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Summary This article focuses on a distributed optimization problem under an undirectd and connected network affected by DoS attacks. First, new event‐triggered mechanisms are developed to schedule the communication between agents. Then, an event‐based resilient distributed optimization algorithm that removes the requirement of continuous communication in the existing results is designed to achieve consensus optimization. Assisted by the design of regulator equations and the algebraic graph theory, it proves that the proposed algorithm is exponentially convergent. Moreover, Zeno behavior can be excluded by reductio ad absurdum. The simulation results validate that the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

Satellite-Drone Image Cross-View Geolocalization has wide applications. Due to the pronounced variations in the visual features of 3D objects under different angles, Satellite-Drone cross-view image geolocalization remains an unresolved challenge. The key to successful cross-view geolocalization lies in extracting crucial spatial structure information across different scales in the image. Recent studies improve image matching accuracy by introducing an attention mechanism to establish global associations among local features. However, existing methods primarily focus on using single-scale features and employ a single-channel attention mechanism to correlate local convolutional features from different locations. This approach inadequately explores and utilizes multi-scale spatial structure information within the image, particularly lacking in the extraction and utilization of locally valuable information. In this paper, we propose a cross-view image geolocalization method based on multi-scale information and a dual-channel attention mechanism. The multi-scale information includes features extracted from different scales using various convolutional slices, and it extensively utilizes shallow network features. The dual-channel attention mechanism, through successive local and global feature associations, effectively learns depth discriminative features across different scales. Experimental results were conducted using existing satellite and drone image datasets, with additional validation performed on an independent self-made dataset. The findings indicate that our approach exhibits superior performance compared to existing methods. The methodology presented in this paper exhibits enhanced capabilities, especially in the exploitation of multi-scale spatial structure information and the extraction of locally valuable information. [ABSTRACT FROM AUTHOR]

Rapid and accurate battery fault diagnosis and distinction is of great importance in electrical vehicles and electrochemical energy storage system. However, misdiagnosis and missed diagnosis happened occasionally. In this paper, a statistical analysis-based multi-fault diagnosis method is proposed to detect and localize short circuit faults, electrical connection faults and voltage sensor faults in LFP battery packs. This method uses non-redundant interleaved voltage measurement topology to detect battery voltages, where every voltage sensor measures the sum of two neighboring batteries and one connection resistor between them. The statistical analysis method sets detection thresholds based on the battery operating data, and captures fault characteristics by analyzing abnormal changes in battery voltage unrelated to current. Theoretical analysis and tests verified that this method can diagnose these three kinds of faults. Sensor faults of excessive error and data sticking can also be distinguished. • The proposed method can diagnose short circuit faults, electrical connection faults and sensor faults. • The non-redundant interleaved voltage measurement topology does not require additional voltage sensors, which saves costs. • The introduction of time window avoids blocking the fault diagnosis process when a fault occurs. • Sensor faults of excessive error and data sticking can be distinguished. [ABSTRACT FROM AUTHOR]

A Convolution Neural Network and a Bidirectional Long Short-Term Memory network connected architecture with a Multi-Head Attention mechanism (CNN-BiLSTM-MHA) is studied for predicting state of charge (SOC) of lithium-ion batteries (LIBs). Firstly, an adaptive noise based complete ensemble empirical mode decomposition (AN-CEEMD) algorithm is adopted to catch the intrinsic features of measured battery signals by adding white noises. Secondly, a CNN-BiLSTM model with the MHA mechanism is developed to learn the mapping between processed input signals and battery SOC, it has three parts: 1) the CNN extracts features of the processed data, mine the relation among input signals, and promote estimation precision; 2) the BiLSTM has memory ability to catch battery dynamics, and the Swish activation function in the BiLSTM ensures unsaturated and reduces over-fitting due to its upper unbound and lower bound; 3) The multi-head attention mechanism uses several independent self-attention layers to associate input information and variables, extracts more associate information by adding weights; it reduces the overfitting risk of a single attention head, and improves the model generalization performance through joint learning of multiple heads. Finally, experiments and simulations are implemented under four operating conditions at five different temperatures, and the presented method is verified effective. • A CNN-BiLSTM network with multi-head attention (MHA) is explored to identify SOC. • The CNN extracts features of input data, and mine the relation among input signals. • The BiLSTM with feedback structure has memory ability to catch battery dynamics. • The MHA mechanism improves model performance through joint learning of multiple heads. [ABSTRACT FROM AUTHOR]

