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The capacity and cycling performance of cathodes are key factors in aqueous zinc batteries (AZBs). The search for cathode materials with long cycle lives and high specific capacities is of paramount importance. In this study, a bimetallic telluride with a hollow polyhedral structure was synthesized using a hydrothermal method followed by vapor deposition. This composite exhibits high conductivity, facilitates rapid diffusion of electrolyte ions into the interior, and accelerates redox reactions, thereby enhancing electrochemical performance. The CoTe2-NiTe2 electrode demonstrates an impressive specific capacity of 188.8 mAh/g at 1 A/g, highlighting its efficiency in storing a significant amount of charge per unit mass during electrochemical reactions. The assembled CoTe2-NiTe2//Zn battery shows favorable capacity retention (76.4%) after 10000 cycles. The energy density is remarkably high, reaching 290.3 Wh/kg, while maintaining a power density of 1.75 kW/kg. This bimetallic telluride strategy holds great promise as an alternative cathode for AZBs.

Abstract Flexible aqueous zinc batteries (FAZBs) with high safety and environmental friendliness are promising smart power sources for smart wearable electronics. However, the bare zinc anode usually suffers from damnable dendrite growth and rampant side reaction on the surface, greatly impeding practical applications in FAZBs. Herein, a composite polymer interface layer is artificially self‐assembled on the surface of the zinc anode by graft‐modified fluorinated monomer (polyacrylic acid‐2‐(Trifluoromethyl)propenoic acid, PAA‐TFPA), on which an organic–inorganic hybrid (PAA‐Zn/ZnF2) solid electrolyte interface (SEI) with excellent ionic conductivity is formed by interacting with Zn2+. Both the pouch cell and fiber zinc anode exhibit excellent plating/stripping reversibility after protecting by this organic–inorganic SEI, which can be stably cycled more than 3000 h in symmetric Zn||Zn cells or 550 h in fiber Zn||Zn cells. Additionally, this interface layer preserves zinc anode with excellent mechanical durability under various mechanical deformation (stably working for another 1200 h after bending 100 h). The corresponding PAA‐Zn/ZnF2@Zn||MnO2 full cell displays an ultra‐long life span (79% capacity retention after 3000 cycles) and mechanical robustness (85% of the initial capacity for another 3000 cycles after bending 100 times). More importantly, the as‐assembled cells can easily power smart wearable devices to monitor the user's health condition.

The detection of apple yield in complex orchards plays an important role in smart agriculture. Due to the large number of fruit trees in the orchard, improving the speed of apple detection has become one of the challenges of apple yield detection. Additional challenges in the detection of apples in complex orchard environments are vision obstruction by leaves, branches and other fruit, and uneven illumination. The YOLOv5 (You Only Look Once version 5) network structure has thus far been increasingly utilized for fruit recognition, but its detection accuracy and real-time detection speed can be improved. Thus, an upgraded lightweight apple detection method YOLOv5-PRE (YOLOv5 Prediction) is proposed for the rapid detection of apple yield in an orchard environment. The ShuffleNet and the GhostNet lightweight structures were introduced into the YOLOv5-PRE model to reduce the size of the model, and the CA (Coordinate Attention) and CBAM (Convolutional Block Attention Module) attention mechanisms were used to improve the detection accuracy of the algorithm. After applying this algorithm on PC with NVIDIA Quadro P620 GPU, and after comparing the results of the YOLOv5s (You Only Look Once version 5 small) and the YOLOv5-PRE models outputs, the following conclusions were obtained: the average precision of the YOLOv5-PRE model was 94.03%, which is 0.58% higher than YOLOv5s. As for the average detection time of a single image on GPU and CPU, it was 27.0 ms and 172.3 ms, respectively, which is 17.93% and 35.23% higher than YOLOV5s. Added to that, the YOLOv5-PRE model had a missed detection rate of 6.54% when being subject to back-light conditions, and a false detection rate of 4.31% when facing front-light conditions, which are 2.8% and 0.86% higher than YOLOv5s, respectively. Finally, the feature extraction process of the YOLOv5-PRE model was presented in the form of a feature map visualization, which enhances the interpretability of the model. Thus, the YOLOv5-PRE model is more suitable for transplanting into embedded devices and adapts well to different lighting conditions in the orchard, which provides an effective method and a theoretical basis for the rapid detection of apples in the process of rapid detection of apple yield.

