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Alzheimer's disease is neurodegenerative and characterized by progressive cognitive impairment. Synaptic dysfunction appears in the early stage of Alzheimer's disease and is significantly correlated with cognitive impairment. However, the specific regulatory mechanism remains unclear. Here, we found the transcription factor Maf1 to be upregulated in Alzheimer's disease and determined that conditional knockout of Maf1 in a transgenic mouse model of Alzheimer's disease restored learning and memory function; the downregulation of Maf1 reduced the intraneuronal calcium concentration and restored neuronal synaptic morphology. We also demonstrated that Maf1 regulated the expression of NMDAR1 by binding to the promoter region of Grin1 , further regulating calcium homeostasis and synaptic remodelling in neurons. Our results clarify the important role and mechanism of the Maf1-NMDAR1 signalling pathway in stabilizing synaptic structure, neuronal function and behaviour during Alzheimer's disease pathogenesis. This therefore serves as a potential diagnostic and therapeutic target for the early stage of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Accurate prediction of lithium ion (li-ion) battery capacity is of great significance to battery health status management. In this paper, the different discharge time corresponding to the equal voltage interval is taken as the health factor. Three highly correlated health factors are extracted from the battery discharge curve, and the Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) is used to estimate the battery capacity accurately and quickly. Firstly, health factors related highly to battery capacity from the battery cycle life test are extracted, and the selected health factors are analyzed using the Spearman correlation coefficient and Pearson correlation coefficient. Secondly, this paper analyzes the prediction effect of different combinations of selected health factors using BPNN optimized by a genetic algorithm. Then, to verify the superiority of the proposed optimization algorithm, different optimization algorithms are used to adjust and optimize the parameters of BPNN automatically, and the experimental data are used for comparative analysis. Finally, the GA-BP is compared with other common battery capacity prediction methods. The results show that the GA-BP neural network prediction model can accurately and effectively predict the capacity of li-ion battery when using selected health factors. [ABSTRACT FROM AUTHOR]

Cable termination serves as a crucial carrier for high-speed train power transmission and a weak part of the cable insulation system. Partial discharge detection plays a significant role in evaluating insulation status. However, field testing signals are often contaminated by external corona interference, which affects detection accuracy. This paper proposes a classification model based on wavelet transform (WT) and deep belief network (DBN) to accurately and rapidly identify corona discharge in the partial discharge signals of vehicle-mounted cable terminals. The method utilizes wavelet transform for noise reduction, employing the sigmoid activation function and analyzing the impact of WT on DBN classification performance. Research indicates that this method can achieve an accuracy of over 89% even with limited training samples. Finally, the reliability of the proposed classification model is verified using measured mixed signals. [ABSTRACT FROM AUTHOR]

UDP (User Datagram Protocol) does not adopt retransmission mechanism. Packet loss may happen when poor contact or electromagnetic interference occurs, which can aff ect the link. Quick analysis for a single data file is achieved by using the Wireshark's built-in statistical and ranking functions. Based on the ranking idea, Auto-Array method, which adopts a software algorithm, is further studied. Tests and comparisons show that Auto-Array method can provide quick analysis for multiple data files. Details of the packet loss in UDP data files can also be output. The method can provide criteria for evaluating the reliability of UDP transmission. [ABSTRACT FROM AUTHOR]

Channel steel-reinforced brick column technology has gained significant popularity in rural China due to its convenience and cost effectiveness. However, current research on channel steel reinforcement is sparse, and engineering applications often rely solely on construction experience. This reliance leads to significant construction errors, inconsistent reinforcement effects, and, in some cases, tragedies such as the collapse of Changsha's "4.29" self-built houses. Therefore, in this paper, experimental and simulation studies on brick columns reinforced with external channel steel were conducted, and the results show that channel steel reinforcement can significantly enhance the axial load capacity of brick columns. However, increased initial stress levels and height-to-thickness ratios substantially reduce the reinforcement effect. Under axial pressure, the outer channel steel fails mainly through bending and buckling instability. Still, due to its good ductility, its failure occurs later than the brick column after being restrained by sufficient wall screws. Based on the experimental and simulation results, a method for calculating the axial compressive bearing capacity of the reinforced column is proposed, providing theoretical support and engineering guidance for applying this reinforcement method. [ABSTRACT FROM AUTHOR]

