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In this study, specific measures adopted by the social media platforms in China supporting personal information management are investigated via surveys targeting such platforms. The purpose of this paper is to find out how social media platforms understand information management, and from which aspects and through what specific methods they provide support for information management, which contributes to understanding the issues and strategies associated with personal information management on social media.

The dimensions and specific contents of the current platform support provided for information management are clearly defined by performing qualitative text analysis based on the content obtained from 11 platform policies published by five representative Chinese social media platforms.

How social media platforms support personal information management on creation, collection, utilisation, sharing, storage, protection, removal and modification is identified. By analysing the status quo of support provided by the Chinese social media platform, some issues are proposed for discussion. Improved normative management is required to address the coexistence of multivalued information and management risks. However, the user rights are limited because the platform policies tend to be more focused on the perspective of the social media platform. Furthermore, the platform policy contents regarding information management are incomplete, and the applicability of these policies should be improved.

This study seeks to contribute to personal information management on social media from the perspective of platform support. The perspective from the platforms as the service providers supporting information management also helps identify information management challenges and potential strategies. Furthermore, combining with the personal information management perspective, this study provides a background understanding of information management under a social collaborative framework for platforms, authorities, users and memory institutions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2020-0249

This paper aims to address the issue of model discontinuity typically encountered in traditional Denavit-Hartenberg (DH) models. To achieve this, we propose the use of a local Product of Exponentials (POE) approach. Additionally, a modified calibration model is presented which takes into account both kinematic errors and high-order joint-dependent kinematic errors. Both kinematic errors and high-order joint-dependent kinematic errors are analyzed to modify the model.

Robot positioning accuracy is critically important in high-speed and heavy-load manufacturing applications. One essential problem encountered in calibration of series robot is that the traditional methods only consider fitting kinematic errors, while ignoring joint-dependent kinematic errors.

Laguerre polynomials are chosen to fitting kinematic errors and high-order joint-dependent kinematic errors which can avoid the Runge phenomenon of curve fitting to a great extent. Levenberg–Marquard algorithm, which is insensitive to overparameterization and can effectively deal with redundant parameters, is used to quickly calibrate the modified model. Experiments on an EFFORT ER50 robot are implemented to validate the efficiency of the proposed method; compared with the Chebyshev polynomial calibration methods, the positioning accuracy is improved from 0.2301 to 0.2224 mm.

The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed calibration methods on an industrial serial robot.

Structural stress and strain in the key components of aircraft structure is important for structural health monitoring and strength assessment. However, the measure of dynamic strain is often difficult to implement because of the complex test equipment and inconvenient measure points, especially in flight test. This study aims to propose an algorithm of dynamic strain estimation using the acceleration response in time domain to simplify the measure of dynamic strain.

The relationship between the strain and acceleration response is established through the sinusoidal response or modal analysis, which is insensitive to the excitation position and form. A band-pass filter is used to obtain the modal acceleration response, and a filter frequency band selection method is proposed. Then, the dynamic strain at the concerned points can be estimated based on the modal superposition principle.

Simulation and experiment are implemented to validate the applicability and effectiveness of the strain estimation method. The estimated strain results agree well with numerical simulation as well as the experimental results. The simplicity and accuracy of the strain estimation method show practicability for dynamic strength and fatigue analysis in engineering applications.

An algorithm of dynamic strain estimation using the acceleration response in time domain is developed. A band-pass filter is used to obtain the modal acceleration response, and a filter frequency band selection method is proposed. The dynamic strain at the concerned points can be estimated based on the modal superposition principle.

This paper aims to clarify the semi-conductive behavior of the passive layer formed in concrete environment without and with presence of chloride ions under different loading conditions. Passivation and depassivation of steel play an essential role in the subsequent stages of the corrosion process. Due to the nature of passive films on metals, they show electrochemical properties of a semi-conductor.

A C-ring model was proposed in this experiment to induce stress on the specimens. Specimens under different levels of compressive and tensile loadings were exposed to chloride-free and chloride-contaminated solutions and their semi-conductive behavior was investigated using Mott–Schottky technique.

Irrespective of the type and magnitude of the applied load, the passive film on rebars in simulated concrete pore solution is a highly disordered n-type semi-conductor. In all specimens, the presence of chloride ions decreases the slope of the Mott-Schottky plots, the donor density and the space charge layer thickness, which leads to a thinner passive film. Results indicate that steel specimens immersed in chloride-free pore solution under tensile loadings passivate more rapidly compared to those under compressive loadings. However, the situation in chloride-contaminated solution is different, and steel under tensile stress exhibits more corrosion than steel under compressive stress or under no load.

Reinforced concrete structures inevitably experience variable mechanical loads, and continuous degradation from aggressive environments. Therefore, it is imperative to study the synergic impact of different types of mechanical loadings and exposure to chloride ions on this process. This paper fulfils this need.

The resolution of electroencephalography (EEG) forward problem by the finite element method (FEM) involves the modeling of current dipoles with the singularities. The purpose of the paper is to investigate the accuracy issue of the two alternative methods, the direct method and the subtraction method for the modeling of current dipoles.

Finite element modeling of current dipoles using the direct method and the alternative implementations of the subtraction method.

The accuracy and the performance of different methods are compared through a four-layer spherical head model with available analytical solution. Results show that the subtraction method involving only the surface integrals provides the best accuracy.

The subtraction method removes the difficulty of modeling the singularity of current dipoles but the accuracy depends on the implementation.

This study aims to explore how specific cues with new manifestations (i.e. herding message and price discount information) and customer cognitive style influence attention allocation and purchase intention.