Measurement data cleaning is a key step of edge computing in a distribution network; it is beneficial to improve the state perception and regional autonomy level of a distribution network. According to the temporal and spatial correlation of measurement data in the distribution network, a joint cleaning method of measurement data in a distribution network is proposed based on the correntropy criterion with variable center unscented Kalman filter (CC-VC-UKF). Initially, the mean square error (MSE) in the original unscented Kalman filter (UKF) is replaced by the correntropy criterion with variable center (CC-VC) to improve the accuracy of filtering the measurement data in the distribution network with a non-Gaussian non-zero mean measurement deviation. Then, the measured data of different measuring devices located on the same section of the line are filtered based on the CC-VC-UKF algorithm according to their respective reference time series to improve the signal-to-noise ratio of the measured data. Then, the filtered measured data are filtered and cleaned based on the CC-VC-UKF algorithm according to the space–time joint filtering and cleaning technology. Finally, the method is used to test the measurement data of the distribution network obtained by a power supply company in a city in north China to solve the problem of measurement deviation caused by the existence of space distance. Results show that this method can obtain FTU measurement data with higher precision from network topology based on the filtered TTU measurement data through the media of filtered spatial measurement deviation.

Background: Knowledge of the factors that influence maternal serum screening (MSS) service utilization can be used to develop health policies to promote equitable access to MSS and further diagnostic tests. The purpose of this study was to find the factors associated with utilization of MSS as well as the current status of service utilization in mainland China.Methods: This was a hospital-based cross-sectional study with respondents interviewed with a questionnaire designed based on Andersen's behavioral model. Descriptive statistics, univariate analysis, and multilevel logistic regression analysis were used to identify the factors associated with MSS utilization, and to explore potential methods to improve screening uptake.Results: A total of 8110 women who had given birth within the previous 7 days in one of 111 participating institutions from six provinces in mainland China were interviewed. Approximately 36% of the participants had used MSS. Women between 20 and 35 years, who resided in urban areas, were educated, were in a stable occupation, who had health knowledge, who attended maternal preparation classes, who had received eight or more prenatal checkups, who were from a region of higher social economic status, and who delivered in a tertiary healthcare institution were significantly more likely to use MSS than their counterparts. As compared with other factors, insufficient education is the single most important demographic factor for service underutilization.Conclusions: Efforts should not only be made to target the population that underuses MSS, but the overall organization of MSS service delivery should be assessed during policy development to make access to MSS equitable to the entire population of mainland China. [ABSTRACT FROM AUTHOR]

The robust Kalman filter with correntropy loss has received much attention in recent years for forecasting-aided state estimation in power systems, since it efficiently reduces the negative influence of various abnormal situations, such as non-Gaussian communication, changing environment, and instrument failures, and obviously improves the stability of power systems. However, the existing correntropy-based robust Kalman filters usually use the Gaussian function with a fixed center as the kernel function in correntropy, which may not be a suitable choice in practical applications of power system forecasting-aided state estimation (PSSE). To address this issue, a new and robust unscented Kalman filter, called the maximum correntropy with variable center unscented Kalman filter (MCVUKF), is proposed in this paper for PSSE. Specifically, MCVUKF adopts an extended version of correntropy, whose center can be located at any position, to replace the original correntropy in an unscented Kalman filter to improve the performance in PSSE. Moreover, by using an exponential function of the innovation vector to adjust a covariance matrix, an enhanced MCVUKF (En-MCVUKF) method is also developed for suppressing the influence of bad data to the innovation vector and further improving the accuracy of PSSE. Finally, extensive simulations have been conducted on IEEE 14-bus, 30-bus, and 57-bus test power systems, and the simulation results have shown the superiority of the proposed MCVUKF and En-MCVUKF methods compared with several related state-of-the-art Kalman filter methods.