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The purpose of this paper is to present an automatic inspection and control method for a reagent rapid test strip production system, with image processing techniques.

Fluorescence, color arrangement and combination matching with the database were used to identify the responses of biochemicals. The position accuracy and insufflation consistency between the control line and test line on a reagent rapid test strip will be analyzed from the image after series processing.

The system can identify failed products and regulate production conditions to insure that the quality standard is maintained. The idea edges of the control line and test line are the boundary at which a significant change occurs in the surface reflectance and illumination of the viewer. But the change of the real boundary of the test line may be insufficient for identification.

As the illumination of biological reagent images cannot be measured precisely in the production process, and the intensity of the background light source is difficult to control, there are always significant errors in the production process. If the environment at sampling could be precisely controlled, the accuracy of the system could be enhanced.

This study developed software architecture for a biological reagent production and inspection system. Future studies will focus on the implementation of testing, and improvement of the system, so that it can be applied to medical systems for the benefit of all patients.

This study aims to explore whether, how and when leaders' artificial intelligence (AI) symbolization (i.e. the demonstration of leaders' acceptance of and support for AI by engaging in AI-related behaviors and/or displaying objects that reflect their affinity for AI) affects employee job crafting behaviors.

The authors conducted two studies (i.e. an experiment and a multi-wave field survey) with samples from different contexts (i.e. United States and China) to test our theoretical model. The authors used ordinary least squares (OLS) and hierarchical linear modeling (HLM) to test the hypotheses.

Leaders' AI symbolization is positively related to employee change readiness and, in turn, promotes employee job crafting. Moreover, employee-attributed impression management motives moderate the positive indirect effect of leaders' AI symbolization on employee job crafting via change readiness, such that this indirect effect is stronger when employee-attributed impression management motives are low (vs high).

Leaders should engage in AI symbolization to promote employee job crafting and avoid behaviors that may lead employees to attribute their AI symbolization to impression management.

By introducing the concept of leaders' AI symbolization, this study breaks new ground by illustrating how leaders' AI symbolization positively influences employees' change readiness, as well as job crafting in the workplace. Further, integrating AI as a novel and timely context for evaluating job crafting contributes to the literature where empirical research is relatively scant, particularly regarding the factors that prompt employees to engage in job crafting.