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This paper presents a method to establish typical operating conditions for train traction systems by combining site-based short trip analysis with nonparametric tests, in order to overcome various obstacles, such as inadequate efficiency testing results based on rated points of train traction systems, which often fail to accurately reflect the actual energy efficiency level of systems during long-distance operation under multiple conditions, and the existing absence of typical operating conditions essential for supporting the evaluation of traction systems' energy efficiency levels and guiding the energy-saving design of products. First, short trips were categorized, screened and randomly combined. Then, the optimal short-trip combination was identified through nonparametric tests. Subsequently, the typical operating conditions were developed, taking the traction system of an electric locomotive model as a case study. Furthermore, the proposed method was verified by analyzing errors in operating characteristic parameters and assessing the similarities in velocity-force (V-F) distribution probability two-dimensional matrix. The verification results revealed a deviation between the constructed typical operating conditions and the characteristic values derived from sampled data at 4.54%, and the consistency in V-F distribution probabilities at 98.06%. These findings indicate that the method proposed in this paper accurately reflects the actual operating characteristics of train traction systems, with the established typical operating conditions closely aligning with the actual operating conditions of train traction systems, underscoring their potential for aiding in energy-saving design and energy efficiency evaluation for train traction systems.

Temperature rise measurement is crucial for evaluating the performance of motors and ensuring their safe operation. In order to more effectively, accurately, and scientifically evaluate temperature rise measurement results for traction motors, the incorporation of the measurement uncertainty concept holds practical significance. This paper proposes a method for evaluating uncertainties in temperature rise measurement of traction motors based on technical specifications outlined in JJF 1059.1-2012 Evaluation and expression of uncertainty in measurement. The current study started with a comprehensive analysis of uncertainty sources in temperature rise measurement results of traction motors using the standard IEC 60349-2:2010 Electric traction-Rotating electrical machines for rail and road vehicles-Part 2: Electronic converter-fed alternating current motors. Subsequently, an uncertainty evaluation model was constructed, and practical examples were used to evaluate measurement uncertainties, specifically involving standard uncertainty evaluation and calculations of uncertainties in synthesis and extension. The results showed that, with a confidence probability of 95%, the motor temperature rise measurement result stood at 79.22 K, containing an extension uncertainty of 1.30 K, which quantitatively characterized the quality of the test results. The proposed method provides a technical basis for determining the confidence level of temperature rise measurement results for rolling stock traction motors, while offering guidance for evaluating uncertainties in temperature rise measurement for motor winding, especially concerning curve fitting using the least squares method.

The design and safe operation of nuclear reactor are inseparable from high-precision and highresolution simulation program. With the development of calculation methods and the progress of computer hardware, highfidelity numerical reactor technology becomes a research hotspot. However, due to the complexity of the objective world and the limitation of human cognitive level, there is uncertainty in any measurement and mathematicalphysical modeling process. Therefore, the simulation results need to provide the corresponding uncertainty as the confidence interval to meet the requirements of high confidence in numerical reactor technology. In order to promote the development of sensitivity and uncertainty analysis of reactor system, OECD/NEA (Organization for Economic Cooperation and Development/Nuclear Energy Agency) issued the UAMLWRs (uncertainty analysis in modelling of light water reactors) benchmark. The exercises are carried out in 3 phases that is neutronics phase, core phase and system phase. In the neutronics phase, the uncertainty of nuclear data is transferred from the evaluation nuclear database files (ENDF) to multigroup micro sections, and then to macro fewgroup sections and key parameters of steadystate core calculation. The core phase includes three independent physical processes which is fuel performance analysis, timedependent neutronics and bundle thermalhydraulic. The system phase includes multiphysics coupling calculation, and finally gives the best estimation plus uncertainty (BEPU) result. In order to provide confidence interval while giving numerical simulation results, the uncertainty quantification of fuel performance, assembly burnup and thermalhydraulic analysis was carried out based on the second phase (core phase) of UAMLWRs benchmark. Using the uncertainty analysis platform NECPUNICORN independently developed by Xi’an Jiaotong University, the light water reactor fuel analysis code FEMAXI, the lattice physics program NECPBambooLattice and the subchannel thermalhydraulic code CTF were coupled respectively. Firstly, according to the characteristics of different physical processes, the sources of uncertainty to be considered were analyzed. Then, according to the characteristics of sparse and unfilled rank covariance matrix of nuclear data, COST method was applied to reduce the sample size. It is found that for fuel performance analysis, boundary conditions, geometric parameters and material properties have significant effects on the uncertainty of fuel center temperature. For the burnup process, the nuclear data and manufacturing parameters have a significant impact on the uncertainty of eigenvalues, power distribution, twogroup constants and nucleon density. For thermal hydraulic analysis process, boundary conditions, geometric parameters and model coefficients have a great influence on the uncertainty of coolant and cladding temperature. For each physical field, quantifying the uncertainty of its input and output parameters is of great significance for quantifying the uncertainty of multi-physical coupling process of complex system.