This paper is concerned with anti-disturbance output feedback control of uncertain systems via dynamic guaranteed cost triggering mechanism. An output disturbance observer is constructed to attenuate matched disturbances and the mismatched one is attenuated by the H ∞ approach. A new dynamic event-triggered mechanism is established in terms of guaranteed cost triggering condition and triggering threshold. A composite controller is built on an observer based state feedback controller and an output disturbance compensator. By employing the Lyapunov stability method, a sufficient condition is presented in the framework of linear matrix inequality to ensure the asymptotic stability of the closed-loop system with the prescribed cost. Comparative simulation studies are supplied to verify the effectiveness of the proposed composite control scheme. • Compared with the state DOB [25] , an output DOB is established firstly to reconstruct matched disturbances in input channels. • A new dynamic ETM is put forward and contains [36] as a special case. • A composite controller is constructed by an observer-based state feedback controller and a disturbance compensator. • A structural separation technique is utilized to tackle the controller gain and the observer gains. [ABSTRACT FROM AUTHOR]

Cladophora qinghaiensis, an endemic species of Cladophora in saltwater lakes, was scientifically named in 2021 (hereafter referred to as Cladophora). Cladophora exists in different morphologies, including attached submerged Cladophora (AC), grown floating Cladophora (GFC), and death floating Cladophora (DFC). Previous satellite remote sensing has mainly focused on identifying floating algae. In this study, Qinghai Lake served as a case study, and a classification decision tree model (CDTM) was proposed. The model employed the chlorophyll spectral index (CSI) and the normalized difference vegetation index (NDVI) to differentiate AC, Floating Cladophora (FC), and water. Additionally, the floating Cladophora index (FCI) was introduced to further distinguish GFC and DFC within FC. The method was applicable to Sentinel-2 images from 2016–2023. Visual interpretation methods were used for Landsat series images from the summer months (July to September) to obtain the AC and FC. The results demonstrate that over the past 30 years, the areas inhabited by AC and FC have increased gradually. The three morphologies of Cladophora also exhibited seasonal variations, with growth observed annually in May–June, reaching peaks in August–September, and gradually declining in October. In addition, by combining factors such as water surface area and climatic factors, we analyzed the driving forces influencing the changes in Cladophora. In this research, AC and FC showed significant correlations with the water surface area, with correlation coefficients (r) of approximately 0.9 and 0.7, respectively. These new findings provide valuable insights regarding the spatiotemporal changes and underlying causes for different morphologies of Cladophora in global saline lakes. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the impact of anthocyanin-rich black currant extract (BCE) on the structural properties of starch and the inhibition of glycosidases, gathering data and research evidence to support the use of low glycemic index (GI) foods. The BCE induced a change in the starch crystal structure from A-type to V-type, resulting in a drop in digestibility from 81.41 % to 65.57 %. Furthermore, the inhibitory effects of BCE on glycosidases activity (α -glucosidase: IC 50 = 0.13 ± 0.05 mg/mL and α -amylase: IC 50 = 2.67 ± 0.16 mg/mL) by inducing a change in spatial conformation were confirmed through in vitro analysis. The presence of a 5′-OH group facilitated the interaction between anthocyanins and receptors of amylose, α -amylase, and α -glucosidase. The glycosyl moiety enhanced the affinity for amylose yet lowered the inhibitory effect on α -amylase. The in vivo analysis demonstrated that BCE resulted in a reduction of 3.96 mM·h in blood glucose levels (Area Under Curve). The significant hypoglycemic activity, particularly the decrease in postprandial blood glucose levels, highlights the potential of utilizing BCE in functional foods for preventing diabetes. [Display omitted] • BCE delayed starch digestion by both structural change and glycosidases inhibition. • The 5′-OH of anthocyanin enhanced the ability to bind amylose and glycosidases. • Starch containing BCE attenuated postprandial hyperglycemia in vivo efficiently. [ABSTRACT FROM AUTHOR]

