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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]

• RK1 exhibits neuroprotective effects in PC12 cells and primary neuron. • RK1 alleviates AD pathology, and the effect is better than the clinical drug donepezil. • The protective effect of RK1 is associated with the activation of AMPK/Nrf2 signaling pathway. • RK1 is expected to be a potential therapeutic drug for AD. The pathogenesis of Alzheimer's disease (AD) is complex, resulting in unsatisfactory effects of single-target therapeutic drugs. Accumulation evidence suggests that low toxicity multi-target drugs may play effective roles in AD. Ginseng is the root and rhizome of Panax ginseng Meyer, which can be used not only as herbal medicine but also as a functional food to support body functions. Ginsenoside RK1 (RK1), obtained from ginseng plants through high-temperature treatment, has antiapoptotic, antioxidant, anti-inflammatory effects and these events are involved in the development of AD. So, we believe that RK1 may be an effective drug for the treatment of AD. We aimed to investigate the potential protective effects and mechanisms of RK1 in AD. Neuronal damage was detected by MTT assay, LDH assay, immunofluorescence and western blotting. Oxidative stress was measured by JC-1 staining, reactive oxygen species (ROS) staining, superoxide dismutase (SOD) and malonaldehyde (MDA). The cognitive deficit was measured through morris water maze (MWM) and novel object recognition (NOR) tests. RK1 attenuated Aβ-induced apoptosis, restored mitochondrial membrane potential (ΔΨm), and reduced intracellular levels of ROS in both PC12 cells and primary cultured neurons. In vivo, RK1 significantly improved cognitive deficits and mitigated AD-like pathological features. Notably, RK1 demonstrated superior efficacy compared to the positive control drug, donepezil. Mechanistically, our study elucidates that RK1 modulates the phosphorylation of AMP-activated protein kinase (AMPK) and its downstream target, NF-E2-related factor 2 (Nrf2), leading to the optimization of mitochondrial membrane potential, reduction of ROS levels, and mitigation of AD-like pathology. It's noteworthy that blocking the AMPK signaling pathway attenuated the protective effects of RK1. RK1 demonstrates superior efficacy in alleviating cognitive deficits and mitigating pathological changes compared to donepezil. These findings suggest the potential utility of RK1-based therapies in the development of treatments for AD. [Display omitted] [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]

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]