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The purpose of this paper is to design a model that can accurately forecast the supply chain sales.

This paper proposed a new model based on lightGBM and LSTM to forecast the supply chain sales. In order to verify the accuracy and efficiency of this model, three representative supply chain sales data sets are selected for experiments.

The experimental results show that the combined model can forecast supply chain sales with high accuracy, efficiency and interpretability.

With the rapid development of big data and AI, using big data analysis and algorithm technology to accurately forecast the long-term sales of goods will provide the database for the supply chain and key technical support for enterprises to establish supply chain solutions. This paper provides an effective method for supply chain sales forecasting, which can help enterprises to scientifically and reasonably forecast long-term commodity sales.

The proposed model not only inherits the ability of LSTM model to automatically mine high-level temporal features, but also has the advantages of lightGBM model, such as high efficiency, strong interpretability, which is suitable for industrial production environment.

Social networks provide convenient communication and connection among people, and they have become essential in college students' lives. However, problems also come along with increasing concern about trust and privacy issues. This research attempts to investigate the trust and privacy perceptions of university students when using social networks for learning purposes.

This paper investigated the differences in trust and privacy perceptions between undergraduate (UG) and postgraduate (PG) students through an online survey with 96 subjects in Hong Kong. The authors used the Mann–Whitney U test to compare the differences between the responses provided by UG and PG subjects.

The authors found that both PG and UG students were generally satisfied with the use of social networking sites (SNSs) for learning. However, PG subjects used SNSs more for learning and were more willing to exchange with classmates than UG and PG perceived higher value of SNSs than UG students. The authors also found a relative lack of privacy awareness of UG students.

Based on the study’s findings, the authors made some recommendations about the application of SNSs for learning purposes. The authors also suggest universities provide more guidance and training to students on the privacy issues of SNSs.

Even though some previous studies have focused on studying privacy and trust issues on SNSs, studies that aim at university students in the context of Asia–Pacific are rather limited, especially university students' own trust and privacy perceptions on using SNSs for learning purposes.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2020-0042

This study aims to investigate the spectral illumination characteristics and geometric features of bicycle parts and proposes an image stitching method for their automatic visual inspection.

The unrealistic color casts of feature inspection is removed using white balance for global adjustment. The scale-invariant feature transforms (SIFT) is used to extract and detect the image features of image stitching. The Hough transform is used to detect the parameters of a circle for roundness of bicycle parts.

Results showed that maximum errors of 0°, 10°, 20°, 30°, 40° and 50° for the spectral illumination of white light light-emitting diode arrays with differential shift displacements are 4.4, 4.2, 7.8, 6.8, 8.1 and 3.5 per cent, respectively. The deviation error of image stitching for the stem accessory in x and y coordinates are 2 pixels. The SIFT and RANSAC enable to transform the stem image into local feature coordinates that are invariant to the illumination change.

This study can be applied to many fields of modern industrial manufacturing and provide useful information for automatic inspection and image stitching.