A sandwich-type photoelectrochemical biosensor based on Ru(bpy)32+ sensitized In2S3 for aflatoxin B1 detection.

Aflatoxin B1 (AFB1), classified as a class I carcinogen, is a widespread mycotoxin that poses a serious threat to public health and economic development, and the food safety problems caused by AFB1 have aroused worldwide concern. The development of accurate and sensitive methods for the detection of AFB1 is significant for food safety monitoring. In this work, a sandwich-type photoelectrochemical (PEC) biosensor for AFB1 detection was constructed on the basis of an aptamer-antibody structure. A good photocurrent response was obtained due to the sensitization of In2S3 by Ru(bpy)32+. In addition, this sandwich-type sensor constructed by modification with the antibody, target detector, and aptamer layer by layer attenuated the migration hindering effect of photogenerated carriers caused by the double antibody structure. The aptamer and antibody synergistically recognized and captured the target analyte, resulting in more reliable PEC response signals. CdSe@CdS QDs-Apt were modified as a signal-off probe onto the sensor platform to quantitatively detect AFB1 with a "signal-off" response, which enhanced the sensitivity of the sensor. The PEC biosensor showed a linear response range from 10-12 to 10-6 g mL-1 with a detection limit of 0.023 pg mL-1, providing a feasible approach for the quantitative detection of AFB1 in food samples.

Event-triggered H∞ consensus for uncertain nonlinear systems using integral sliding mode based adaptive dynamic programming.

This paper studies a robust optimal consensus problem for uncertain nonlinear multi-agent systems, where the uncertainties include both input and external disturbances. Adaptive distributed observer, integral sliding mode control and H∞ adaptive dynamic programming are integrated to obtain a sub-optimal control protocol for each follower. Firstly, an adaptive distributed observer is designed for state estimation of the leader, which serves as the reference of the ADP algorithm. Then, an H∞ ADP algorithm is presented to make each follower track the reference in real-time. An integral sliding manifold-based discontinuous control is designed to eliminate the matched uncertainty, and continuous control is obtained by solving the Hamilton-Jacobi-Isaac equation under the H∞ tracking framework. Two event-triggered rules are developed to relieve the communication pressure. For simplicity, a critic-only structure is used to numerically implement the proposed algorithm, and a concurrent learning technique is employed to update weights of neural networks. All signals in the closed-loop system are proven to be uniformly ultimately bounded. Finally, a simulation is conducted to demonstrate demonstrates the effectiveness of the method.

Secondary Structural Ensemble Learning Cluster for Estimating the State of Health of Lithium-Ion Batteries.

Accurate online state-of-health (SOH) estimation can improve the operational efficiency of lithium-ion batteries (LIBs) and ensure the safety of energy storage systems. However, the complex electrochemical properties of LIBs make accurate SOH estimation challenging. To overcome this challenge, we propose a secondary structural ensemble learning (SSEL) cluster. The proposed SSEL cluster includes multiple SSEL frameworks established separately within different SOH data intervals, allowing the identification of stable feature-SOH relationships. The adaptability and basic accuracy of each SSEL framework are guaranteed by various base learners and the corresponding stacking model and bagging model fusion. Each framework remains unique and specialized owing to the adoption of back propagation neural networks, which adjust learner weights based on the feature-SOH relationship at each interval. The effectiveness of the SSEL cluster was verified using the Oxford Battery Degradation Dataset 1. Comparisons showed that the proposed estimation method performs better than traditional machine learning methods.

Efficacy of the WINROP algorithm for retinopathy of prematurity screening in Southern China.

AIM: To evaluate the predicting efficacy of severe retinopathy of prematurity (ROP) by the WINROP algorithm (http://winrop.com) in Southern China. METHODS: All preterm infants with the gestational age (GA) less than 32wk were included. Their ROP screening results and serial postnatal body weight were analysed retrospectively. Weekly body weight was entered into and measured by the WINROP system. The outcomes were analysed, and the sensitivity, specificity, positive predictive value and negative predictive value (NPV) were calculated. RESULTS: Totally 432 infants with a median GA of 30.0 (24.0-31.9)wk, and a median birth weight (BW) of 1360 (540-2700) g were included. Among these 432 infants, 50 were diagnosed as type 1 ROP but only 28 were identified by the WINROP algorithm. The sensitivity was 56% (28/50) and the NPV was 92% (252/274). However, for infants with BW <1000 g or GA <28wk, the sensitivity was 93.8% (15/16) and 93.3% (14/15), respectively. Meanwhile, with several postnatal complications added as additional risk factors, the sensitivity was increased to 96% (48/50). CONCLUSION: The sensitivity of the WINROP algorithm from the Southern Chinese cohort is not as high as that reported in developed countries. This algorithm is effective for detecting severe ROP from extremely small or preterm infants. Modification of the algorithm with additional risk factors could improve the predictive value for infants with a GA>28wk in China.