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Traffic state estimation plays a fundamental role in traffic control and management. In the connected vehicles (CVs) environment, more traffic-related data perceived and interacted by CVs can be used to estimate traffic state. However, when there is a low penetration rate of CVs, the data collected from CVs would be inadequate. Meanwhile, the representativeness of the collected data is positively correlated with the penetration rate. This article presents a traffic state estimation method based on a deep learning algorithm under a low and dynamic CVs penetration rate environment. Specifically, we design a K-Nearest Neighbor (KNN) data filling model integrating acceleration data to solve the problem of insufficient data. This method can fuse the time feature of speed by acceleration modification and mine the distribution features of speed by KNN. In addition, to reduce the estimation error caused by penetration rate, we design a Long Short-Term Memory (LSTM) model, which uses penetration rate estimated by Macroscopic Fundamental Diagram (MFD) as one of the input factors. Finally, we use the concept of operational efficiency for reference, dividing traffic state into three categories according to the estimated speed: free flow, optimal flow, and congestion. SUMO is used to simulate traffic cases under different penetration rates to evaluate our scheme. The results suggest that our data filling model can significantly improve filling accuracy under a low penetration rate; there is also a better performance of our estimation model than that of other comparison models in both low and dynamic penetration rates., Author(s): Fan Ding [1]; Yongyi Zhang [1,2]; Rui Chen [3]; Zhanwen Liu [4]; Huachun Tan (corresponding author) [1] 1. Introduction Traffic state estimation is a vitally important part of intelligent [...]

Picoxystrobin (PIC) is a fungicide extensively used for disease control in both crops and vegetables. Residues of PIC in vegetables pose a potential threat to human health due to their accumulation in the food chain. In this study, a specific PIC monoclonal antibody (mAb) was developed by introducing a carboxylic acid arm into PIC and subsequently preparing a hapten and an artificial antigen. A sensitive and rapid time-resolved fluorescence immunochromatographic assay (TRFICA) was established based on the mAb. Subsequently, using a time-resolved fluorescent microsphere (TRFM) as signal probe, mAbs and microspheres were covalently coupled. The activated pH, the mAb diluents, the mAb amount, and the probe amount were optimized. Under optimized conditions, the quantitative limits of detection (qLOD) of PIC in cucumber, green pepper, and tomato using TRFICA were established at 0.61, 0.26, and 3.44 ng/mL, respectively; the 50% inhibiting concentrations (IC50) were 11.76, 5.29, and 37.68 ng/mL, respectively. The linear ranges were 1.81–76.71, 0.80–35.04, and 8.32–170.55 ng/mL, respectively. The average recovery in cucumber, green pepper, and tomato samples ranged from 79.8% to 105.0%, and the corresponding coefficients of variation (CV) were below 14.2%. In addition, 15 vegetable samples were selected and compared with the results obtained using ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS). The results revealed a high degree of concordance between the proposed method and UPLC-MS/MS. In conclusion, the devised TRFICA method is a valuable tool for rapid, on-site, and highly sensitive detection of PIC residues in vegetables. [ABSTRACT FROM AUTHOR]

Aqueous zinc battery has been regarded as one of the most promising energy storage systems due to its low cost and environmental benignity. However, the safety concern on Zn anodes caused by uncontrolled Zn dendrite growth in aqueous electrolyte hinders their application. Herein, sucrose with multi-hydroxyl groups has been introduced into aqueous electrolyte to modify Zn2+ solvation environment and create a protection layer on Zn anode, thus effectively retarding the growth of zinc dendrites. Atomistic simulations and experiments confirm that sucrose molecules can enter into the solvation sheath of Zn2+, and the as-formed unique solvation structure enhances the mobility of Zn2+. Such fast Zn2+ kinetics in sucrose-modified electrolyte can successfully suppress the dendrite growth. With this sucrose-modified aqueous electrolyte, Zn/Zn symmetric cells present more stable cycle performance than those using pure aqueous electrolyte; Zn/C cells also deliver an impressive higher energy density of 129.7 Wh·kg−1 and improved stability, suggesting a great potential application of sucrose-modified electrolytes for future Zn batteries. [ABSTRACT FROM AUTHOR]