smart washers are conducted. Experimental results reveal the linear relationship between the pre-load and the wavelength of the FBG sensor encircling the washer. Both analytical and experimental results demonstrate that the proposed novel approach is sensitive to the bolt pre-load and can monitor in real time the bolt looseness in the entire loading range.

The Permanent Magnet Synchronous Motor (PMSM) is the power source maintaining the stable and efficient operation of various pieces of equipment; hence, its reliability is crucial to the safety of public equipment. Convolutional Neural Network (CNN) models face challenges in extracting features from PMSM current data. A new Discrete Wavelet Transform Convolutional Neural Networks (DW-CNN) feature with fusion weight updating Long Short-Term Memory (LSTM) anomaly detection is proposed in this paper. This approach combines Discrete Wavelet Transform (DWT) with high and low-frequency separation processing and LSTM. The anomaly detection method adopts DWT and CNN by separating high and low-frequency processing. Moreover, this method combines the hybrid attention mechanism to extract the multi-current signal features and detects anomalies based on weight updating the LSTM network. Experiments on the motor bearing real fault dataset and the PMSM stator fault dataset prove the method’s strong capability in fusing current features and detecting anomalies.

Ketamine was developed as an anesthetic. Esketamine is the isolated S-enantiomer of racemic ketamine. They provide new avenues for the treatment of depression, especially treatment-resistant depression. Considering differences in the pharmacokinetics and hormonal status of ketamine in patients of different genders, sex-based differences in esketamine adverse drug events (ADE) may also be observed. This study presents data mining and safety analysis of adverse events of ketamine and esketamine between genders, promoting the individualization of clinical practice. Adverse drug reactions to ketamine and esketamine reported between the first quarter of 2004 and the second quarter of 2023 in the U.S. Food and Drug Administration on Adverse Event Reporting System (FAERS) were extracted. Thereafter, the reporting odds ratio (ROR) with 95% confidence interval (CI) was calculated. A total of 2907 female reports and 1634 male reports on esketamine were included in the analysis. ROR mining showed that completed suicide, decreased therapeutic product effects, urinary retention, and hypertension were common in men. Additionally, 552 female and 653 male ketamine reports were recorded. ROR mining revealed that toxicity to various agents, bradycardia, cystitis and agitation, were more likely to occur in men, whereas women were more likely to develop suicidal ideation, increased transaminase levels, sclerosing cholangitis, and sterile pyuria. The adverse events of esketamine and ketamine differ across genders, which should be considered in clinical practice to provide individualized treatment.

Two-dimensional (2D) ferromagnetic materials with unique magnetic properties have great potential for next-generation spintronic devices with high flexibility, easy controllability, and high heretointegrability. However, realizing magnetic switching with low power consumption at room temperature is challenging. Here, we demonstrate the room-temperature spin-orbit torque (SOT) driven magnetization switching in an all-van der Waals (vdW) heterostructure using an optimized epitaxial growth approach. The topological insulator Bi 2 Te 3 not only raises the Curie temperature of Fe 3 GeTe 2 (FGT) through interfacial exchange coupling but also works as a spin current source allowing the FGT to switch at a low current density of ~2.2×10 6 A/cm 2 . The SOT efficiency is ~2.69, measured at room temperature. The temperature and thickness-dependent SOT efficiency prove that the larger SOT in our system mainly originates from the nontrivial topological origin of the heterostructure. Our experiments enable an all-vdW SOT structure and provides a solid foundation for the implementation of room-temperature all-vdW spintronic devices in the future. Magnetic random access memory (MRAM) exhibits remarkable device endurance, while also offering potential operation speed and energy efficiency improvements compared to conventional random access memory. However, challenges remain, both in terms of efficiency, and miniaturization. Here, Wang et al demonstrate a van der Waals (vdW) based spin-orbit torque switching, in a Fe 3 GeTe 2 /Bi2Te 3 heterostructure, paving the way for thinner and higher efficiency spin-orbit torque based vdW MRAM.

This paper has analyzed the defects of the traditional extended description function (EDF) based LLC small signal modeling method when driving light emitting diode (LED) load and proposed an accuracy improvement method. Detailed small signal model modeling methods have been deduced, and the accuracy of different modeling methods has been compared thoroughly. To suppress the second-order harmonic ripple in the direct current link-induced output current ripple, a quasi-resonant controller (QRC) is adopted to realize active ripple rejection (ARR). Considering the frequency of the second-order harmonic ripple changes with the grid voltage frequency, a single-phase software phase lock loop (SPLL) is adopted to extract the frequency of the second-order harmonic. By dynamically regulating the control parameter of the QRC according to the locked second-order frequency, output voltage ripple active rejection ability immune to grid voltage frequency deviation is obtained. Based on the deduced accurate small signal model, the digital controller is designed with stability, steady state and dynamics performance guaranteed. The accuracy of the proposed small signal model, the SPLL and the QRC-based ARR method has been verified at last.

