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Electrochemical nitrate reduction to ammonia is a promising alternative strategy to the traditional Haber-Bosch process but suffers from a low Faradaic efficiency and limited ammonia yield due to the sluggish multi-electron/proton-involved steps. Herein, we report a typical hollow cobalt phosphide nanosphere electrocatalyst assembled on a self-supported carbon nanosheet array synthesized with a confinement strategy that exhibits an extremely high ammonia yield rate of 8.47 mmol h −1 cm −2 through nitrate reduction reaction, which is highly superior to previously reported values to our knowledge. In situ experiments and theoretical investigations reveal that the dynamic equilibrium between the generation of active hydrogen on cobalt phosphide and its timely consumption by nitrogen intermediates leads to a superior ammonia yield with a high Faradaic efficiency. This unique insight based on active hydrogen equilibrium provides new opportunities for large-scale ammonia production through electrochemical techniques and can be further used for carbon dioxide capture. While electrochemical conversion of nitrate to ammonia offers a renewable means to remediate waste compounds, it is challenging to achieve selective catalysis. Here, authors demonstrate a strategy to improve electrocatalytic ammonia production using cobalt phosphide on carbon nanosheet arrays.

Previous studies have used the anaerobic threshold (AT) to non-invasively predict muscle fatigue. This study proposes a novel method for the automatic classification of muscle fatigue based on surface electromyography (sEMG). The sEMG data were acquired from 20 participants during an incremental test on a cycle ergometer using sEMG sensors placed on the vastus rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), and gastrocnemius (GA) muscles of the left leg. The ventilation volume (VE), oxygen uptake (VO2), and carbon dioxide production (VCO2) data of each participant were collected during the test. Then, we extracted the time-domain and frequency-domain features of the sEMG signal denoised by the improved wavelet packet threshold denoising algorithm. In this study, we propose a new muscle fatigue recognition model based on the long short-term memory (LSTM) network. The LSTM network was trained to classify muscle fatigue using sEMG signal features. The results showed that the improved wavelet packet threshold function has better performance in denoising sEMG signals than hard threshold and soft threshold functions. The classification performance of the muscle fatigue recognition model proposed in this paper is better than that of CNN (convolutional neural network), SVM (support vector machine), and the classification models proposed by other scholars. The best performance of the LSTM network was achieved with 70% training, 10% validation, and 20% testing rates. Generally, the proposed model can be used to monitor muscle fatigue.

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

The electrochemical oxygen reduction reaction (ORR) to produce hydrogen peroxide (H 2 O 2 ) is appealing due to its sustainability. However, its efficiency is compromised by the competing 4e − ORR pathway. In this work, we report a hierarchical carbon nanosheet array electrode with a single-atom Ni catalyst synthesized using organic molecule-intercalated layered double hydroxides as precursors. The electrode exhibits excellent 2e − ORR performance under alkaline conditions and achieves H 2 O 2 yield rates of 0.73 mol g cat −1 h −1 in the H-cell and 5.48 mol g cat −1 h −1 in the flow cell, outperforming most reported catalysts. The experimental results show that the Ni atoms selectively adsorb O 2 , while carbon nanosheets generate reactive hydrogen species, synergistically enhancing H 2 O 2 production. Furthermore, a coupling reaction system integrating the 2e − ORR with ethylene glycol oxidation significantly enhances H 2 O 2 yield rate to 7.30 mol g cat −1 h −1 while producing valuable glycolic acid. Moreover, we convert alkaline electrolyte containing H 2 O 2 directly into the downstream product sodium perborate to reduce the separation cost further. Techno-economic analysis validates the economic viability of this system. Electrochemical oxygen reduction is a promising method for H 2 O 2 production but suffers from poor activity and low yield. In this work, the authors present Ni single atom catalyst for efficient H 2 O 2 production which is further coupled with organic oxidation reaction to enhance the economic feasibility of the system.

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

Power line interference (PLI) is a major source of noise in sEMG signals. As the bandwidth of PLI overlaps with the sEMG signals, it can easily affect the interpretation of the signal. The processing methods used in the literature are mostly notch filtering and spectral interpolation. However, it is difficult for the former to reconcile the contradiction between completely filtering and avoiding signal distortion, while the latter performs poorly in the case of a time-varying PLI. To solve these, a novel synchrosqueezed-wavelet-transform (SWT)-based PLI filter is proposed. The local SWT was developed to reduce the computation cost while maintaining the frequency resolution. A ridge location method based on an adaptive threshold is presented. In addition, two ridge extraction methods (REMs) are proposed to fit different application requirements. Parameters were optimized before further study. Notch filtering, spectral interpolation, and the proposed filter were evaluated on the simulated signals and real signals. The output signal-to-noise ratio (SNR) ranges of the proposed filter with two different REMs are 18.53–24.57 and 18.57–26.92. Both the quantitative index and the time–frequency spectrum diagram show that the performance of the proposed filter is significantly better than that of the other filters.

Ice processes in open channels are inherently multi‐physics phenomena, characterized by strong coupling of hydrodynamics, heat transfer, and phase‐change dynamics. While many models have been proposed to investigate ice processes, previous studies are often limited by oversimplified representations of flow, presumptions on channel geometry, and inadequate treatment of thermal and phase‐change interactions. This paper presents a double layer‐averaged mathematical model for solving coupled hydro‐ice‐thermal dynamics in open channels with complex cross‐sectional geometries. The model explicitly resolves mass, momentum, and heat exchanges between an upper ice‐water mixture layer and a lower clear‐water layer, enabling simulation of phase‐change processes and temperature evolution. The model is benchmarked against laboratory and field data, demonstrating satisfactory agreement with observed ice growth, decay, thermal profiles, and stage hydrographs. Application to a water diversion canal illustrates the model's capability to predict coupled hydro‐thermal ice evolution and its response to varied environmental and operational conditions. This modeling framework offers a promising tool for simulating ice processes in open channels under varying hydraulic and thermal conditions.

