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Currently, with the advancement of dual-clutch transmission (DCT) control systems and vehicle performance, it is necessary to develop better objective evaluation methods for DCT vehicles. The starting process is a critical element affecting the driving and riding experience of DCT vehicles. Therefore, it is crucial to establish and improve a starting process evaluation model for the objective evaluation to DCT vehicles and optimization to DCT control strategies. This paper proposes a new method to evaluate the DCT vehicle starting process objectively. The method analyzes and models the time-series signals of the driving data using the LSTM neural network and uses the attention mechanism to improve the evaluation performance and enhance the interpretability of the evaluation results. Taking the dynamic performance evaluation as an example, the evaluation results indicate that the proposed model is better than the conventional methods, showing notable efficacy and preponderance. [ABSTRACT FROM AUTHOR]

The unique features of the sulfenamides' S(II)-N bond lead to interesting stereochemical properties and significant industrial functions. Here we present a chemoselective Chan–Lam coupling of sulfenamides to prepare N-arylated sulfenamides. A tridentate pybox ligand governs the chemoselectivity favoring C–N bond formation, and overrides the competitive C-S bond formation by preventing the S,N-bis-chelation of sulfenamides to copper center. The Cu(II)-derived resting state of catalyst is captured by UV-Vis spectra and EPR technique, and the key intermediate is confirmed by the EPR isotope response using 15N-labeled sulfenamide. A computational mechanistic study reveals that N-arylation is both kinetically and thermodynamically favorable, with deprotonation of the sulfenamide nitrogen atom occurring prior to reductive elimination. The origin of ligand-controlled chemoselectivity is explored, with the interaction between the pybox ligand and the sulfenamide substrate controlling the energy of the S-arylation and the corresponding product distribution, in agreement with the EPR studies and kinetic results. Sulfenamides are a class of divalent sulfur-derived scaffolds featuring an S-N bond with many industrial applications, however, they are also challenging substrates for synthetic transformations. Here, the authors present a highly chemoselective ligand-controlled Chan–Lam coupling of sulfenamides as a straightforward route to N-arylated sulfenamides. [ABSTRACT FROM AUTHOR]

Voice coil motor (VCM) has obvious rate-dependent hysteresis characteristics, which means that when the frequency of the input signal changes, the travel distance and the shape of the hysteresis loop will change significantly. Based on the Nonlinear Auto-Regressive with Exogenous Inputs (NARX) neural network model, a rate-dependent hysteresis model consisting of a transfer function sub-model of the VCM and a NARX neural network sub-model is proposed for VCM in this paper. Different from the commonly used rate-dependent operator model, the proposed model has a relatively simple mathematic format. By introducing the transfer function of VCM, the initial prediction of the amplitude and phase shift is realized dynamically, which ensures the nonlinear fitting effect of the NARX neural network. Comparisons of the model responses with the measured data under different frequencies of input current signals indicate that the proposed model can dynamically describe the nonlinear rate-dependent hysteresis of VCM with very high accuracy. On this basis, the inverse model is designed by adopting the method of direct inverse, and the effectiveness of the inverse model in trajectory tracking is preliminarily verified by simulation. [ABSTRACT FROM AUTHOR]

Brain network analysis based on structural and functional magnetic resonance imaging (MRI) is considered as an effective method for consciousness evaluation of hydrocephalus patients, which can also be applied to facilitate the ameliorative effect of lumbar cerebrospinal fluid drainage (LCFD). Automatic brain parcellation is a prerequisite for brain network construction. However, hydrocephalus images usually have large deformations and lesion erosions, which becomes challenging for ensuring effective brain parcellation works. In this paper, we develop a novel and robust method for segmenting brain regions of hydrocephalus images. Our main contribution is to design an innovative inpainting method that can amend the large deformations and lesion erosions in hydrocephalus images, and synthesize the normal brain version without injury. The synthesized images can effectively support brain parcellation tasks and lay the foundation for the subsequent brain network construction work. Specifically, the novelty of the inpainting method is that it can utilize the symmetric properties of the brain structure to ensure the quality of the synthesized results. Experiments show that the proposed brain abnormality inpainting method can effectively aid the brain network construction, and improve the CRS-R score estimation which represents the patient's consciousness states. Furthermore, the brain network analysis based on our enhanced brain parcellation method has demonstrated potential imaging biomarkers for better interpreting and understanding the recovery of consciousness in patients with secondary hydrocephalus. [ABSTRACT FROM AUTHOR]

Required by the supercritical carbon dioxide (sCO2) coal-fired power cycle, sCO2 entering a boiler has a high temperature and can cause overheating of tubes. To eliminate the pressure drop penalty effect, the sCO2 boiler consists of several modules, each having different heat flux received from the furnace side (q) and different CO2 temperature in the cooling wall tube (Tf). We aim to search for the best matching strategy coupling furnace side and tube side to obtain the lowest temperature of tubes. By theoretically analyzing the wall temperature influenced by q, Tf and a comprehensive thermal resistance C, two matching methods are introduced: the heat flux-temperature matching (HTM) which matches higher q with lower Tf, and the heat flux-heat flux matching (HHM) that matches higher q with higher allowable-heat-flux at the temperature limit of tubes. HTM is a conventional method but HHM is newly proposed here. We show that, if C is identical for different modules, the two methods coincide; otherwise, HHM is recommended. For a sCO2 boiler driving 1000 MWe power plant, smaller cooling wall temperatures are obtained by HHM than HTM. Based on HHM, the mid-partition wall, heat transfer enhancement, and downward flow are comprehensively used, decreasing the wall temperature significantly. [ABSTRACT FROM AUTHOR]

A series of semimetal Bi-doped carbon fibers (Bi/CFs) were prepared by electrospinning-calcined polyacrylonitrile technique and characterized by XRD, SEM, EDS, XPS and UV–Vis DRS spectra to probe the morphology, structure and optical properties. Then, the Bi/CFs hybrids as photocatalysts were investigated the photocatalytic performances for degradation of representative environmental contaminants (Congo red dye and antibiotics sulfanilamide) under visible light irradiation (λ > 420 nm). In different compositions, the photocatalytic efficiencies have followed the order as Bi (5%)/CFs > Bi (10%)/CFs > Bi (1%)/CFs > polyacrylonitrile carbon fibers, that was mainly for the surface plasmon resonance effect of semimetal Bi, which made Bi/CFs hybrids had high light-harvesting properties and effective separation abilities of photogenerated carriers. According to the results of optical performance and the scavengers trapping experiments, the feasible photocatalytic mechanism was deduced. This study provided semimetal Bi, a new plasmonic co-catalyst, as a well substitute for Au and Ag noble metal to be a promising potential for environmental contaminants treatment. [ABSTRACT FROM AUTHOR]