LCL filters are extensively utilized in Grid-connected inverters due to their exceptional capability in suppressing high-frequency harmonics. The active damping method is commonly employed to mitigate the resonance peak of the LCL filter. However, this control strategy induces a shift in the natural resonance point. To address this issue, a novel active damping control strategy based on the principle of equivalent transformation is proposed in this paper, which not only effectively suppresses the resonance peak but also avoids deviation from the natural resonance point. Finally, experiments are carried out on a three-phase LCL Grid-connected inverter, and the experimental results show that the control strategy has good steady-state performance, dynamic response, and robustness under both rigid and ultra-weak network conditions.

There is a close relationship between Parkinson’s disease (PD) and circadian rhythm disturbances. To investigate genes linked to circadian rhythm disruption in PD, we analyzed the PD dataset via bioinformatic techniques. We downloaded 2 datasets from the GEO database; one dataset (GSE20163) was used as an internal training set, whereas the other dataset (GSE20164) was used as an external set. We first performed differential gene expression analysis on the GSE20163 dataset to identify differentially expressed genes (DEGs). We then conducted weighted gene co-expression network analysis on this dataset to select the most significant modules (turquoise modules) and took the intersection of the two to obtain crossover genes (85 genes), which were functionally enriched in the Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). These 85 crossover genes were again taken to intersect with circadian rhythm-related genes (2531) to form circadian rhythm-related genes (16). Box plot presentation was performed and found that all p -values were significant. Least absolute shrinkage and selection operator (LASSO) regression analysis was carried out to find 2 representative genes (CDK5, BSCL2). ROC curve analysis was conducted in both the cohort and the validation set, demonstrating statistically significant in each group. Gene set enrichment analysis (GSEA) was conducted on these 2 genes in the internal training set. Analysis of these two genes revealed that they are involved in the regulation of biological processes and synaptic signaling. The diagnostic and predictive model for Parkinson’s disease (PD), which relies on circadian-related genes (BSCL2 and CDK5), has excellent diagnostic and predictive performance. This model could serve as a valuable device in the diagnosis of circadian disturbance in PD. Finally, immunohistochemistry and immunoblotting-related experiments were performed for further confirmation. Immunoblot analysis revealed decreased protein levels of BSCL2 and CDK5 in the PD group. Immunohistochemistry further demonstrated reduced TH-positive neuron counts in this experimental cohort. Taking these findings together, BSCL2 and CDK5 are important genes associated with circadian rhythm disturbance in PD.

In this article, a cascade fuzzy neural network (FNN) control approach is proposed for position control of quadrotor unmanned aerial vehicle (UAV) system with high coupling and underactuated. For the attitude loop with limited range, the FNN controller parameters were trained offline using flight data, whereas for the position loop, the method based on FNN compensation proportional-integral-derivative (PID) was adopted to tune the system online adaptively. This method not only combined the advantages of fuzzy systems and neural networks but also reduced the amount of calculation for cascade neural network control. Simulations of fixed set point flight and spiral and square trajectory tracking flight were then conducted. The comparison of the results showed that our method had advantages in terms of minimizing overshoot and settling time. Finally, flight experiments were carried out on a DJI Tello quadrotor UAV. The experimental results showed that the proposed controller had good performance in position control.

Pre-stress degradation or looseness of rock bolts in mining or tunnel engineering threatens the stability and reliability of the structures. In this paper, an innovative piezoelectric device named a “smart washer” with the impedance method is proposed with the aim of developing a real-time device to monitor the pre-stress level of rock bolts. The proposed method was verified through tests on a rock bolt specimen. By applying high-frequency sweep excitations (typically >30 kHz) to the smart washer that was installed on the rock bolt specimen, we observed that the variation in impedance signatures indicated the rock bolt pre-stress status. With the degradation of rock bolt pre-stress, the frequency in the dominating peak of the real part of the electrical impedance signature increased. To quantify the effectiveness of the proposed technique, a normalized root mean square deviation (RMSD) index was developed to evaluate the degradation level of the rock bolt pre-stress. The experimental results demonstrated that the normalized RMSD-based looseness index, which was computed from the impedance value detected by the “smart washer”, increased with loss of the pre-stress of the rock bolt. Therefore, the proposed method can effectively detect the degradation of rock bolt pre-stress, as demonstrated by experiments.