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An electrochemical platform for signal amplification probing chloride ions (Cl - ) is constructed by the composite integrating core-shell structured nitrogen-doped porous carbon@Ag-based metal-organic frameworks (NC@Ag-MOF) with polypyrrole (PPy). It is based on the signal of solid-state AgCl derived from Ag-MOF, since both NC and PPy have good electrical conductivity and promote the electron transport capacity of solid-state AgCl. NC@Ag-MOF was firstly synthesized with NC as the scaffold and then, PPy was anchored on NC@Ag-MOF by chemical polymerization. The composite NC@Ag-MOF-PPy was utilized to modify the electrode, which exhibited a higher peak current and lower peak potential during Ag oxidation compared with those of Ag-MOF and NC@Ag-MOF-modified electrodes. More importantly, in the coexistence of chloride (Cl - ) ions in solution, the NC@Ag-MOF-PPy-modified electrode displayed a fairly stable and sharp peak of solid-state AgCl with the peak potentials gradually approaching zero, which might effectively overcome the background interference caused by electroactive substances. The oxidation peak currents of solid-state AgCl increased linearly with the concentration of  Cl - ions in a broad range of 0.15 µM-40 mM and 40-250 mM, with detection limits of 0.10 µM and 40 mM, respectively. The practical applicability for Cl - ions determination was demonstrated using human serum and urine samples. The results suggest that NC@Ag-MOF-PPy composite could be a promising candidate for the construction of the electrochemical sensor., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Lead (Pb) and arsenic (As) contaminated soils, caused by Pb and zinc (Zn) smelting activities, pose an urgent environmental issue. Magnetic biochar (MB) has been regarded as an increasingly appealing candidate for the remediation of multi-metals in contaminated soils or their leachate. Finding economically feasible preparation methods for MB and demonstrating its remediation potential is desperately required for the remediation of such complex smelting sites. In this study, a modified MB was prepared using an optimized co-precipitation method, and its application potential for Pb/As simultaneous removal based on the basic properties of a typical Pb/Zn smelting site was evaluated. The surface modifications of MB facilitated the encapsulation of various ultrafine iron oxide particles, predominantly γ-Fe 2 O 3 and Fe 3 O 4 , whilst notably enhancing the presence of oxygen-containing surface functional groups. The adsorption of Pb(II) and As(III) by MB was well-described using the pseudo-second-order adsorption and Langmuir models. The existence of SO 4 2- and Ca 2+ in the soil leachate competed with the adsorption sites for Pb(II) and As(III). Notably, within the pH range of 5-9, the adsorption efficiency of Pb(II) by MB increased with the rising solution pH, whereas alterations in pH minimally affected the removal rate of As(III), maintaining a consistent removal rate exceeding 95%. Furthermore, dissolved organic matter (DOM) abundant in organic functional groups, particularly CO and CC groups, significantly augmented the adsorption affinity for both Pb(II) and As(III). An application rate of 2 g/L could effectively reduce the concentration of Pb(II) and As(III) in soil leachate to <0.05 mg/L. The findings demonstrated the potential of the prepared MB for simultaneous removal of As(III) and Pb(II) in soil leachate, which should be beneficial to multi-metals polluted soil remediation in Pb/Zn smelting sites., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Accurate prediction of PV power is essential to ensuring the safe and economic operation of power systems with high PV penetration. The current PV power prediction scheme considering the spatio-temporal correlation characteristics is relatively simple in data processing, resulting in low prediction accuracy; at the same time, the missing data also poses a great problem to the prediction. Therefore, in order to improve the prediction accuracy and solve the problem of missing data, this paper proposes a PV power spatio-temporal prediction model considering time-shift correction and a multi-station information fusion strategy Firstly, relevant power station clusters are constructed using hierarchical clustering, and a similar daily data filtering model considering the variation characteristics of daily power characteristic curves is proposed to filter the data; Secondly, multiple BP neural network models are constructed and multiple reference power stations with high relevance are predicted using irradiance information; Thirdly, the prediction results of