To empirically validate the research hypotheses, an eye-tracking experiment with a 2 × 2 × 2 mixed design was conducted on a sample of 44 participants recruited from a university in China. Repeated measures analysis of variance was employed for data analysis.

The results show that herding message and price discount information play different roles in viewers' attention and have an interactive effect on attention. Moreover, individual cognitive styles moderate the impact of herding message on attention allocation. Still, two cues positively affect customer purchase intention.

This study guides future research by applying cue utilization theory to investigate the effects of two cues in live streaming. Findings offer practical implications for how live streaming cues affect viewers' attention allocation and purchase intention.

This paper aims to propose a fast machine compression scheme, which can solve the problem of low-bandwidth transmission for underwater images.

This fast machine compression scheme mainly consists of three stages. Firstly, raw images are fed into the image pre-processing module, which is specially designed for underwater color images. Secondly, a divide-and-conquer (D&C) image compression framework is developed to divide the problem of image compression into a manageable size. And extreme learning machine (ELM) is introduced to substitute for principal component analysis (PCA), which is a traditional transform-based lossy compression algorithm. The execution time of ELM is very short, thus the authors can compress the images at a much faster speed. Finally, underwater color images can be recovered from the compressed images.

Experiment results show that the proposed scheme can not only compress the images at a much faster speed but also maintain the acceptable perceptual quality of reconstructed images.

This paper proposes a fast machine compression scheme, which combines the traditional PCA compression algorithm with the ELM algorithm. Moreover, a pre-processing module and a D&C image compression framework are specially designed for underwater images.

This paper aims to understand the influence of cylinder liner temperature on friction power loss of piston skirts and the synergistic effect of cylinder liner temperature on lubrication and heat transfer between piston skirt and cylinder liner.

A method to calculate the influence of cylinder liner temperature on piston skirt lubrication is proposed. The lubrication is calculated by considering the different temperature distribution of the cylinder liner and corresponding piston temperature calculated by a new multilayer thermal resistance model. This model uses the inner surface temperature of the cylinder liner as the starting point, and the starting temperature corresponding to different positions of the piston is calculated using the time integral average. Besides, the transient heat transfer of mixed lubrication is taken into account. Six temperature distribution schemes of cylinder liner are designed.

Six temperature distributions of cylinder liner are designed, and the maximum friction loss is reduced by 34.4% compared with the original engine. The increase in temperature in the second part of the cylinder liner will lead to an increase in friction power loss. The increase of temperature in the third part of the cylinder liner will lead to a decrease in friction power loss. The influence of temperature change in the third part of the cylinder liner on friction power loss is greater than that in the second part.

The influence of different temperature distribution of cylinder liner on the lubrication and friction of piston skirt cylinder liner connection was simulated.

In this paper the authors aim to argue that the existence of a strong corporate governance mechanism (a formal credibility-enhancing mechanism) and the presence of a more trustworthy-looking CEO (an informal credibility-enhancing mechanism) are substitutes.

By using machine-learning-based facial-feature-point detection technique, the authors construct a proprietary facial-trustworthiness database for a large-scale of CEOs in the US listed companies. First, the authors manually search for qualifying CEO image from websites and annual reports. Second, by following the neuroscience and psychology literature, the authors use the machine-learning-based face detector to identify the facial features in the CEO photos to calculate a rich and reliable set of facial-trustworthiness measures. The authors then construct a composite facial-trustworthiness index for each CEO. After obtaining accounting data, the authors’ final sample comprises 16,201 firm-year observations for 3,186 CEOs in the sample period of 2000-2018.

The results of the authors’ regression analyses show a negative association between board monitoring intensity and CEOs' facial trustworthiness, indicating that board directors may factor CEOs' facial trustworthiness into their monitoring decisions. Moreover, the authors find that these results are mainly driven by CEOs whose tenure is below the third quartile (i.e. eight years). The authors further find stronger results for externally hired CEOs than internally promoted CEOs. Finally, the authors’ results remain robust when using change models or subsample of CEO photos in recent years.

First, to the best of the authors’ knowledge, this is the first study that adopts a large sample to provide systematic evidence on the directors' use of facial trustworthiness. This study extends the literature by documenting the impacts of CEOs' individual characteristics on the board monitoring intensity. Second, the results of this study emphasized the important role of perceptions based on executives' facial appearance in firm valuation, executive compensation and audit fee, and by presenting empirical evidence that CEOs' facial trustworthiness affects board monitoring intensity. Third, this study responds to the call for research on personalized trust by Hsieh et al. (2020).

Because of the multiple design elements and complicated relationship among design elements of complex products design, it is tough for designers to systematically and dynamically express and manage the complex products design process.

To solve these problems, a supernetwork model of complex products design is constructed and analyzed in this paper. First, the design elements (customer demands, design agents, product structures, design tasks and design resources) are identified and analyzed, then the sub-network of design elements are built. Based on this, a supernetwork model of complex products design is constructed with the analysis of the relationship among sub-networks. Second, some typical and physical characteristics (robustness, vulnerability, degree and betweenness) of the supernetwork were calculated to analyze the performance of supernetwork and the features of complex product design process.

The design process of a wind turbine is studied as a case to illustrate the approach in this paper. The supernetwork can provide more information about collaborative design process of wind turbine than traditional models. Moreover, it can help managers and designers to manage the collaborative design process and improve collaborative design efficiency of wind turbine.

The authors find a new method (complex network or supernetwork) to describe and analyze complex mechanical product design.