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Article
Publication date: 7 March 2016

Fei Zhong, Chunlei Zhang, Wensheng Li, Jingpin Jiao and Liqiang Zhong

Super304H steel is a new fine-grained austenitic heat-resistant stainless steel developed in recent years, and it is widely used in high temperature section superheater and…

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Abstract

Purpose

Super304H steel is a new fine-grained austenitic heat-resistant stainless steel developed in recent years, and it is widely used in high temperature section superheater and reheater tubes of ultra-supercritical thermal power units’ boiler. Currently intergranular corrosion (IGC) has occurred in a few austenitic stainless steel tubes in ultra-supercritical units and led to boiler leakage. The purpose of this paper is to find a nondestructive method to quickly and easily detect IGC of austenitic stainless steel tube.

Design/methodology/approach

This paper uses the nonlinear characteristics of ultrasonic propagation in steel tube to detect the IGC of Super304H tube.

Findings

The experimental results show that the nonlinear coefficient generally increases sensitively with the degree of IGC; hence, the nonlinear coefficient can be used to assess IGC degree of tubes, and the nonlinear coefficient measurement method is repeatable for the same tube.

Research limitations/implications

A theory of how IGC would affect the ultrasonic signals and lead to a nonlinear response needs further research.

Practical implications

A nondestructive method to quickly and easily detect IGC is provided.

Social implications

Using ultrasonic nonlinear coefficient to assess IGC degree of tubes is a new try.

Originality/value

This paper provides a new way to test IGC.

Details

Anti-Corrosion Methods and Materials, vol. 63 no. 2
Type: Research Article
ISSN: 0003-5599

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Article
Publication date: 10 February 2023

Lingfeng Dong, Jinghui (Jove) Hou, Liqiang Huang, Yuan Liu and Jie Zhang

This paper aims to explore the effects of normative and hedonic motivations on continuous knowledge contribution, and how past contribution experience moderates the effects of the…

878

Abstract

Purpose

This paper aims to explore the effects of normative and hedonic motivations on continuous knowledge contribution, and how past contribution experience moderates the effects of the motivations on continuous knowledge contribution.

Design/methodology/approach

Based on goal-framing theory, the present study proposes a comprehensive theoretical model by integrating normative and hedonic motivations, past contribution experience and continuous knowledge contribution. The data for virtual community members' activities were collected using the Python Scrapy crawler. Logit regression was used to validate the integrative model.

Findings

The results show that both normative motivation (reflected by generalized reciprocity and social learning) and hedonic motivation (reflected by peer recognition and online attractiveness) are positively associated with continuous knowledge contribution. Moreover, these effects are found to be significantly influenced by members' past knowledge contribution experience. Specifically, the results suggest that past knowledge contribution experience undermines the influence of generalized reciprocity on continuous knowledge contribution but strengthens the effect of peer recognition and online attractiveness.

Originality/value

Although the emerging literature on continuous knowledge contribution mainly focuses on motivations as antecedents that promote continuous knowledge contribution, most of these studies assume that the relationship between motivating mechanisms and continuous knowledge contribution does not change over time. The study is one of the initial studies to examine whether and how the influence of multiple motivations evolves relative to levels of past contribution experience.

Details

Information Technology & People, vol. 37 no. 1
Type: Research Article
ISSN: 0959-3845

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Book part
Publication date: 28 March 2022

C. Ganeshkumar, Arokiaraj David and D. Raja Jebasingh

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were…

Abstract

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

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