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The purpose of this study is to examine the impact of position adjusted turnover ratio on mutual fund performance.

The author calculates position adjusted turnover ratio in the same three steps as Edelen et al. (2013). Position adjusted turnover ratio is intended to be a trading cost proxy that captures both fund trading volume and per-trade costs. A metric of eight Morningstar performance measures is utilized.

Results show that funds with a higher position adjusted turnover ratio tend to have a lower risk-adjusted performance, such as indicated by both Sharpe and Sortino ratios, and even though these funds may have a higher annualized return.

The sample selection process is subject to a survival bias. Also, this study utilizes Morningstar performance measures rather than the widely used factors models.

This study examines the impact of invisible costs from fund trading. These findings encourage fund managers to take strategic steps to reduce the overall invisible cost impact to improve fund performance.

Few studies have investigated fund trading cost measured by position adjusted turnover ratio and its impact on fund performance. Further, this study contributes to current literature by using eight Morningstar fund performance variables, which are practitioner-oriented and are accessible by investors.

This study aims to investigate the differences in consumers’ perceptions of trust, performance expectancy and intention to hire between human financial advisors with high/low expertise and robo-advisors.

Three experiments were conducted. The respondents were randomly assigned to human advisors with high/low expertise or a robo-advisor. Data were analyzed using MANCOVA.

The results suggest that consumers prefer human financial advisors with high expertise to robo-advisors. There are no significant differences between robo-advisors and novice financial advisors regarding performance expectancy and intention to hire.

This pioneering study extends the self-service technology adoption theory to examine adoption of robo-advisors vs human financial advisors with different expertise levels. To the best of the authors’ knowledge, it is among the first studies to address multi-dimensionality of trust in the context of artificial intelligence-based self-service technologies.

The purpose of this study is to build a consensus model of social network group decision-making (SNGDM) based on improved PageRank algorithm. By objectively and fairly measuring the evaluation ability of participants in the decision-making process, the authors can improve the fairness and authenticity of the weight solution of decision-makers (DM) in the decision-making process. This ensures the reliability of the final group consensus results.

This study mainly includes six parts: preference expression, calculation of DM's weight, preference aggregation, consensus measurement, opinion adjustment and alternative selection. First, Pythagorean fuzzy expression is introduced to express the preference of DMs, which expands the scope of preference expression of DMs. Second, based on the social network structure among DMs, the process of “mutual judgment” among DMs is increased to measure the evaluation ability of DMs. On this basis, the PageRank algorithm is improved to calculate the weight of DMs. This makes the process of reaching consensus more objective and fair. Third, in order to minimize the evaluation difference between groups and individuals, a preference aggregation model based on plant growth simulation algorithm (PGSA) is proposed to aggregate group preferences. Fourth, the consensus index of DMs is calculated from three levels to judge whether the consensus degree reaches the preset value. Fifth, considering the interaction of DMs in the social network, the evaluation value to achieve the required consensus degree is adjusted according to the DeGroot model to obtain the overall consensus. Finally, taking the group preference as the reference, the ranking of alternatives is determined by using the Pythagorean fuzzy score function.

This paper proposes a consensus model of SNGDM based on improved PageRank algorithm to aggregate expert preference information. A numerical case of product evaluation is introduced, and the feasibility and effectiveness of the model are explained through sensitivity analysis and comparative analysis. The results show that this method can solve the problem of reaching consensus in SNGDM.

Different DMs may have different judgment criteria for the same decision-making problem, and the angle and depth of considering the problem will also be different. By increasing the process of mutual evaluation of DMs, the evaluation ability of each DM is judged only from the decision-making problem itself. In this way, the evaluation opinions recognized by most DMs will form the mainstream of opinions, and the influence of corresponding DMs will increase. Therefore, in order to improve the fairness and reliability of the consensus process, this study measures the real evaluation ability of DMs by increasing the “mutual judgment” process. On this basis, the defect of equal treatment of PageRank algorithm in calculating the weight of DMs is improved. This ensures the authenticity and objectivity of the weight of DMs. That is to improve the effectiveness of the whole evaluation mechanism. This method considers both the influence of DMs in the social network and their own evaluation level. The weight of DMs is calculated from two aspects: sociality and professionalism. It provides a new method and perspective for the calculation of DM’s weight in SNGDM.

The purpose of this research on the control of three-axis aero-dynamic pendulum with disturbance is to facilitate the applications of equipment with similar pendulum structure in intelligent manufacturing and robot.

The controller proposed in this paper is mainly implemented in the following ways. First, the kinematic model of the three-axis aero-dynamic pendulum is derived in state space form to construct the predictive model. Then, according to the predictive model and objective function, the control problem can be expressed a quadratic programming (QP) problem. The optimal solution of the QP problem at each sampling time is the value of control variable.

The trajectory tracking and point stability tests performed on the 3D space with different disturbances are validated and the results show the effectiveness of the proposed control strategy.

This paper proposes a nonlinear unstable three-axis aero-dynamic pendulum with less power devices. Meanwhile, the trajectory tracking and point stability problem of the pendulum system is investigated with the model predictive control strategy.

To predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research results can also provide references for railway departments to plan railway operation lines reasonably and efficiently.

This paper intends to establish a seasonal cycle first order univariate grey model (GM(1,1) model) combing with a seasonal index. GM (1,1) is termed as the trend equation to fit the railway passenger volume in China from 2014 to 2018. The railway passenger volume in 2019 is used as the experimental data to verify the forecasting effect of the proposed model. The forecasting results of the seasonal cycle GM (1,1) model are compared with the traditional GM (1,1) model, seasonal grey model (SGM(1,1)), Seasonal Autoregressive Integrated Moving Average (SARIMA) model, moving average method and exponential smoothing method. Finally, the authors forecast the railway passenger volume from 2020 to 2022.