The tin bronze- x Cr 3 C 2 -graphite composites (x = 5, 10, 15, and 20 wt%) were fabricated by mechanical alloying and spark plasma sintering. Fine-scale uniform distribution of composites was achieved. The Cr 3 C 2 particles enhanced the mechanical properties and wear resistance of copper-based composites. The as-sintered tin bronze-20Cr 3 C 2 -graphite showed a hardness of 109 HV, a compressive strength of 333 MPa, and a yield strength of 219 MPa. In addition, the wear rate of as-sintered tin bronze-20Cr 3 C 2 -graphite was maintained in the order of 10−5 at 30 N, 50 N, and 70 N. The wear mechanism of the studied composites varied from surface fatigue and adhesion wear to abrasive wear with the increase of Cr 3 C 2 content during dry sliding. These findings provide a new method for developing copper-based composites with enhanced mechanical properties and wear resistance. • The tin bronze-Cr 3 C 2 -graphite composites were fabricated by powder metallurgy. • The Cr 3 C 2 particles enhanced the mechanical properties and wear resistance of copper-based composites. • The wear mechanism of the studied composites varied with the increase of Cr 3 C 2 content. [ABSTRACT FROM AUTHOR]

In the ancient book "Shen Nong's Herbal Classic," Panax ginseng CA Mey was believed to have multiple benefits, including calming nerves, improving cognitive function, and promoting longevity. Ginsenosides are the main active ingredients of ginseng. Ginsenoside RK3 (RK3), a rare ginsenoside extracted from ginseng, displays strong pharmacological potential. However, its effect on neurogenesis remains insufficiently investigated. This study aims to investigate whether RK3 improves learning and memory by promoting neurogenesis, and to explore the mechanism of RK3 action. The therapeutic effect of RK3 on learning and memory was determined by the Morris water maze (MWM) and novel object recognition test (NORT). The pathogenesis and protective effect of RK3 on primary neurons and animal models were detected by immunofluorescence and western blotting. Protein expression of cAMP response element-binding protein (CREB)/brain-derived neurotrophic factor (BDNF) signaling pathway was detected by western blotting. Our results showed that RK3 treatment significantly improved cognitive function in APPswe/PSEN1dE9 (APP/PS1) mice and C57BL/6 (C57) mice. RK3 promotes neurogenesis and synaptogenesis in the mouse hippocampus. In vitro , RK3 prevents Aβ-induced injury in primary cultured neurons and promotes the proliferation of PC12 as well as the expression of synapse-associated proteins. Mechanically, the positve role of RK3 on neurogenesis was combined with the activation of CREB/BDNF pathway. Inhibition of CREB/BDNF pathway attenuated the effect of RK3. In conclusion, this study demonstrated that RK3 promotes learning and cognition in APP/PS1 and C57 mice by promoting neurogenesis and synaptogenesis through the CREB/BDNF signaling pathway. Therefore, RK3 is expected to be further developed into a potential drug candidate for the treatment of Alzheimer's disease (AD). [Display omitted] • RK3 improves cognitive impairment in APP/PS1 mice. • RK3 improves the learning and memory ability of C57 mice. • RK3 promotes neurogenesis and synaptogenesis. • The effect of RK3 is associated with activation of the CREB/BDNF signaling pathway. [ABSTRACT FROM AUTHOR]