multiple reference power stations are input to the data processing module for time-shift analysis and spatial correlation information fusion correction, which solves the missing data problem of the target power station to be predicted. Finally, it is input to One-dimensional Convolutional Neural Network(1DCNN) to achieve the power prediction of the target power station with missing data. The simulation analysis shows that the root mean square error (RMSE) of a sunny day forecast is 3.31%; the RMSE of a non-sunny day forecast is 9.65%, which proves the accuracy of this two-layer neural network is higher compared to other model structures, so the proposed scheme has certain reliability and accuracy in the prediction of PV power with missing data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

An increasing number of deep autoencoder-based algorithms for intelligent condition monitoring and anomaly detection have been reported in recent years to improve wind turbine reliability. However, most existing studies have only focused on the precise modeling of normal data in an unsupervised manner; few studies have utilized the information of fault instances in the learning process, which results in suboptimal detection performance and low robustness. To this end, we first developed a deep autoencoder enhanced by fault instances, that is, a triplet-convolutional deep autoencoder (triplet-Conv DAE), jointly integrating a convolutional autoencoder and deep metric learning. Aided by fault instances, triplet-Conv DAE can not only capture normal operation data patterns but also acquire discriminative deep embedding features. Moreover, to overcome the difficulty of scarce fault instances, we adopted an improved generative adversarial network-based data augmentation method to generate high-quality synthetic fault instances. Finally, we validated the performance of the proposed anomaly detection method using a multitude of performance measures. The experimental results show that our method is superior to three other state-of-the-art methods. In addition, the proposed augmentation method can efficiently improve the performance of the triplet-Conv DAE when fault instances are insufficient., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

For data-driven fast economic dispatch in the electricity-gas cyber-physical system (EGCPS), the strong reliance on communication networks makes it vulnerable to intentional cyberattacks. False data injection attack (FDIA) is emerging as a severe threat to secure system operation. To enhance the cybersecurity and computational efficiency in data-driven fast economic dispatch, a data-driven approach is proposed to serve both exact locational detection of FDIA and fast economic dispatch in real time. The data-driven approach concatenates a residual network (ResNet) and attention long short term memory model (ALSTM) based on LSTM and attention mechanism (called "ResNet-ALSTM"), which can achieve temporal correlations and feature extraction of large amounts of measurements. The proposed ResNet-ALSTM can serve as a multi-label classifier to detect the outlier locations of tampered measurements in the power system of the EGCPS. Further, the Fast Dynamic Time Warping algorithm can timely recover the tampered measurements of power system after FDIA. Finally, the proposed ResNet-ALSTM can achieve fast economic dispatch in the EGCPS with the recovered measurements of power system. The performances of the proposed methods are experimentally evaluated on IEEE 24-bus power system and Belgian 20-node gas system. The results demonstrate that the proposed methods can achieve the locational detection of FDIA, tampered data recovery and data-driven fast economic dispatch with high accuracy and computational efficiency. This research is a promising solution for safeguarding secure system operation of EGCPS against FDIA., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

This trial was conducted to investigate the effect of mannose oligosaccharides (MOS) on the growth performance, antioxidation, immunity and disease resistance of Vibro Parahemolyticus in juvenile abalone Haliotis discus hannai Ino. Four formulated diets were produced to contain 0.00 g/kg, 0.40 g/kg, 0.80 g/kg and 1.60 g/kg Actigen®, with functional ingredients of MOS, respectively. Accordingly, the experimental diets were named as A0, A4, A8 and A16. After 120-days feeding trial, the best growth performance was observed in A8 group (P < 0.05) and there was no significant difference in A0, A4 and A16 groups. With the increase of dietary MOS, the activity of the total antioxidant capacity in hepatopancreas is increasingly elevated (P < 0.05) while no