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The asymmetry of the velocity profile caused by geometric deformation, complex turbulent motion and other factors must be considered to effectively use the flowmeter on any section. This study aims to better capture the flow field information and establish a model to predict the profile velocity, we take the classical double elbow as the research object and propose to divide the flow field into three categories with certain common characteristics.

The deep learning method is used to establish the model of multipath linear velocity fitting profile average velocity. A total of 480 groups of data are taken for training and validation, with ten integer velocity flow fields from 1 m/s to 10 m/s. Finally, accuracy research with relative error as standard is carried out.

The numerical experiment yielded the following promising results: the maximum relative error is approximately 1%, and in the majority of cases, the relative error is significantly lower than 1%. These results demonstrate that it surpasses the classical optimization algorithm Equal Tab (5%) and the traditional artificial neural network (3%) in the same scenario. In contrast with the previous research on a fixed profile, we focus on all the velocity profiles of a certain length for the first time, which can expand the application scope of a multipath ultrasonic flowmeter and promote the research on flow measurement in any section.

This work proposes to divide the flow field of double elbow into three categories with certain common characteristics to better capture the flow field information and establish a model to predict the profile velocity.

The purpose is to predict the distribution of the residual pretightening force of the bolt group under the action of any initial pretightening force, and to achieve the final residual pretightening force as the target to solve the initial pretightening force value to be applied.

Based on the finite element method and the elastic interaction theory between bolt group, this paper establishes a prediction model for the residual pretightening force distribution of bolt group for one-step pretightening and multi-step pretightening of gasketless flange connection systems. In addition, using the general modeling method given in this paper, the prediction model of residual pretightening force of long plate bolt connection system is established, and compared with reference, which fully proves the effectiveness and universality of the general prediction model of residual pretightening force of bolt group.

The appropriate pretightening sequence, increasing the number of pretightening steps and variable amplitude loading can effectively reduce the influence of elastic interaction and improve the uniformity of residual pretightening force of the bolt group. And the selection of material, number of bolts and connected thickness of bolt connection system also has a great influence on the distribution of residual pretightening force of bolt groups.

The general prediction model for the residual pretightening force of bolt group of connecting structural components considering elastic interaction given in this paper can provide a reference for the design and optimization of the bolt assembly process of the rotor system and the casing system in aero-engine and the prediction of the performance of the connecting system.

This paper aims to classify the inductorless Chua’s circuits into two types from the topological structures and construct a chaotic circuit under this new classification framework.

In this paper, two types of inductorless Chua’s circuit models are presented from topological structure, among which the first type of inductorless Chua’s circuit (FTICC) model is much closer to the original Chua’s circuit. Under this classification framework, a new inductorless Chua’s circuit that belongs to the FTICC model is built by replacing LC parallel resonance of the original Chua’s circuit with a second order Sallen–Key band pass filter.

Compared with a paradigm of a reported inductorless Chua’s circuit that belongs to the second type of inductorless Chua’s circuit (STICC) model, the newly proposed circuit can present the attractors which are much more closely to the original Chua’s attractors. The dynamical behaviors of coexisting period-doubling bifurcation patterns and boundary crisis are discovered in the newly proposed circuit from both numerical simulations and experimental measurements. Moreover, a crisis scenario is observed that unmixed pairs of symmetric coexisting limit cycles with period-3 traverse through the entire parameter interval between coexisting single-scroll chaotic attractors and double-scroll chaotic attractor.

The newly constructed circuit enriches the family of inductorless Chua’s circuits, and its simple topology with small printed circuit board size facilitates the various types of engineering applications based on chaos.

This work describes the fabrication of composite nanogrease based on carbon nanotubes (CNTs) as an additive at different volume concentrations 0, 0.5, 1, 2 and 3 Wt.% and investigates the correlation between CNTs and grease rheological behaviour. In addition, study the influence of shear thinning rate at various temperatures and investigates the thermal conductivity of nanogrease. The results demonstrated that grease behaves like a Newtonian viscoelastic material with a narrow linear domain. The thermal conductivity of nanogrease was enhanced by about 31.58 per cent, and the thermal and mechanical stabilities improved. Moreover, the apparent viscosity and dropping point increased by about 93 and 27 per cent, respectively.

Grease was dissolved in chloroform (10 Wt.%), at 25°C for 1 h. In parallel, functionalized CNTs with different volume concentrations (0.5, 1, 2 and 3 Wt.%) were dispersed in N,N-dimethylformamide; the dispersion was stirred for 15 min, and then sonicated (40 kHz, 150 W) for 30 min. Grease solution was then added to the CNTs. The nanofluid was magnetically stirred for 15 min and then sonicated for 2 h. This ensured uniform dispersion of nanoparticles in the base fluid.

Inexpensive and simple fabrication of nanogrease. Thermal conductivity of nanogrease was typically enhanced compared to other reported studies. Apparent viscosity and dropping point increases with the increase the volume concentration.

This work describes the inexpensive and simple fabrication of nanogrease for improving properties of lubricants, which improve power efficiency and extend lifetimes of mechanical equipment.

Corporate investment behavior increases the uncertainty of a company’s operation and performance. The purpose of this paper is to investigate how analyst recommendations respond to corporate uncertainty caused by investment behavior and what motivates analysts to react as they do.

The authors test two motivation hypotheses: the hypothesis that analysts are currying favor with management to obtain private information and the hypothesis that analysts have conflicts of interest due to connections. Using Chinese analyst-level data from 2007 to 2015, the authors find that overall investment levels, R&D investment and M&A events are significantly positively correlated with analyst recommendations, suggesting that analysts tend to react optimistically to corporate investment behavior.

Analysts are only optimistic about companies with low information transparency, suggesting that analysts may be trying to curry favor with management to gain access to private information. The authors find that analysts with stronger recommendations have more private information and analysts with more private information publish more accurate earnings forecasts, which supports the hypothesis that analysts curry favor with management through optimistic recommendations to obtain more private information. This is consistent with the logic that the difficulty of earnings forecasting increases under uncertain conditions, increasing the demand for private information. The authors then group the analysts according to their underwriting connections, securities company’s proprietary connections and fund connections, and find that the positive correlation between corporate investment behavior and analyst recommendations exists only in the unconnected groups. This is evidence against the hypothesis that analysts have conflicts of interest due to their connections.

First, the authors link the optimism of analysts with the uncertainty of analysts’ information inputs to partially unpack the black box of analysts’ analyses. Second, the authors test the two hypotheses mentioned. There is a lack of comparative studies on the influence of different motivations on the behavior of analysts.

All that we know about the Chief Executive Officer (CEO) labour market in China comes from the studies of public listed companies and State-owned Enterprises (SOEs). This is the first attempt to examine the operation of the CEO labour market across all industrial sectors of the Chinese economy. We find that the influence of the State extends beyond SOEs into many privately owned firms. Government is often involved in CEO appointments in domestic firms and, when this is the case, the CEO has less job autonomy and is less likely to have pay linked to firm performance. Nevertheless, we find that incentive schemes are commonplace and include contracts linking CEO pay directly to firm performance, annual bonus schemes, the posting of performance bonds, and holding company stock. The elasticity of pay with respect to company performance is one or more in two-fifths of the cases where CEOs have performance contracts, suggesting many face high-powered incentives. We also show that State-owned and domestic privately owned firms are more likely than foreign-owned firms to use incentive contracts.