Phase recognition plays an essential role for surgical workflow analysis in computer assisted intervention. Transformer, originally proposed for sequential data modelling in natural language processing, has been successfully applied to surgical phase recognition. Existing works based on transformer mainly focus on modeling attention dependency, without introducing auto-regression. In this work, an Auto-Regressive Surgical Transformer, referred as ARST, is first proposed for on-line surgical phase recognition from laparoscopic videos, modeling the inter-phase correlation implicitly by conditional probability distribution. To reduce inference bias and to enhance phase consistency, we further develop a consistency constraint inference strategy based on auto-regression. We conduct comprehensive validations on a well-known public dataset Cholec80. Experimental results show that our method outperforms the state-of-the-art methods both quantitatively and qualitatively, and achieves an inference rate of 66 frames per second (fps). [ABSTRACT FROM AUTHOR]

Background: The composition of glutenin protein significantly affects protein–starch interactions and starch digestion characteristics in wheat dough matrices. To elucidate the effects of high molecular weight glutenin subunits at the Glu‐B1 locus on dough processing quality, the detailed structural changes of protein, starch, and their complexes were compared in Mixolab dough samples of two near isogenic lines 7 + 8 and 7 + 9. Results: The results showed that the degree of protein aggregation increased continuously during dough processing, as did the destruction and rearrangement of the gluten network. Compared to 7 + 8, the stronger and more stable protein network formed in 7 + 9 dough induced intensive interactions between protein and starch, primarily through hydrogen bonds and isomeric glycosidic bonds. In 7 + 9 dough, the more compact and extensive protein–starch network significantly inhibited starch gelatinization during dough pasting, while during the dough cooling stage [from C4 (82.8 °C) to C5 (52.8 °C)], more protein–starch complexes composed of monomeric proteins and short‐chain starch were generated, which remarkably inhibited starch retrogradation. All protein–starch interactions in the 7 + 9 dough improved the starch digestion resistance, as reflected by the high content of resistant starch. Conclusion: The more extensive and intensive protein–starch interactions in the 7 + 9 dough inhibited the gelatinization and enzymatic hydrolysis of starch, thereby producing more slowly digestible starch and resistant starch. These findings demonstrate the feasibility of optimizing the texture and digestibility of wheat‐based food products by regulating the behavior and interactions of proteins and starch during dough processing. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

A spectral collocation approach based on integrated polynomials is presented to investigate the statics and free vibrations of Euler–Bernoulli beams with axially variable cross section, modulus of elasticity, and mass density. The basic concept of the approach is the expansion of the highest derivatives appearing in the governing equations instead of the solution function itself by the truncated basis function. Then lower order derivatives and the function itself are obtained by integration. The constants appearing from the integrating process are determined by given classical or elastic restrained boundary conditions. Also, by incorporating the decomposition technique into the present approach, higher order vibration modes can be achieved even for stepped beams. Numerical examples including the statics and free vibrations of the beams with variance in geometry or material have been successfully solved, and the results are compared with those analytical or numerical solutions in the existing literature. The convergence and comparison studies show that convergent speed is rather rapid and the present approach can yield high accurate results with low computational efforts. Furthermore, the accuracy is not particularly affected by the adopted polynomials. [ABSTRACT FROM AUTHOR]

Action surface electromyography (SEMG) signals can be acquired from human skin surface. Its pattern recognition plays a very important role in practical applications such as human prosthesis and human-computer interface systems. For the purpose of increasing the recognition accuracy, we proposed a new recognition method combining fuzzy entropy (FuzzyEn) with multi-scale analysis. Considering the nonlinear and non-stationary characteristics of the SEMG, a multi-scale fuzzy entropy (MSFuzzyEn) feature was introduced and applied to the pattern recognition of six type action SEMG signals of the forearm. Firstly, multi-scale decomposition was applied to original signal using wavelet decomposition. Then MSFuzzyEn of the decomposed signals were calculated and inputted to support vector machine (SVM) for classification as feature vectors. The mean recognition accuracy reached 97%, which was 3% greater than that when FuzzyEn of original signal is applied to the classification of SEMG signals. The results have proved that the MSFuzzyEn is effective and precise in the classification of action SEMG signals.