significant difference was observed on activity of glutathione S-transferase (P > 0.05). The activities of superoxide dismutase and glutathione peroxidase were firstly increased and then decreased, with the highest values in A8 group (P < 0.05). Immune-related parameters were significantly affected by dietary MOS inclusion. Specifically, the activities of alkaline phosphatase and acid phosphatase in hepatopancreas and serum of abalone fed diets containing MOS were significantly higher than those of control A0 group (P < 0.05). Moreover, the highest values of both enzymes were observed in hepatopancreas of A8 group but in serum of A16 group, respectively. The lysozyme activities in hepatopancreas and serum of A4 group were significantly higher than those of other groups (P < 0.05) and there was no significant difference in A0, A8 and A16 groups (P > 0.05). The activities of cytophagy and respiratory burst in serum of abalone were not significantly affected by dietary MOS content (P > 0.05). The mRNA levels of focal adhesion kinase and integrin-linked kinase were gradually elevated with the increase of dietary MOS, with the highest value recorded in A16 group (P < 0.05). The gene expression of caspse-3 in A8 group was dramatically higher than those of other groups (P < 0.05) and there was no significant difference in A0, A4 and A16 groups (P > 0.05). The mRNA level of nuclear factor-κB was not significantly affected by dietary MOS (P > 0.05). During 56 h of V. Parahemolyticus challenge period, the accumulated mortality rate of abalone fed diets containing MOS were significantly lower than that of control A0 group in each time point (P < 0.05). Overall, the lowest rate was happened in A8 group (P < 0.05). In conclusion, MOS inclusion in diet has obviously positive effect on growth, immunity and disease resistance capability of abalone, with the optimal level of Actigen® at 0.80 g/kg in diet., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

In the present study, a sialic acid-binding lectin was identified and characterized from Manila clam Ruditapes philippinarum (designed as RpSABL-1). Multiple alignments strongly suggested that RpSABL-1 was a new member of the sialic acid-binding lectin family. In non-stimulated clams, RpSABL-1 transcripts were constitutively expressed in all five tested tissues, especially in hepatopancreas. After Vibrio anguillarum challenge, the expression of RpSABL-1 mRNA was significantly up-regulated at 6 h (P < 0.05), 12 h (P < 0.01) and 24 h (P < 0.01). Recombinant RpSABL-1 protein (rRpSABL-1) displayed apparent binding activities towards lipopolysaccharides (LPS) and peptidoglycan (PGN), but not to glucan or chitin in vitro. Coinciding with the PAMPs binding assay, rRpSABL-1 exhibited obvious agglutination activities against Gram-positive bacterium Staphyloccocus aureus, Gram-negative bacteria Escherichia coli, V. anguillarum and Vibrio harveyi. Meanwhile, rRpSABL-1 showed antibacterial activities against E. coli, and biofilm formation of E. coli could also be inhibited after incubated with rRpSABL-1. Moreover, the encapsulation, phagocytosis and chemotactic ability of hemocytes could be enhanced by rRpSABL-1. All these results suggested that RpSABL-1 could function as a pattern recognition receptor with versatile functions in the innate immune responses of R. philippinarum., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The optimal tuning of adaptive flap controller can improve adaptive flap control performance on uncertain operating environments, but the optimization process is usually time-consuming and it is difficult to design proper optimal tuning strategy for the flap control system (FCS). To solve this problem, a novel adaptive flap controller is designed based on a high-efficient differential evolution (DE) identification technique and composite adaptive internal model control (CAIMC) strategy. The optimal tuning can be easily obtained by DE identified inverse of the FCS via CAIMC structure. To achieve fast tuning, a high-efficient modified adaptive DE algorithm is proposed with new mutant operator and varying range adaptive mechanism for the FCS identification. A tradeoff between optimized adaptive flap control and low computation cost is successfully achieved by proposed controller. Simulation results show the robustness of proposed method and its superiority to conventional adaptive IMC (AIMC) flap controller and the CAIMC flap controllers using other DE algorithms on various uncertain operating conditions. The high computation efficiency of proposed controller is also verified based on the computation time on those operating cases., (Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.)