Background: Casuarina equisetifolia (C. equisetifolia) is a woody species with many excellent features. It has natural resistance against drought, salt and saline-alkali stresses. WRKY transcription factors (TFs) play significant roles in plant response to abiotic stresses, therefore, molecular characterization of WRKY gene family under abiotic stresses holds great significance for improvement of forest trees through molecular biological tools. At present, WRKY TFs from C. equisetifolia have not been thoroughly studied with respect to their role in salt and saline-alkali stresses response. The current study was conducted to bridge the same knowledge gap. Results: A total of 64 WRKYs were identified in C. equisetifolia and divided into three major groups i.e. group I, II and III, consisting of 10, 42 and 12 WRKY members, respectively. The WRKY members in group II were further divided into 5 subgroups according to their homology with Arabidopsis counterparts. WRKYs belonging to the same group exhibited higher similarities in gene structure and the presence of conserved motifs. Promoter analysis data showed the presence of various response elements, especially those related to hormone signaling and abiotic stresses, such as ABRE (ABA), TGACG (MeJA), W-box ((C/T) TGAC (T/C)) and TC-rich motif. Tissue specific expression data showed that CeqWRKYs were mainly expressed in root under normal growth conditions. Furthermore, most of the CeqWRKYs were up-regulated by NaCl and NaHCO3 stresses with few of WRKYs showing early responsiveness to both stresses while few others exhibiting late response. Although the expressions of CeqWRKYs were also induced by cold stress, the response was delayed compared with other stresses. Transgenic C. equisetifolia plants overexpressing CeqWRKY11 displayed lower electrolyte leakage, higher chlorophyll content, and enhanced tolerance to both stresses. The higher expression of abiotic stress related genes, especially CeqHKT1 and CeqPOD7, in overexpression lines points to the maintenance of optimum Na+/K+ ratio, and ROS scavenging as possible key molecular mechanisms underlying salt stress tolerance. Conclusions: Our results show that CeqWRKYs might be key regulators of NaCl and NaHCO3 stresses response in C. equisetifolia. In addition, positive correlation of CeqWRKY11 expression with increased stress tolerance in C. equisetifolia encourages further research on other WRKY family members through functional genomic tools. The best candidates could be incorporated in other woody plant species for improving stress tolerance. [ABSTRACT FROM AUTHOR]

Lithium–ion batteries are key elements of electric vehicles and energy storage systems, and their accurate State of Charge (SOC) estimation is momentous for battery energy management, safe operation, and extended service life. In this paper, the Multi-Kernel Relevance Vector Machine (MKRVM) and Whale Optimization Algorithm (WOA) are used to estimate the SOC of lithium–ion batteries under different operating conditions. In order to better learn and estimate the battery SOC, MKRVM is used to establish a model to estimate lithium–ion battery SOC. WOA is used to automatically adjust and optimize weights and kernel parameters of MKRVM to improve estimation accuracy. The proposed model is validated with three lithium–ion batteries under different operating conditions. In contrast to other optimization algorithms, WOA has a better optimization effect and can estimate the SOC more accurately. In contrast to the single kernel function, the proposed multi-kernel function greatly improves the precision of the SOC estimation model. In contrast to the traditional method, the WOA-MKRVM has a higher precision of SOC estimation. [ABSTRACT FROM AUTHOR]

The scientific prediction of the TBM penetration rate is of great significance to the selection of hydraulic tunnel construction methods, construction schedule and cost estimation. In view of the high nonlinearity, fuzziness and complexity of TBM excavation process, and in order to improve the prediction accuracy and computational efficiency, the partial least squares regression (PLSR) has been applied to extract the principal components of the influencing parameters. Then the deep neural network (DNN) is employed to train and forecast the TBM penetration rate. A prediction model of TBM penetration rate based on the coupled method of PLSR and DNN is proposed. Based on the measured data of the double-shield TBM construction of a water conveyance tunnel in the Lanzhou water source construction project, six impact parameters including the rock uniaxial compressive strength, rock uniaxial tensile strength, cutter head thrust, cutter head speed, rock mass integrity coefficient and rock Cerchar abrasiveness index are selected to verify the prediction reasonability of the model. The fitting and prediction accuracy of the different prediction methods are compared and analyzed. The research results show that the PLSR can effectively overcome the problem of multiple collinearity between the independent variables. The extracted principal components are trained as the input layer of the DNN, which simplifies the structure of the neural network. The PLSR-DNN coupled model effectively avoids the over-fitting and inadequate fitting problems. It has the characteristics of fast convergence, stable solution and high fitting accuracy. The average relative fitting error of the PLSR-DNN prediction model is 2.96%, and the average relative prediction error is 3.27%. The fitting accuracy and prediction accuracy of the PLSR-DNN prediction model is significantly higher than those of PLSR model alone, BP neural network model and SVR model, respectively.