The quarterly data of national railway passenger volume have a clear tendency of cyclical fluctuations and show an annual growth trend. According to the comparison of the modeling results, the authors know that the seasonal cycle GM (1,1) model has the best prediction effect with the mean absolute percentage error of 1.32%. It is much better than the other models, reflecting the feasibility of the proposed model.

As the previous grey prediction model could not solve the series prediction problem with seasonal fluctuation, and there are few research studies on quarterly railway passenger volume forecasting, GM (1,1) model is taken as the trend equation and combined with the seasonal index to construct a combination forecasting model for accurate forecasting results in this study. Besides, considering the impact of the epidemic on passenger volume, the authors introduce a disturbance factor to deal with the forecasting results in 2020, making the modeling results more scientific, practical and referential.

This study examines the relationship between streamers' product descriptions, customer comments and online sales and focuses on the moderating effect of streamer–viewer relationship strength.

Between June 2021 and April 2022, the structured data of 965 livestreaming and unstructured text data of 42,956,147 characters from two major live-streaming platforms were collected for the study. Text analysis and regression analysis methods were employed for data analysis.

First, the authors' analysis reveals an inverted U-shaped relationship between comment length and product sales. Notably, comment volume and comment emotion positively influence product sales. Furthermore, the semantic richness, emotion and readability of streamers' product descriptions also positively influence product sales. Secondly, the authors find that the strength of streamer–viewer relationship weakens the positive effects of comment volume and comment emotion without moderating the inverted U-shaped effect of comment length. Lastly, the strength of streamer–viewer relationship also diminishes the positive effects of emotion, semantics and readability of streamers' product descriptions on product sales.

This study is the first to concurrently examine the direct and interactive effects of user-generated content (UGC) and marketer-generated content (MGC) on consumer purchase behaviors in livestreaming e-commerce, offering a novel perspective on individual decision-making and cue utilization in the social retail context.

The purpose of this paper is to examine China's approach to circular economy (CE) and investigate how the foreign concept of CE has been turned into a national strategy for implementation in production, circulation and consumption. This study aims to highlight the Chinese characteristics in the implementation of CE from central to local levels including the “trial and test” by pilot schemes and the role of local governments in CE transformation of industrial parks and in building CE cities. Based on what has been achieved, this paper aims to identify the gaps to be filled in the next stage of CE implementation.

This paper engages in critical analysis of state policies, plans, laws and regulations and case studies of Suzhou New District and Shanghai city in the building CE-oriented industrial park and CE city, respectively.

China has taken a top-down approach to CE characterised by strong government involvement in both policy and plan making and implementation at local levels. The government’s financial investment and administrative assistance proved to be crucial in the early stage of CE implementation to close the loop at industrial parks and in cities. In comparison, participation by enterprises and individuals is still weak and limited, which should be the focus of the next stage of CE implementation.

There is an absence of legal literature that studies circular economy in China. This paper fills the gap by examining the development of CE law and policy as well as CE implementation at local levels from industrial parks to cities.

An ideal method for predicting the fatigue life of spherical thrust elastomeric bearings has not been reported, thus far. This paper aims to present a method for predicting the fatigue life of laminated rubber spherical thrust elastomeric bearings.

First, the mechanical properties of standard rubber samples were tested; the axial stiffness, cocking stiffness, torsional stiffness and fatigue life of several full-size spherical thrust elastomeric bearings were tested. Then, the stiffness results were calculated using the neo-Hookean, Mooney–Rivlin and Yoeh models. Using a modified Mooney–Rivlin constitutive model, this paper proposes an improved method for fatigue life prediction, which considers the laminated characteristics of a spherical thrust elastomeric bearing and loads of multiple multi-axle conditions.

The Mooney–Rivlin model could accurately describe the stiffness characteristics of the spherical thrust elastomeric bearings. A comparative analysis of experimental results shows that the model can effectively predict the life of a spherical thrust elastomeric bearing within its range of use and the prediction error is within 20%.

The fatigue parameters of elastomeric bearings under multiaxial loads were fitted and corrected using experimental data and an accurate and effective multiaxial fatigue-life prediction expression was obtained. Finally, the software was redeveloped to improve the flexibility and efficiency of modeling and calculation.

A radial offset jet has the flow characteristics of a radial jet and an offset jet, which are encountered in many engineering applications. The purpose of this paper is to study the dynamics and mass transfer characteristics of the radial offset jet with an offset ratio 6, 8 and 12.

Three turbulence models, namely the SST k-? model, detached eddy simulation model, and improved delayed detached eddy simulation (IDDES), were applied to the radial offset jet with an offset ratio eight and their results were compared with experimental results. The contrasting results, such as the distributions of mean and turbulent velocity and pressure, show that the IDDES model was the best model in simulating the radial offset jet. The results of the IDDES were analyzed, including the Reynolds stress, turbulent kinetic energy, triple-velocity correlations, vertical structure and the tracer concentration distribution.

In the axisymmetric plane, Reynolds stresses increase to reach a maximum at the location where the jet central line starts to be bent rapidly, and then decrease with increasing distance in the radial direction. The shear layer vortices, which arise from the Kelvin-Helmholtz instability near the jet exit, become larger scale results in the entrainment and vortex pairing, and breakdown when the jet approaches the wall. Near the wall, the vortex swirling direction is different at both front and back of attachment point. In the wall-jet region, the concentration distributions present self-similarity while it keeps constant below the jet in the recirculation region.

The radial offset jet with other offset ratio and exit angle is not considered in this paper and should be investigated.

The results obtained in this paper will provide guidance for studying similar flow and a better understanding of the radial offset jet.

This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.

In this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.

The results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.

The paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.