The detection of the gravitational-wave (GW) event GW190521 raises questions about the formation of black holes (BHs) within the pair-instability mass gap (PIBHs). We propose that Population III star clusters significantly contribute to events similar to GW190521. We perform N-body simulations and find that PIBHs can form from stellar collisions or binary black hole (BBH) mergers, with the latter accounting for 90% of the contributions. Due to GW recoil during BBH mergers, approximately 10%–50% of PIBHs formed via BBH mergers that escaped from clusters, depending on BH spins and cluster escape velocities. The remaining PIBHs can participate in secondary and multiple BBH formation events, contributing to GW events. Assuming Population III stars form in massive clusters (initially 100,000 M⊙) with a top-heavy initial mass function, the average merger rates for GW events involving PIBHs with 0% and 100% primordial binaries are 0.005 and 0.017 yr−1 Gpc−3, respectively, with maximum values of 0.030 and 0.106 yr−1 Gpc−3. If Population III stars form in low-mass clusters (initial mass of 1000 M⊙ and 10,000 M⊙), the merger rate is comparable with a 100% primordial binary fraction but significantly lower without primordial binaries. We also calculate the characteristic strains of the GW events in our simulations and find that about 43.4% (LISA), 97.8% (Taiji), and 66.4% (Tianqin) of these events could potentially be detected by space-borne detectors, including LISA, Taiji, and TianQin. Next-generation GW detectors such as DECIGO, the Einstein Telescope, and Cosmic Explorer can nearly cover all these signals.

Lower limb isometric strength is crucial for predicting diseases, monitoring rehabilitation, and assessing activity levels. Manual testing lacks quantitative evaluation, while handheld dynamometers (HHDs) require skilled raters and isokinetic dynamometers are expensive and complex. Existing devices often focus on single-joint measurements for specific populations. To address the need for multi-joint quantitative muscle strength assessment, along with portability, affordability, and ease of use, this study developed the isometric dynamometer for the lower extremity (IDLE) to measure hip flexion, knee extension, knee flexion, and ankle dorsiflexion strength. Its validity and reliability were evaluated in 20 healthy adults (50% female). The IDLE demonstrated excellent validity compared to a strap-fixed HHD (Pearson’s r  ≥ 0.907, ICC ≥ 0.908, P  < 0.01). Intra-rater reliability was excellent (ICC ≥ 0.926) for male knee extension (bilateral), left knee flexion, and right ankle dorsiflexion; as well as for female right hip flexion, knee extension (bilateral), and right knee flexion, and good (ICC ≥ 0.808) for other measurements. Inter-rater reliability was excellent (ICC ≥ 0.901) for all measurements except male right ankle dorsiflexion. The IDLE is a valid and reliable device for measuring lower extremity isometric strength in healthy adults, with further validation in clinical populations recommended.

BACKGROUNDConsidering that trophoblast cell-surface antigen 2 (Trop2) is overexpressed in a wide range of human epithelial cancers, it presents an attractive target for diagnosis and treatment of multiple types of cancer. Herein, we have developed a Trop2-specific radiotracer, 68Ga-MY6349, and present a prospective, investigator-initiated trial to explore the clinical value of 68Ga-MY6349 PET/CT.METHODSIn this translational study, 90 patients with 15 types of cancer who underwent 68Ga-MY6349 PET/CT were enrolled prospectively. Among them, 78 patients underwent paired 68Ga-MY6349 and 18F-FDG PET/CT, and 12 patients with prostate cancer underwent paired 68Ga-MY6349 and 68Ga-PSMA-11 PET/CT.RESULTSAmong the 90 patients across 15 types of cancer, 68Ga-MY6349 uptake in tumors was generally high but heterogeneous, varying among lesions, patients, and cancer types. Trop2 expression level determined by immunohistochemistry was highly correlated with 68Ga-MY6349 uptake at primary and metastatic tumor sites. 68Ga-MY6349 PET/CT showed higher tumor uptake (quantified by maximum standardized uptake value) than 18F-FDG PET/CT in certain types of cancer, including breast (7.2 vs. 5.4, P < 0.001), prostate (9.2 vs. 3.0, P < 0.001), and thyroid cancers (8.5 vs. 3.7, P < 0.001). Compared with 68Ga-PSMA-11, 68Ga-MY6349 PET/CT exhibited comparable lesion uptake (12.2 vs. 12.5, P = 0.223) but a better tumor-to-background contrast (15.8 vs. 12.2, P < 0.001) for primary and metastatic prostate cancer, allowing visualization of more metastatic lesions.CONCLUSION68Ga-MY6349 PET/CT is a noninvasive method for comprehensively assessing Trop2 expression in tumors, which can improve diagnosis and staging for cancer patients and aid in decision making for Trop2-targeted therapies and advancing of personalized treatment.TRIAL REGISTRATIONClinicalTrials.gov NCT06188468.FUNDINGNational Natural Science Foundation of China, National Key R&D Program of China, Nuclear Energy R&D project, Fujian Research and Training Grants for Young and Middle-aged Leaders in Healthcare, Key Scientific Research Program for Young Scholars in Fujian, and Fujian Natural Science Foundation for Distinguished Young Scholars.