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This paper aims to examine how channel whistleblowing intensity affects a distributor’s compliance to the manufacturer’s request and how that impact is influenced by institutional environments.

Based on paired survey data, which was collected from an automobile manufacturer in China and its 211 distributors, combined with secondary data, this study used hierarchical regression analyses to test the hypotheses.

The study finds that channel whistleblowing intensity has an inverted U-shaped effect on distributor compliance. In addition, this curvilinear effect is stronger in regions with more effective legal systems and higher social trust, but the authors do not find perceived vertical control moderating the effect of whistleblowing intensity on distributor compliance.

First, this study enriches the marketing literature by highlighting the significance of whistleblowing and especially its downside in marketing channel management. Second, moving beyond prior marketing studies’ focus on bilateral controls, it recognizes channel whistleblowing as a peer-enforced control mechanism. Third, it identifies environmental factors as shift parameters that alter the impact of channel whistleblowing, attesting to the importance of “discriminating alignment.”

The findings caution channel managers against the double-edged effects of whistleblowing and inform the conditions that amplify this impact.

This work highlights the bright and dark sides of channel whistleblowing and uncovers situations in which it works or fails to promote distributor compliance.

The purpose of this paper is to develop a new vibration probe sensor for measurement of particle mass flow rate in gas–solid two phase flow.

A new vibration probe sensor based on polyvinylidene fluoride (PVDF) piezoelectric film is designed. The particle impact model according to Hertz contacting theory is presented. The average amplitude, standard deviation and spectral peak at the natural frequency of the probe (21.2 kHz) of the signals acquired through experiments are chosen as characteristic quantities for further analysis.

Through experimental study of relation between three characteristic quantities and the mass flow rate and air flow velocity, a good regularity is found in the average amplitude and the spectral peaks at natural frequency of the probe. According to the particle impact model, the structure of quantitative model is built and parameters of two models are calculated from experimental data. Additionally, tests are made to estimate mass flow rate. The average errors are 5.85 and 4.26 per cent, while the maximum errors are 10.81 and 8.65 per cent. The spectral peak at natural frequency of the probe is more applicable for mass flow rate measurement.

The sensor designed and the quantitative models established may be used in dilute phase pneumatic conveying lines of coal-fired power plants, cement manufacturing facilities and so on.

First, the new sensor is designed and the quantitative models are established. Second, the spectral peak at natural frequency of the probe is found that can be used for measurement of mass flow rate.

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

This paper summarizes the design thinking of pleated clothing, uses virtual software to simulate the pleated clothing process and clothing effect and applies the fractal geometry principle to conduct relevant experiments, which verifies the possibility of simulation software assisting pleated clothing design, improves research and development efficiency, lays a foundation for future process parameter collection to upgrade the pleated clothing database and realizes the diversity of digital innovative design of pleated clothing.

According to the principle of self-similarity and fractal dimension in fractal geometry, the design thinking of pleated clothing is put forward and the simulation folding of 3D digital technology is expanded. The design method takes traditional craft and digital technology as an example. The two-dimensional design thinking of pleated clothing is geometric crease thinking of fabric swatch and geometric expansion of basic clothing pattern. Three-dimensional modeling design thinking, such as based on the human body shape, geometric shape and folding mode. As well as digital design thinking, such as the angle of the fabric element and the perspective of garment pattern change.

This paper analyzes the Y structure of the similar shape “binary tree” in origami art, the principle of infinite folding with 45? As the unit and the point set capacity theory of Cantor concentric circles in the fractal dimension principle expands the design thinking of pleated clothing, applies a large number of experimental cases of design thinking inspired by the fractal geometry of pleated clothing and validates the possibility of simulation software assisting pleated clothing design.

We argue here that digital technology should continue to be used in a similar way for research and development in other clothing design-related processes in the future. When it comes to the possibilities for digital innovation in relation to pleated clothing, there is a lack of data for modeling the characteristics of such clothing and establishing the processing requirements. Thus, there is scope here for innovation breakthroughs in terms of both the art and the technology associated with clothing design, not to mention how the two are integrated. This paper has laid some potential foundations for this through its elaboration of fractal-inspired design in the context of pleated garments.

Numerous experiments were undertaken and digital simulation software was used to explore various potential designs in the paper. This confirmed the possibility of using simulation software to assist in the design of pleated clothing, which offers the scope for improved research and more efficient development. However, if the processing parameters for changes in the pleated fabric are not set in the software, it is impossible to automatically generate auxiliary clothes. The collection of processing parameters and the creation of a more comprehensive pleated clothing database should therefore be the focus of future research.

The paper is oriented toward future clothing design. There is some impact on research, practice and/or society. This opens up the possibility of using fractal geometry in the design of the folds and dimensional energy in pleated clothing. On the whole, the innovation point is clear and the idea is novel. By seeking the principles of science and technology, sensing the infinite possibilities, improving the design efficiency and inspiring innovative design thinking methods, in order to meet the diversity and personality needs of the external shape of the human body.

The concave and convex texture of pleated fabric is geometric and decorative. It has a direct reference value for pleated clothing design by using the concepts of partial and whole in fractal geometry and the quantity and energy conversion in fractal dimension space. Due to the influence of clothing intelligent manufacturing policy, there is a lack of data sorting for the modeling characteristics and process requirements of pleated clothing, and there is innovative design thinking that needs to be integrated with technology and art, which lays a foundation for the future collection of process parameters to upgrade the pleated clothing database.