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The aim of this paper was to investigate the effect of strain rate on microstructure and corrosion behavior of 2205 duplex stainless steel, after high-temperature compression tests.

The specimens were prepared using a Gleeble3800 thermo-simulation machine over a range of temperatures from 850 to 1,250°C and strain rates from 0.005 to 5 s−1, and the corresponding flow curves and deformation microstructure obtained were further analyzed. To evaluate the effect of strain rate on corrosion behavior, potentiodynamic polarization tests and double-loop electrochemical potentiodynamic reactivation (DL-EPR) were used to characterize the electrochemical performance.

Compared with strain rate of 0.5 s−1, the worst corrosion resistance behavior from the potentiodynamic polarization test results after deformation at 0.005 s−1 was attributed to more austenite (γ) and ferrite (δ) grain boundaries or δ/γ phase interface formation due to the better effect of γ dynamic recrystallization (DRX) or δ dynamic recovery (DRV). Increasing strain rate to 5 s−1 lowered the corrosion resistance, due to the increase in dislocation density. At the low strain rate of 0.005 s−1, the susceptibility to intergranular corrosion (IGC) was comparatively high after deformation at 1050 and 1150°C with more γ/γ grains and δ/γ phase boundary formation, which was lowered with the strain rate increase to 0.5 s−1, due to suppressing effect of γ DRX.

The paper provides the scientific basis for the practical application of hot working of 2205 duplex stainless steel.

The purpose of constructing a degradation index (DI) is to better characterize the degradation degree of mechanical transmission compared with relying solely on spectral oil data, which leads to an accurate estimation of the failure time when the transmission no longer fulfills its function.

The DI is modeled using a weighted average function with two desirable properties: maximizing the monotonic trend and minimizing the variance of failure threshold between different transmissions. The method includes concentration modification, data selection and data fusion steps that lead to a reasonable mechanical transmission degradation model. The proposed methodology was verified through a case study involving multispectral oil data sampled from several power-shift steering transmissions.

The results show that the DI outperforms all spectral oil data. Compared with the existing spectral oil data-based degradation modeling approach for mechanical transmissions, the present methodology provides an accurate RUL prediction.

There are several important directions for future research: First, more degradation data (i.e. ferrography) that are tailored to the degradation modeling of mechanical transmission need to be involved. Second, more effective degradation data selection methodologies that are applicable for multiple data types need to be developed. Third, kernel methods that can fuse the nonlinear degradation data need to be investigated.

The novelty of this methodology lies in integrating the multiple degradation data in a unified DI. And the main contribution of this paper is to establish a new direction in degradation modeling and RUL prediction of mechanical transmission.

Utilizing the extended transportation-imagery model, this study categorizes three storytelling elements into six distinct factors – character types, influencer-character congruence, imaginable titles, concrete details, replication difficulty and artistic processing – to explore how these factors enhance influencer engagement.

This study utilized a quantitative research design, analyzing 1,660 influencer-created videos over a six-month period. Narrative elements were examined through manual coding, and their impact on live comments was assessed using negative binomial regression to identify key factors driving audience engagement.

Research results show that non-fictional characters, imaginable titles and concrete details significantly increased live comments. Conversely, high replication difficulty negatively influenced engagement. Notably, influencer-character congruence and artistic processing showed no significant effect.

This study advances the extended transportation-imagery model by integrating narrative elements with live comments, offering new perspectives on real-time audience engagement. The findings deepen our understanding of how storytelling techniques enhance the effectiveness of influencer marketing. From a managerial standpoint, this research provides strategic insights for influencers and brands to refine their content strategies, ultimately boosting audience engagement.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

The present study is intended to develop an effective approach to the real-time modeling of general dynamic nonlinear systems based on the multidimensional Taylor network (MTN).

The authors present a detailed explanation for modeling the general discrete nonlinear dynamic system by the MTN. The weight coefficients of the network can be obtained by sampling data learning. Specifically, the least square (LS) method is adopted herein due to its desirable real-time performance and robustness.

Compared with the existing mainstream nonlinear time series analysis methods, the least square method-based multidimensional Taylor network (LSMTN) features its more desirable prediction accuracy and real-time performance. Model metric results confirm the satisfaction of modeling and identification for the generalized nonlinear system. In addition, the MTN is of simpler structure and lower computational complexity than neural networks.

Once models of general nonlinear dynamical systems are formulated based on MTNs and their weight coefficients are identified using the data from the systems of ecosystems, society, organizations, businesses or human behavior, the forecasting, optimizing and controlling of the systems can be further studied by means of the MTN analytical models.

MTNs can be used as controllers, identifiers, filters, predictors, compensators and equation solvers (solving nonlinear differential equations or approximating nonlinear functions) of the systems of ecosystems, society, organizations, businesses or human behavior.

The operating efficiency and benefits of social systems can be prominently enhanced, and their operating costs can be significantly reduced.

Nonlinear systems are typically impacted by a variety of factors, which makes it a challenge to build correct mathematical models for various tasks. As a result, existing modeling approaches necessitate a large number of limitations as preconditions, severely limiting their applicability. The proposed MTN methodology is believed to contribute much to the data-based modeling and identification of the general nonlinear dynamical system with no need for its prior knowledge.

As artificial intelligence technology empowers service robots, they increasingly communicate with consumers in a human-like manner. This study aims to investigate the effect of service robots’ different conversational styles (competent conversational style vs. cute conversational style) on consumer service acceptance and demonstrate the moderating role of consumers’ technology anxiety.

