Specific interactions between scaffold protein SH3 and multiple ankyrin repeat domains protein 3 (Shank3) and synapse-associated protein 90/postsynaptic density-95–associated protein (SAPAP) are essential for excitatory synapse development and plasticity. In a bunch of human neurological diseases, mutations on Shank3 or SAPAP are detected. To investigate the dynamical and thermodynamic properties of the specific binding between the N-terminal extended PDZ (Post-synaptic density-95/Discs large/Zonaoccludens-1) domain (N-PDZ) of Shank3 and the extended PDZ binding motif (E-PBM) of SAPAP, molecular dynamics simulation approaches were used to study the complex of N-PDZ with wild type and mutated E-PBM peptides. To compare with the experimental data, ₉₇₄QTRL₉₇₇ and ₉₆₆IEIYI₉₇₀ of E-PBM peptide were mutated to prolines to obtain the M4P and M5P system, respectively. Conformational analysis shows that the canonical PDZ domain is stable while the βN extension presents high flexibility in all systems, especially for M5P. The high flexibility of βN extension seems to set up a barrier for the non-specific binding in this area and provide the basis for specific molecular recognition between Shank3 and SAPAP. The wild type E-PBM tightly binds to N-PDZ during the simulation while loss of binding is observed in different segments of the mutated E-PBM peptides. Energy decomposition and hydrogen bonds analysis show that M4P mutations only disrupt the interactions with canonical PDZ domain, but the interactions with βN1′ remain. In M5P system, although the interactions with βN1′ are abolished, the binding between peptide and the canonical PDZ domain is not affected. The results indicate that the interactions in the two-binding site, the canonical PDZ domain and the βN1′ extension, contribute to the binding between E-PBM and N-PDZ independently. The binding free energies calculated by MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) are in agreement with the experimental binding affinities. Most of the residues on E-PBM contribute considerably favorable energies to the binding except A963 and D964 in the N-terminal. The study provides information to understand the molecular basis of specific binding between Shank3 and SAPAP, as well as clues for design of peptide inhibitors.

In lithium-ion battery management systems (BMSs), accurate state of charge (SOC) estimation is essential for the stable operation of BMSs. Furthermore, the accuracy of SOC estimation is significantly influenced by the precision of battery model parameters. To improve the SOC estimation accuracy, this paper focuses on the second-order RC equivalent circuit model, firstly designs a simple and reliable improved adaptive forgetting factor (IAFF) regulation mechanism, and proposes the improved adaptive forgetting factor recursive least squares (IAFFRLS) algorithm, which not only improves the accuracy of parameter identification, but also exhibits excellent performance in anti-interference. Secondly, based on the identified model, a weighted multi-innovation improved Sage–Husa adaptive extended Kalman filter (WMISAEKF) algorithm is proposed to solve the problem of filter divergence caused by noise covariance updating. It fully utilizes historical innovations to reasonably allocate innovation weights to achieve accurate SOC estimation. Compared with the VFFRLS algorithm and AFFRLS algorithm, the IAFFRLS algorithm reduces the root mean square error (RMSE) by 29.30% and 19.29%, respectively, and the RMSE under noise interference is decreased by 82.37% and 78.59%, respectively. Based on the identified model for SOC estimation, the WMISAEKF algorithm reduces the RMSE by 77.78%, compared to the EKF algorithm. Furthermore, the WMISAEKF algorithm could still converge under different levels of noise interference and incorrect initial SOC values, which proves that the proposed algorithm has good stability and robustness. Simulation results verify that the parameter identification algorithm proposed in this paper demonstrates higher identification accuracy and anti-interference performance. The proposed SOC estimation algorithm has higher estimation accuracy and good robustness, which provides a new practical support for extending battery life.

In this paper, we present a methodology for identifying the multi-input–multi-output (MIMO) Hammerstein nonlinear model under colored noise. The Hammerstein model presented comprises neural fuzzy models (NFMs) as its static nonlinear block and rational transfer functions (RTF) model as its dynamic linear block. The hybrid signals consisting of separable signals and random signals are utilized to deal with the MIMO Hammerstein model identification issue, and the separable signals to implement separation identification of MIMO Hammerstein model is introduced, that is, the two blocks are separately identified. First, parameters of the linear block are estimated applying correlation function-based least squares method in the presence of measurable input–output of Gaussian signals, which can efficiently weaken the process noise interference. Second, estimate of noise parameters vector is introduced to solve the unknown noise vector in the information matrix, then a recursive extended least squares method is developed for identifying parameters of nonlinear block and colored noise model based on available input–output of random signals. The validity and precision of the presented methodology are demonstrated applying a numerical simulation and a nonlinear process, and it is known from the research results that compared with existing identification techniques, the methodology utilized achieved higher identification accuracy.

To explore the potential for further enhancing the gas mixing uniformity of natural gas (NG) engines, this paper identifies turbulent kinetic energy (TKE), which has an essential impact on gas mixing, as the entry point of the research. After establishing a computational fluid dynamics (CFD) model for NG engines’ direct injection and mixing processes, the inlet pressure is selected as the experimental variable to investigate the influence of TKE on gas mixing uniformity. In particular, by proposing the theoretical concept of the core mixing stage, the numerical variation rule between the best mixture concentration region (BMCR) percentage and the mean turbulent kinetic energy (MTKE) of the core mixing stage is analyzed under certain injection timing conditions. The results indicate that, with identical intake pressures, an advanced gas injection timing elevates the total turbulence kinetic energy (TTKE) during the core mixing stage, thereby amplifying the uniformity of the gas mixture at the ignition. In specific scenarios, as the intake pressure increases, the decreasing trend in the BMCR proportion closely resembles the diminishing trend in the MTKE during the core mixing stage. Scrutinizing the variation trend in either parameter allows for an approximate prediction of the variation trend in the other parameter. When the intake pressure is gradually raised from the naturally aspirated state, the adequacy of the gas jet development is progressively reduced by the increasing back pressure in the cylinder.