Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model

Short-term traffic flow prediction is one of the most important issues in the field of adaptive traffic control system and dynamic traffic guidance system. In order to improve the accuracy of short-term traffic flow prediction, a short-term traffic flow local prediction method based on combined kernel function relevance vector machine (CKF-RVM) model is put forward. The C-C method is used to calculate delay time and embedding dimension. The number of neighboring points is determined by use of Hannan-Quinn criteria, and the CKF-RVM model is built based on genetic algorithm. Finally, case validation is carried out using inductive loop data measured from the north–south viaduct in Shanghai. The experimental results demonstrate that the CKF-RVM model is 31.1% and 52.7% higher than GKF-RVM model and GKF-SVM model in the aspect of MAPE. Moreover, it is also superior to the other two models in the aspect of EC

Integration and learning: a case study of the international higher physical education talent-cultivation model

IntroductionThis study delves into the multifaceted components of talent-training models within China’s physical education domain through Sino-foreign cooperative initiatives. Employing a mixed-methods approach, it systematically evaluates the innovative systems developed by pilot units and outlines their experiential insights.MethodsUsing a mixed-methods approach, this research extensively evaluates the situation of pilot units by collecting and analyzing data from closed-ended and open-ended questionnaires as well as interview responses. The study categorizes and analyzes the data to comprehensively understand cooperative talent-training models.ResultsThe findings are classified into three main themes: Learning, Integration, and Binary Evaluation and Practice Reform. Under the Learning theme, the study observed a selective integration of foreign educational paradigms into the local context, respecting the distinctiveness of Chinese education and aligning with national policies promoting unique educational systems. Additionally, the Integration theme underscores the necessity of meticulously assimilating introduced educational resources into China’s educational fabric, highlighting the need for adaptability when integrating foreign educational elements. Furthermore, the Binary Evaluation and Practice Reform theme reveal the establishment of a dualistic evaluation and reform system tailored to cooperative education specifics, outlining challenges associated with ideological and cultural disparities when integrating certain foreign education aspects into the Chinese context.DiscussionThis research provides insightful exploration into the complexities of collaborative talent-training models in Physical Education. It not only elucidates the assimilation of foreign paradigms but also highlights nuanced challenges and prospects for developing tailored educational systems within specific regional and national contexts

Truncated eigenvalue equation and long wavelength behavior of lattice gauge theory

We review our new method, which might be the most direct and efficient way for approaching the continuum physics from Hamiltonian lattice gauge theory. It consists of solving the eigenvalue equation with a truncation scheme preserving the continuum limit. The efficiency has been confirmed by the observations of the scaling behaviors for the long wavelength vacuum wave functions and mass gaps in (2+1)-dimensional models and (1+1)-dimensional $\sigma$ model even at very low truncation orders. Most of these results show rapid convergence to the available Monte Carlo data, ensuring the reliability of our method.Comment: Latex file, 4 pages, plus 4 figures encoded with uufile

Urban Link Travel Time Estimation Based on Low Frequency Probe Vehicle Data

To improve the accuracy and robustness of urban link travel time estimation with limited resources, this research developed a methodology to estimate the urban link travel time using low frequency GPS probe vehicle data. First, focusing on the case without reporting points for the GPS probe vehicle on the target link in the current estimation time window, a virtual report point creation model based on the K-Nearest Neighbour Rule was proposed. Then an improved back propagation neural network model was used to estimate the link travel time. The proposed method was applied to a case study based on an arterial road in Changchun, China: comparisons with the traditional artificial neural network method and the spatiotemporal moving average method revealed that the proposed method offered a higher estimation accuracy and better robustness

Static magnetic order with strong quantum fluctuations in spin-1/2 honeycomb magnet Na2Co2TeO6

