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This study focuses on the propagation behavior of traveling wave solution in microcrystalline materials using the polynomial complete discriminant system method. By establishing a complete discriminant system, we systematically analyze the formation and evolution process of traveling wave solution in microcrystalline materials. Specifically, we apply the cubic polynomial extension to the strain wave equation to obtain more accurate analytical solutions. Additionally, two-dimensional, three-dimensional, and contour plots are generated to visually illustrate the characteristics of the obtained solutions, facilitating a more intuitive understanding of their physical significance. These findings not only help reveal the propagation mechanism of traveling wave solution but also provide a theoretical foundation for the application of microcrystalline materials.

Dithieno[3,2‐b:2',3'‐d]thiophene (DTT) derivative contains three annulated thiophene rings, which have attracted great attention in the field of materials due to its planar and conjugated molecular, structural rigidity, and increased solubility. Herein, the one‐step modular construction of a series of structurally well‐defined 2,6‐disubstituted dithieno[3,2‐b:2',3'‐d]thiophenes (2,6‐DTT) involves Pd/Cu(I)‐catalyzed Sonogashira coupling and LiNH2‐mediated ring‐closing reaction with element sulfur was reported. The method allows the selective and straightforward introduction of different aryl substitution into DTT without otherwise tedious separation and purification processes. The preliminary study of the thermal and photophysical properties demonstrated that the introduction of substituent in phenyl has great impact for the parent molecule, which enable this protocol to synthesis high‐performance 2,6‐DTT through the different alkynes. [ABSTRACT FROM AUTHOR]

Swarm intelligence method is an effective way to improve the particle degradation and sample depletion of the traditional particle filter. This paper proposes a particle filer based on the gravitation field algorithm (GF-PF), and the gravitation field algorithm is introduced into the resampling process to improve particle degradation and sample depletion. The gravitation field algorithm simulates the solar nebular disk model, and introduces the virtual central attractive force and virtual rotation repulsion force between particles. The particles are moves rapidly to the high-likelihood region under action of the virtual central attractive force. The virtual rotation repulsion force makes the particles keep a certain distance from each other. These operations improve estimation performance, avoid overlapping of particles and maintain the diversity of particles. The proposed method is applied into INS/gravity gradient aided navigation, by combining the sea experimental data of an inertial navigation system. Compared with the particle swarm optimization particle filter(PSO-PF) and artificial physics optimized particle filter (APO-PF), the GF-PF has higher position estimate accuracy and faster convergence speed with the same experimental conditions.

The steady airflow field on a ship is affected by structure and motion and challenged by phenomena such as the low measurement accuracy of the wind field caused by the occlusion of the anemometer. In this work, an improvement in the accuracy of wind measurements affected by structure is proposed, and a method for combining anemometer and X-band marine radar (RCRF) data is designed to further obtain wind parameters. The first step is to use the multivariate bias strategy to achieve the optimal layout of multiple anemometers based on computational fluid dynamics (CFD) numerical simulation data. Then, random forest (RF) is employed to train the wind parameter estimation model. Finally, the wind parameters are optimally estimated by combining the anemometer with the X-band radar. Under the ideal simulation, noise, and temporal uncertainty combined with anemometer noise conditions, the RCRF algorithm performance is evaluated. Compared with the bias correction combination four-anemometer weighted fusion algorithm (FAF-BC) and the BP neural network algorithm for radar wind measurement combination (RCBP), the mean errors in wind direction and speed are reduced by 1.99° and 6.99% at most. The maximum errors are reduced by 14.46° and 15.81% at most, respectively. [ABSTRACT FROM AUTHOR]

The transporter associated with antigen processing (TAP) translocates antigenic peptides from the cytosol into the lumen of the endoplasmic reticular and plays a critical role in the major histocompatibility complex (MHC) class I molecule-mediated antigenic presentation pathway. In this study, the porcine TAP1 gene was mapped to the pig chromosome 7 (SSC7) and was closely linked to the marker SSC2B02 (retention fraction=43%, LOD=15.18). Subcellular localization of TAP1 by transient transfection of PK15 cells indicated that the TAP1 protein might be located in the endoplasmic reticulum (ER) in pig kidney epithelial cells (PK-15). Gene expression analysis by semi-quantitative RT-PCR revealed that TAP1 was selectively expressed in some immune and immune-related tissues. Quantitative real-time PCR (qRT-PCR) analysis revealed that this gene was up-regulated after treatments that mimic viral and bacterial infection (polyriboinosinic-polyribocytidylic acid (poly(I:C)) and lipopolysaccharide (LPS), respectively). In addition, elevated TAP1 expression was detected after porcine reproductive and respiratory syndrome virus (PRRSV) infection in porcine white blood cells (WBCs). One single nucleotide polymorphism (SNP) in exon 3 of TAP1 was detected in a Landrace pig population by Bsp143I restriction enzyme digestion. Different genotypes of this SNP had significant associations (PP=0.0168), the PRRSV antibody level (PRRSV Ab) (P=0.0445) and the absolute lymphocyte count (LYM#) (P=0.024) of 17 d pigs. Our results showed that the TAP1 gene might have important roles in swine immune responses, and these results provide useful information for further functional studies.

To address particle degeneracy and the challenge of selecting importance density functions in traditional particle filters for complex nonlinear systems, we propose an adaptive mixed-order spherical simplex-radial cubature particle filter algorithm based on constrained optimization and fading memory. This algorithm integrates constrained optimization, an adaptive fading memory strategy, adaptive adjustment of particle numbers and weights, and the advantages of mixed-order spherical simplex-radial cubature Kalman filtering. By designing the importance density function using a mixed-order integration method, the algorithm significantly improves filtering accuracy over traditional cubature Kalman filters while maintaining lower computational complexity than high-order cubature Kalman filter methods. The adaptive fading memory strategy dynamically adjusts the covariance matrix, enhancing sensitivity to current measurement information and reducing the influence of historical data. By dynamically adjusting the noise covariance matrix and constraining the ratio between the error covariance and measurement noise covariance, the algorithm improves the convergence speed and accuracy of state estimation. An adaptive particle number and weight adjustment strategy based on effective sample size and entropy regularization dynamically adjusts the number of particles to reduce computational complexity while ensuring filtering accuracy, and employs entropy regularization to suppress weight over-concentration, thereby reducing the impact of outliers on the filtering results. Simulation results demonstrate that in complex SINS/GNSS integrated navigation systems, especially under high-noise and nonlinear conditions, the proposed algorithm significantly improves positioning accuracy compared to the CPF method, with the maximum latitude error reduced by approximately 54.8%, the maximum longitude error reduced by approximately 40.5%, and the maximum altitude error reduced by approximately 68.3%. Compared to the FMCPF method, the proposed algorithm also shows clear advantages in positioning accuracy, convergence speed, and computational efficiency, validating its effectiveness and practicality in complex environments. [ABSTRACT FROM AUTHOR]