Based on anthropomorphism theory and social presence theory, the authors conducted two scenario-based experiments (restaurant scenario and hotel scenario) to investigate this issue.

The results indicate that service robots’ conversational styles impact consumers’ willingness to accept the use of service robots through perceived social presence and positive emotion. Moreover, consumers perceived social presence and positive emotion play a serial mechanism. In addition, the effect of competent conversational style on consumers perceived social presence is less effective than that of cute conversational style. Finally, the authors demonstrate the moderating role of consumer technology anxiety in the relationship between conversational styles and perceived social presence.

To provide consumers with a positive human–robot interaction experience at the service front line, managers need to make better use of the conversational styles of service robots by comprehensively considering the characteristics of consumer technology anxiety.

This research extends the literature on service robots by integrating consumer characteristics and robots’ conversational styles. These findings highlight the effectiveness of cute conversational style in alleviating consumer technology anxiety.

This paper aims to study and compare the friction stability of wet paper-based clutches with regard to the radial grooves (RG) and waffle grooves (WG).

This paper presents an experimental study of a wet clutch concerning the effect of groove patterns on the friction torque and surface temperature. The friction stabilities of RG and WG are investigated with the applied pressure, rotating speed and automatic transmission fluid (ATF) temperature taken into consideration.

The friction torque and surface temperature of WG are larger than those of RG under the same operating condition. The friction torque difference between RG and WG grows with the increase of applied pressure and narrows with the increase of ATF temperature. Additionally, their temperature difference expands via increasing the rotating speed and ATF temperature or reducing the applied pressure; in this way, not only the variable coefficient difference between RG and WG can be narrowed, but also the friction stability of the clutch can be improved dramatically.

This paper explains the thermodynamic differences between RG and WG; moreover, it is verified experimentally that WG has a better friction stability than RG.

The purpose of this paper is to investigate the friction and wear mechanisms in copper-based self-lubricating composites with MoS₂ as the lubricating phase, which provides a theoretical basis for subsequent research on high-performance copper-based self-lubricating materials.

Friction tests were performed at a speed of 100 r/min, a load of 10 N, a friction radius of 5 mm and a sliding speed of 30 min. Friction experiments were carried out at RT-500°C. The phase composition of the samples was characterized by X-ray diffraction of Cu Ka radiation, and the microstructure, morphology and elemental distribution were characterized by scanning electron microscopy and energy dispersive spectroscopy. Reactants and valences formed during the wear process were analyzed by X-ray photoelectron spectroscopy.

The addition of MoS₂ can effectively improve friction-reducing and anti-wear action of the matrix, which is beneficial to form a lubricating film on the sliding track. After analyzing different changing mechanism of the sliding tracks, the oxides and sulfides of MoS₂, MoO₂, Cu₂O, CuO and Ni(OH)₂ were detected to form a synergetic lubricating film on the sliding track, which is responsible for the excellent tribological properties from room to elevated temperature.

For self-lubrication Cu–Sn–Ni–MoS₂ material in engineering field, there are still few available references on high-temperature application.

This paper provides a theoretical basis for the following research on copper-based self-lubricating materials with high performance.

With this statement, the authors hereby certify that the manuscript is the results of their own effort and ability. They have indicated all quotes, citations and references. Furthermore, the authors have not submitted any essay, paper or thesis with similar content elsewhere. No conflict of interest exits in the submission of this manuscript.

This paper aims to investigate the thermal characteristics of the clutch hydraulic system under various oil flow conditions. Increasing the oil flow is one of the most important approaches to reduce the clutch temperature. However, the effect of the oil flow on the clutch temperature remains to be explored.

The thermal resistance network model and the lumped parameter method are used to study the thermal characteristics of the clutch hydraulic system. The predicted temperature variations of the clutch and the oil are compared with experimental data.

Results demonstrate that the larger the friction power is, the higher the temperatures of the clutch and the oil are. However, the temperature growth rates of the clutch and oil present different trends: the former decreases gradually and the latter increases constantly. Additionally, increasing the oil flow within a certain range gives rise to the decrease of clutch temperature and the increase of oil temperature; nevertheless, their variation trends are gradually weakening. When the oil flow is large enough, it brings a slight effect on the clutch temperature rise.

This paper extends the knowledge into the oil flow supply of the clutch hydraulic system. The conclusions can provide a theoretical guidance for the oil management of the transmission system. Additionally, the thermal resistance network model is also effective and efficient for other hydraulic equipment to predict the temperature variation.

Academic and industrial researches on nanoscale flows and heat transfers are an area of increasing global interest, where fascinating phenomena are always observed, e.g. admirable water or air permeation and remarkable thermal conductivity. The purpose of this paper is to reveal the phenomena by the fractional calculus.

This paper begins with the continuum assumption in conventional theories, and then the fractional Gauss’ divergence theorems are used to derive fractional differential equations in fractal media. Fractional derivatives are introduced heuristically by the variational iteration method, and fractal derivatives are explained geometrically. Some effective analytical approaches to fractional differential equations, e.g. the variational iteration method, the homotopy perturbation method and the fractional complex transform, are outlined and the main solution processes are given.

Heat conduction in silk cocoon and ground water flow are modeled by the local fractional calculus, the solutions can explain well experimental observations.

Particular attention is paid throughout the paper to giving an intuitive grasp for fractional calculus. Most cited references are within last five years, catching the most frontier of the research. Some ideas on this review paper are first appeared.