Kitaev interactions, arising from the interplay of frustration and bond anisotropy, can lead to strong quantum fluctuations and, in an ideal case, to a quantum-spin-liquid state. However, in many nonideal materials, spurious non-Kitaev interactions typically promote a zigzag antiferromagnetic order in the d-orbital transition metal compounds. By combining neutron scattering with muon-spin rotation and relaxation techniques, we provide new insights into the exotic properties of Na2Co2TeO6, a candidate Kitaev material. Below TN, the zero-field muon-spin relaxation rate becomes almost constant (at 0.45 us-1). We attribute this temperature-independent muon-spin relaxation rate to the strong quantum fluctuations, as well as to the frustrated Kitaev interactions. As the magnetic field increases, neutron scattering data indicate a much broader spin-wave-excitation gap at the K-point. Therefore, quantum fluctuations seem not only robust, but are even enhanced by the applied magnetic field. Our findings provide valuable hints for understanding the onset of the quantum-spin-liquid state in Kitaev materials.Comment: 28 pages, 11 figures, and 1 labl

The prognostic value of multiparametric cardiac magnetic resonance in patients with systemic light chain amyloidosis

BackgroundLate gadolinium enhancement (LGE) is a classic imaging modality derived from cardiac magnetic resonance (CMR), which is commonly used to describe cardiac tissue characterization. T1 mapping with extracellular volume (ECV) and native T1 are novel quantitative parameters. The prognostic value of multiparametric CMR in patients with light chain (AL) amyloidosis remains to be thoroughly investigated.MethodsA total of 89 subjects with AL amyloidosis were enrolled from April 2016 to January 2021, and all of them underwent CMR on a 3.0 T scanner. The clinical outcome and therapeutic effect were observed. Cox regression was used to investigate the effect of multiple CMR parameters on outcomes in this population.ResultsLGE extent, native T1 and ECV correlated well with cardiac biomarkers. During a median follow-up of 40 months, 21 patients died. ECV (hazard ratio [HR]: 2.087 for per 10% increase, 95% confidence interval [CI]: 1.379-3.157, P < 0.001) and native T1 (HR: 2.443 for per 100 ms increase, 95% CI: 1.381-4.321, P=0.002) were independently predictive of mortality. A novel prognostic staging system based on median native T1 (1344 ms) and ECV (40%) was similar to Mayo 2004 Stage, and the 5-year estimated overall survival rates in Stage I, II, and III were 95%, 80%, and 53%, respectively. In patients with ECV > 40%, receiving autologous stem cell transplantation had higher cardiac and renal response rates than conventional chemotherapy.ConclusionBoth native T1 and ECV independently predict mortality in patients with AL amyloidosis. Receiving autologous stem cell transplantation is effective and significantly improves the clinical outcomes in patients with ECV > 40%

LRR Conservation Mapping to Predict Functional Sites within Protein Leucine-Rich Repeat Domains

Computational prediction of protein functional sites can be a critical first step for analysis of large or complex proteins. Contemporary methods often require several homologous sequences and/or a known protein structure, but these resources are not available for many proteins. Leucine-rich repeats (LRRs) are ligand interaction domains found in numerous proteins across all taxonomic kingdoms, including immune system receptors in plants and animals. We devised Repeat Conservation Mapping (RCM), a computational method that predicts functional sites of LRR domains. RCM utilizes two or more homologous sequences and a generic representation of the LRR structure to identify conserved or diversified patches of amino acids on the predicted surface of the LRR. RCM was validated using solved LRR+ligand structures from multiple taxa, identifying ligand interaction sites. RCM was then used for de novo dissection of two plant microbe-associated molecular pattern (MAMP) receptors, EF-TU RECEPTOR (EFR) and FLAGELLIN-SENSING 2 (FLS2). In vivo testing of Arabidopsis thaliana EFR and FLS2 receptors mutagenized at sites identified by RCM demonstrated previously unknown functional sites. The RCM predictions for EFR, FLS2 and a third plant LRR protein, PGIP, compared favorably to predictions from ODA (optimal docking area), Consurf, and PAML (positive selection) analyses, but RCM also made valid functional site predictions not available from these other bioinformatic approaches. RCM analyses can be conducted with any LRR-containing proteins at www.plantpath.wisc.edu/RCM, and the approach should be modifiable for use with other types of repeat protein domains